business intelligence

Competing on Analytics: The New Science of Winning
by Thomas H. Davenport and Jeanne G. Harris
Published 6 Mar 2007

Smaller data warehouses were called data marts. Today, as we mentioned, the entire field is often referred to with the term business intelligence and incorporates the collection, management, and reporting of decision-oriented data as well as the analytical techniques and computing approaches that are performed on the data. Business intelligence overall is a broad and popular field within the IT industry—in fact, a 2006 Gartner survey of 1,400 chief information officers suggests that business intelligence is the number one technology priority for IT organizations.8 Two studies of large organizations using ERP systems that we did in 2002 and 2006 revealed that better decision making was the primary benefit sought, and (in 2006) analytics were the technology most sought to take advantage of the ERP data.

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For example, in a 2005 survey of 220 organizations’ approaches to the management of business intelligence (which, remember, also includes some nonanalytical activities, such as reporting), only 45 percent said that their use of business intelligence was either “organizational” or “global,” with 53 percent responding “in my department,” “departmental,” “regional,” or “individual.” In the same survey, only 22 percent of firms reported a formal needs assessment process across the enterprise; 29 percent did no needs assessment at all; and 43 percent assessed business intelligence needs at the divisional or departmental level.3 The reasons for this decentralization are easy to understand.

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To make sure the IT environment fully addresses an organization’s needs at each stage of analytical competition, companies must incorporate analytics and other business intelligence technologies into their overall IT architecture. (Refer to the box “Data and IT Capability by Stage of Analytical Competition.”) As we pointed out in chapter 1, technologists use the term business intelligence (often shortened as BI) to encompass not only analytics—the use of data to analyze, forecast, predict, optimize, and so on—but also the processes and technologies used for collecting, managing, and reporting decision-oriented data. The business intelligence architecture (a subset of the overall IT architecture) is an umbrella term for an enterprise-wide set of systems, applications, and governance processes that enable sophisticated analytics, by allowing data, content, and analyses to flow to those who need it, when they need it.

Using Open Source Platforms for Business Intelligence: Avoid Pitfalls and Maximize Roi
by Lyndsay Wise
Published 16 Sep 2012

Once an organization determines this, they can narrow their search to the solutions that meet these needs. Although the other delivery methods are outside the scope of this book, your company may still want to consider a mix of solutions. Therefore, it makes sense to look at how business intelligence and OS intersect. The components of business intelligence Before we expand to look at the OS market, let’s look a little deeper at how business intelligence pieces fit together by taking a closer look at Figure 1-1 above. The data warehouse represents where all of the information for later use is stored. Figure 1-2 shows this on a high level. The data marts so valued by both Inmon and Kimball are what make up the whole of the data warehouse.

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Using Open Source Platforms for Business Intelligence This page intentionally left blank Using Open Source Platforms for Business Intelligence Avoid Pitfalls and Maximize ROI Lyndsay Wise AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Morgan Kaufmann is an imprint of Elsevier Acquiring Editor: Andrea Dierna Development Editor: Robyn Day Project Manager: Paul Gottehrer Designer: Alisa Andreola Morgan Kaufmann is an imprint of Elsevier 225 Wyman Street, Waltham, MA 02451, USA Copyright r 2012 Elsevier Inc. All rights reserved No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

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I would like to thank the following people and the companies they work for, for either helping to organize interviews and/or take the time to educate me further on specific products, open source outlooks, OSBI use, and general insights: Katie Hutchison, Jessica Swain, Greg Wood, Ketan Karia, Stefano Scamuzzo, Steve Sarsfield, Jill Hara, Susan Davis, Don DeLoach, Yves de Montcheuil, Mike Boyarski, Kim Leadley, Eduardo Paredes, Gerardo Macias, William McKnight, John Kearney, Zibby Keaton, Bruce Belvin, Rebecca Shomair, Kerstin Stephan, Holger Wegstein, and Benjamin Hohmann. In addition, there are several more companies that participated in case studies by sharing their experiences and outlook on open source deployments and their use of business intelligence without their input it would not have been possible to provide insight into the real world applications of open source business intelligence. I would also like to thank Cindi Howson, Howard Dresner, David Loshin, Shawn Rogers, and Wayne Eckerson for sharing their experiences and advice unrestrictedly. And lastly, I would like to thank Gail Wise, for providing advice, support and lending an ear throughout the writing and reviewing process.

Big Data Analytics: Turning Big Data Into Big Money
by Frank J. Ohlhorst
Published 28 Nov 2012

How Retailers Engage Consumers with Social Media and Mobility by Bernie Brennan and Lori Schafer Business Analytics for Customer Intelligence by Gert Laursen Business Analytics for Managers: Taking Business Intelligence beyond Reporting by Gert Laursen and Jesper Thorlund The Business Forecasting Deal: Exposing Bad Practices and Providing Practical Solutions by Michael Gilliland Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy by Olivia Parr Rud CIO Best Practices: Enabling Strategic Value with Information Technology, Second Edition by Joe Stenzel Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media by Frank Leistner Credit Risk Assessment: The New Lending System for Borrowers, Lenders, and Investors by Clark Abrahams and Mingyuan Zhang Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring by Naeem Siddiqi The Data Asset: How Smart Companies Govern Their Data for Business Success by Tony Fisher Demand-Driven Forecasting: A Structured Approach to Forecasting by Charles Chase Executive’s Guide to Solvency II by David Buckham, Jason Wahl, and Stuart Rose The Executive’s Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business by David Thomas and Mike Barlow Fair Lending Compliance: Intelligence and Implications for Credit Risk Management by Clark R.

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Library of Congress Cataloging-in-Publication Data: Ohlhorst, Frank, 1964– Big data analytics : turning big data into big money / Frank Ohlhorst. p. cm. — (Wiley & SAS business series) Includes index. ISBN 978-1-118-14759-7 (cloth) — ISBN 978-1-118-22582-0 (ePDF) — ISBN 978-1-118-26380-8 (Mobi) — ISBN 978-1-118-23904-9 (ePub) 1. Business intelligence. 2. Data mining. I. Title. HD38.7.O36 2013 658.4'72—dc23 2012030191 Preface What are data? This seems like a simple enough question; however, depending on the interpretation, the definition of data can be anything from “something recorded” to “everything under the sun.” Data can be summed up as everything that is experienced, whether it is a machine recording information from sensors, an individual taking pictures, or a cosmic event recorded by a scientist.

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Fortunately, those who have come up with the technologies to mitigate these storage concerns have also come up with a way to derive value from what many see as a burden. It is a process called Big Data analytics. The concepts behind Big Data analytics are actually nothing new. Businesses have been using business intelligence tools for many decades, and scientists have been studying data sets to uncover the secrets of the universe for many years. However, the scale of data collection is changing, and the more data you have available, the more information you can extrapolate from them. The challenge today is to find the value of the data and to explore data sources in more interesting and applicable ways to develop intelligence that can drive decisions, find relationships, solve problems, and increase profits, productivity, and even the quality of life.

Spooked: The Trump Dossier, Black Cube, and the Rise of Private Spies
by Barry Meier
Published 17 May 2021

Meanwhile, lawsuits were starting to pile up against Fusion GPS and Orbis Business Intelligence. In 2017, the three founders of Alfa Bank sued Fusion GPS and Orbis, charging that the information Steele had reported about them was defamatory. Separately, BuzzFeed and Steele’s firm were sued by the Russian owner of several internet service providers who was described in a final memo Steele wrote after the 2016 election as a participant in Moscow’s clandestine cyber attack against Hillary Clinton’s campaign. The lawsuit against BuzzFeed was dismissed. And to defend themselves against the Alfa Bank–connected actions, Fusion GPS and Orbis Business Intelligence, which both denied any wrongdoing, employed the same strategy that Simpson had used in 2013 when Frank VanderSloot, the Mitt Romney donor, came after him.

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Steele was outraged by his depiction in the Horowitz report. Orbis Business Intelligence put out a statement saying the report contained “numerous inaccuracies and misleading statements” and minor changes were made to it. But those fixes were a small victory because the Horowitz report also suggested that Kremlin agents might have locked on to Steele’s sources when they started inquiring about Donald Trump—the scenario Scott Shane, the Times reporter, had painted at the Spy Museum. Russian intelligence operatives were likely monitoring the activities of Orbis Business Intelligence and Steele’s allegations about Michael Cohen’s trip to Prague could have been a plant, the report said.

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In the fall of 2019, Simpson and his partner at Fusion GPS, Peter Fritsch, who was also a former Wall Street Journal reporter, published a book titled Crime in Progress, which they described as telling the “inside story” of the dossier. Christopher Steele, who co-owned an investigative firm in London called Orbis Business Intelligence, was enjoying the limelight, too. A Hollywood production company owned by the actor George Clooney had bought the rights to his story, and in 2019, he attended a celebrity-filled event at a trendy London restaurant. The party was held to honor a new editor of Vanity Fair magazine, and guests included Colin Firth, the well-known actor, and Monica Lewinsky, the former White House intern with whom Bill Clinton had an affair and who now wrote for Vanity Fair.

Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
by Thomas H. Davenport
Published 4 Feb 2014

If there are intermediaries between data scientists and decision makers, the decision makers may not understand all the important data and analytics issues involved in a key decision. There is some evidence that these skills are important. A Gartner study found that “between 70 percent and 80 percent of c­ orporate business intelligence projects fail” owing to “a ­c ombination of poor communications between IT and the business, the failure to ask the right questions or to think about the real needs of the ­business.”3 And granted, business intelligence projects usually involve small rather than big data. However, while the specific percentage of projects that fail is questionable, there is no doubt that lack of communication in small and big data projects causes big problems.

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What Most Large Companies Do Today The classic analytics environment at most big companies includes the operational systems that serve as the sources for data; a data warehouse or collection of federated data marts that house and—­ideally— integrate the data for a range of analysis functions; and a set of business intelligence and analytics tools that enable decisions from the use of ad hoc queries, dashboards, and data mining. Figure 5-4 illustrates the typical big company data warehouse ecosystem. Indeed, big companies have invested tens of millions of dollars in ­hardware platforms, databases, ETL (extract, transform, and load) ­software, BI (business intelligence) dashboards, advanced analytics tools, maintenance contracts, upgrades, middleware, and storage systems that comprise robust, enterprise-class data warehouse environments.

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Jake Porway, “You Can’t Just Hack Your Way to Social Change,” Harvard Business Review blog post, March 7, 2013, http://blogs.hbr.org/cs/2013/03/you_cant_ just_hack_your_way_to.html. 3. Bill Goodwin, “Poor Communication to Blame for Business Intelligence Failure, Says Gartner,” ComputerWeekly.com, January 10, 2011, http://www .computerweekly.com/news/1280094776/Poor-communication-toblame-for-­ business-intelligence-failure-says-Gartner. 4. See Thomas H. Davenport and Jinho Kim, Keeping Up with the Quants (Boston: Harvard Business Review Press, 2013). 5. Sinan Aral with visualization by Nikolaos Hanselmann, “To Go from Big Data to Big Insight, Start with a Visual,” August 27, 2013, Harvard Business Review blog post, http://blogs.hbr.org/2013/08/visualizing-how-online-word-of/. 6.

Digital Accounting: The Effects of the Internet and Erp on Accounting
by Ashutosh Deshmukh
Published 13 Dec 2005

Data mining software and embedded tools are quite user friendly, and if you need to get a deeper understanding of, say, customer behavior and profitability, then you should be capable of specifying the required models. The basic idea is to understand what is possible using these tools. Accountants need to understand the possibilities, or they may fail to exploit the tremendous power of these tools. Exhibit 11. SAP business intelligence Exchange infrastructure SAP R/3 ERP Knowledge warehouse Business information warehouse MySAP business intelligence •Business intelligence platform •Business intelligence tools •Measurement and management Packaged BI solutions •Financial insight •Sales insight •Procurement insight Enterprise portal Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 278 Deshmukh Planning and Budgeting Budgeting has been used as a control tool for many decades.

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These databases and SQL are used by many accounting departments in small- and mid-sized organizations. Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 270 Deshmukh Exhibit 6. Business intelligence tools •Data extraction •Data transformation •Data load Business information warehouse ERP system Reports •Key performance measures •Ad-hoc queries •Business intelligence metadata •OLAP metadata Business intelligence tools OLAP Analysis •Business logic •Mathematical/statistical models •Data mining Executive dashboards Management dashboards Executive information systems Pre-packaged solutions •Planning and budgeting •Consolidations •Financial analytics •Abc/abm •Balanced scorecard •Corporate performance management These tools were soon superceded by specialized report writing tools and analytical tools, which now have evolved to a new category of Business Intelligence (BI) tools; Crystal Reports/Business Objects and Cognos are examples of leading software vendors in this area.

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The effects of the Internet and e-commerce on business Meta Issues •Organizational Models •Business Strategies •Hardware and Software Infrastructure •Integration with ERP Systems Customers Demand Chain Management • Customer Relationship Management • Demand Forecasting • Order Management • Product and Brand Information Management • Channel Management • Customer Services • Business Intelligence Business Finance and Accounting •Financial Reporting •Internal Controls and Audit •Cost Accounting •Treasury Functions Human Resources •Payroll Accounting •Benefits Management •Personnel Management Production •Product Design •Product Development Other Business Processes •Document Storage and Retrieval •Workflows Suppliers Supply Chain Management • Supplier Relationship Management • Production Planning • Materials Management • Transportation and Distribution • Business Intelligence The effects of e-commerce, as can be seen, cut across various industries; industry intermediaries; and, the ultimate, consumers; and also within the industry itself.

The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling
by Ralph Kimball and Margy Ross
Published 30 Jun 2013

Table of Contents Introduction Intended Audience Chapter Preview Website Resources Summary Chapter 1: Data Warehousing, Business Intelligence, and Dimensional Modeling Primer Different Worlds of Data Capture and Data Analysis Goals of Data Warehousing and Business Intelligence Dimensional Modeling Introduction Kimball's DW/BI Architecture Alternative DW/BI Architectures Dimensional Modeling Myths More Reasons to Think Dimensionally Agile Considerations Summary Chapter 2: Kimball Dimensional Modeling Techniques Overview Fundamental Concepts Basic Fact Table Techniques Basic Dimension Table Techniques Integration via Conformed Dimensions Dealing with Slowly Changing Dimension Attributes Dealing with Dimension Hierarchies Advanced Fact Table Techniques Advanced Dimension Techniques Special Purpose Schemas Chapter 3: Retail Sales Four-Step Dimensional Design Process Retail Case Study Dimension Table Details Retail Schema in Action Retail Schema Extensibility Factless Fact Tables Dimension and Fact Table Keys Resisting Normalization Urges Summary Chapter 4: Inventory Value Chain Introduction Inventory Models Fact Table Types Value Chain Integration Enterprise Data Warehouse Bus Architecture Conformed Dimensions Conformed Facts Summary Chapter 5: Procurement Procurement Case Study Procurement Transactions and Bus Matrix Slowly Changing Dimension Basics Hybrid Slowly Changing Dimension Techniques Slowly Changing Dimension Recap Summary Chapter 6: Order Management Order Management Bus Matrix Order Transactions Invoice Transactions Accumulating Snapshot for Order Fulfillment Pipeline Summary Chapter 7: Accounting Accounting Case Study and Bus Matrix General Ledger Data Budgeting Process Dimension Attribute Hierarchies Consolidated Fact Tables Role of OLAP and Packaged Analytic Solutions Summary Chapter 8: Customer Relationship Management CRM Overview Customer Dimension Attributes Bridge Tables for Multivalued Dimensions Complex Customer Behavior Customer Data Integration Approaches Low Latency Reality Check Summary Chapter 9: Human Resources Management Employee Profile Tracking Headcount Periodic Snapshot Bus Matrix for HR Processes Packaged Analytic Solutions and Data Models Recursive Employee Hierarchies Multivalued Skill Keyword Attributes Survey Questionnaire Data Summary Chapter 10: Financial Services Banking Case Study and Bus Matrix Dimension Triage to Avoid Too Few Dimensions Supertype and Subtype Schemas for Heterogeneous Products Hot Swappable Dimensions Summary Chapter 11: Telecommunications Telecommunications Case Study and Bus Matrix General Design Review Considerations Design Review Guidelines Draft Design Exercise Discussion Remodeling Existing Data Structures Geographic Location Dimension Summary Chapter 12: Transportation Airline Case Study and Bus Matrix Extensions to Other Industries Combining Correlated Dimensions More Date and Time Considerations Localization Recap Summary Chapter 13: Education University Case Study and Bus Matrix Accumulating Snapshot Fact Tables Factless Fact Tables More Educational Analytic Opportunities Summary Chapter 14: Healthcare Healthcare Case Study and Bus Matrix Claims Billing and Payments Electronic Medical Records Facility/Equipment Inventory Utilization Dealing with Retroactive Changes Summary Chapter 15: Electronic Commerce Clickstream Source Data Clickstream Dimensional Models Integrating Clickstream into Web Retailer's Bus Matrix Profitability Across Channels Including Web Summary Chapter 16: Insurance Insurance Case Study Policy Transactions Premium Periodic Snapshot More Insurance Case Study Background Claim Transactions Claim Accumulating Snapshot Policy/Claim Consolidated Periodic Snapshot Factless Accident Events Common Dimensional Modeling Mistakes to Avoid Summary Chapter 17: Kimball DW/BI Lifecycle Overview Lifecycle Roadmap Lifecycle Launch Activities Lifecycle Technology Track Lifecycle Data Track Lifecycle BI Applications Track Lifecycle Wrap-up Activities Common Pitfalls to Avoid Summary Chapter 18: Dimensional Modeling Process and Tasks Modeling Process Overview Get Organized Design the Dimensional Model Summary Chapter 19: ETL Subsystems and Techniques Round Up the Requirements The 34 Subsystems of ETL Extracting: Getting Data into the Data Warehouse Cleaning and Conforming Data Delivering: Prepare for Presentation Managing the ETL Environment Summary Chapter 20: ETL System Design and Development Process and Tasks ETL Process Overview Develop the ETL Plan Develop One-Time Historic Load Processing Develop Incremental ETL Processing Real-Time Implications Summary Chapter 21: Big Data Analytics Big Data Overview Recommended Best Practices for Big Data Summary Index Advertisement Introduction The data warehousing and business intelligence (DW/BI) industry certainly has matured since Ralph Kimball published the first edition of The Data Warehouse Toolkit (Wiley) in 1996.

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You'll miss out on updates to fundamental concepts if you skip ahead too quickly. Note This book is laced with tips (like this note), key concept listings, and chapter pointers to make it more useful and easily referenced in the future. Chapter 1: Data Warehousing, Business Intelligence, and Dimensional Modeling Primer The book begins with a primer on data warehousing, business intelligence, and dimensional modeling. We explore the components of the overall DW/BI architecture and establish the core vocabulary used during the remainder of the book. Some of the myths and misconceptions about dimensional modeling are dispelled.

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We'll begin with a primer on DW/BI and dimensional modeling in Chapter 1 to ensure that everyone is on the same page regarding key terminology and architectural concepts. Chapter 1 Data Warehousing, Business Intelligence, and Dimensional Modeling Primer This first chapter lays the groundwork for the following chapters. We begin by considering data warehousing and business intelligence (DW/BI) systems from a high-level perspective. You may be disappointed to learn that we don't start with technology and tools—first and foremost, the DW/BI system must consider the needs of the business. With the business needs firmly in hand, we work backwards through the logical and then physical designs, along with decisions about technology and tools.

Business Metadata: Capturing Enterprise Knowledge
by William H. Inmon , Bonnie K. O'Neil and Lowell Fryman
Published 15 Feb 2008

Some DBMS vendors don’t supply an interface but instead supply utilities and application programming interfaces (APIs) so that you can write your own tools to extract metadata. Cautions that were expressed earlier in the section concerning ERP vendors also apply here: notably, when the vendor changes the system catalog structure, you must modify your tools accordingly. 7.2.6 Business Intelligence Tools Another good source for metadata is the Business Intelligence Tools (BI) environment—the user view of data served up by the data warehouse and data marts. The BI environment includes On-Line Analytical Processing (OLAP) tools. Such products produce cubes or pivot tables that enable drill-down and rollup analysis. Usually, OLAP multidimensional technology is found in the data mart environment.

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Appendix Metadata System of Record Example Table 1 Metadata object system of record Meta Object Entity Name Entity Type Entity Definition Entity Scope Entity Active Ind Entity Logical Business Rule Logical Application Name Entity Synonym Name Logical Attribute Logical Attribute Definition Attribute Logical Business Rule Attribute Logical FK Ind Attribute Business Area Logical Business Function Data Subject Area Physical Column Name Physical Column Data Type Physical Column Length Physical Column Precision Strategic Modeling Tool Tactical Modeling Tool C C C C C U U C U DBMS Tool Data Integration Tool Reporting Tool U C C C C U C U C C U U C C U U C U C U C U C U (Cont.) 283 284 Appendix Table 1 Metadata object system of record (continued) Meta Object Strategic Modeling Tool Physical Column Decimal places Physical Column Default Value Physical Column Nullable Ind Physical Column Comment Physical Column Primary Key Ind Physical Column Foreign Key Ind Table Name Table Owner Table Type Table Comments Physical Table Name Physical Column Name Physical View Name Physical Database Name Physical Schema Name ETL Object Name Source Table Source Column Target Table Target Column ETL Job Name ETL Transformation Rule ETL Job Run Date ETL Job Execution Time ETL Job Row Count ETL Job Status Report Name Report Element Name Report Table Name Report Database Name Report DB Sequence Report Element Business Rule Tactical Modeling Tool DBMS Tool C U C U C U C U C C C C C U U U U U C C C C C Data Integration Tool Reporting Tool C R R R R C C C C C C C R R R R C Appendix 285 Metadata Usage Matrix Example The following table summarizes the metadata objects and the anticipated usage of each object in the following functions: Table 1 Summary of metadata objects usage Source – Metadata Object Entity Name Entity Type Entity Definition Entity Scope Entity Container Entity Active Ind Entity Logical Business Rule Logical Application Name Entity Synonym Name Logical Attribute Logical Attribute Definition Attribute Logical Business Rule Attribute Logical FK Ind Attribute Business Area Logical Business Function Data Subject Area Physical Column Name Physical Column Data Type Physical Column Length Physical Column Precision Physical Column Decimal Places Physical Column Default Value Physical Column Nullable Ind Data Lineage Impact Analysis Definition and or Glossary Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y (Cont.) 286 Appendix Table 1 Summary of metadata objects usage (continued) Source – Metadata Object Physical Column Comment Physical Column Primary Key Ind Physical Column Foreign Key Ind Table Name Table Owner Table Type Table Comments Physical Table Name Physical Column Name Physical View Name Physical Database Name Physical Schema Name ETL Object Name Source Table Source Column Target Table Target Column ETL Job Name ETL Transformation Rule ETL Job Run Date ETL Job Execution Time ETL Job Row Count ETL Job Status Report Name Report Element Name Report Table Name Report Database Name Report DB Sequence Report Element Business Rule Data Lineage Impact Analysis Definition and or Glossary Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Index Abstraction, linking structured and unstructured data, 230–231 Accuracy, metadata information, 33–34 Administration, infrastructure issues functionality requirements, 169 history keeping, 169–170 BI, see Business intelligence Broader term, definition component, 61, 63–64 Business Glossary features, 111–112 integrated technical and business metadata delivery, 152–153 Business intelligence (BI), business metadata delivery infrastructure, 163 Business metadata capture, see Capture, business metadata components, 13 definition, 38 delivery, see Delivery, business metadata funding, see Funding, business metadata historical perspective, 3–11 importance, 274–275 locations corporate forms, 15–16 reports, 14, 29–31 screens, 13–14 origins, 19–20 repository construction, see Metadata project resources, 281–282 technical metadata comparison, 12–13 conversion, 135–136, 182–186 infrastructure for integration, 165–166 separation, 140 tracking over time, 20–21 types, 158–160 Business rules business management, 238–239 business metadata, 237, 245 capturing rationale, 238–239 definition, 235–236 maintenance, 242 management, 243 metadata repository, 244–245 ruleflow, 242–243 sources, 237–238 systems, 239, 242–243 Call center volume, search problem quantification, 70 Capability Maturity Model Integration (CMMI), note-taking as asset producing, 265 Capture, business metadata barriers, 275 corporate knowledge base, 93–94 culture, 95–96 editing automation, 128–129 expansion of definition and descriptions, 129–131 granularization, 129 homonym resolution, 132–134 manual editing, 134–135 staging area, 134 synonym resolution, 131–132 Governance Lite™, 107–109, 111 individual documentation problem, 114–115 knowledge socialization, see Knowledge socialization metadata sources comparison of sources, 127 data warehouse, 126 database management system system catalogs, 124 documents, 123 enterprise resource planning applications, 122 extract-transform-load, 124–125 legacy systems, 125–126 on-line analytical processing tools, 124 on-line transaction processing, 125–126 reports, 122–123 spreadsheets, 123 principles, 95–96 publicity, 112–113 rationale, 90–93 technical metadata conversion to business metadata, 135–136 287 288 Index Capture, business metadata (Continued) technology search, 109–111 Web 2.0 folksonomy, 118–119 mashups, 115–116 overview, 115 Card catalog, see Library card catalog CDC, see Centers for Disease Control Centers for Disease Control (CDC), linking structured and unstructured data, 231 CIF, see Corporate Information Factory C-map, see Concept Map CMMI, see Capability Maturity Model Integration Collective intelligence, knowledge socialization, 97 Communications audits, 251 clarity problems bad business decisions, 57 English language limitations, 59 everyday communications, 56–57 faulty rollups, 57–58 units of measure differences, 59 classification, see Taxonomy definitions components, 61–62 guidelines, 60–61 importance, 59–60 miscellaneous guidelines, 64 usage notes, 62–64 historic library creation, 253 human/computer communication problem, 276–278 screening, 251–253 search problem information and knowledge workers, 65–66 information provider guidelines, 71–72 quantification, 67–70 search techniques, 71 tracking down information, 66–67 Compliance communications audits, 251 historic library creation, 253 screening, 251–253 data profiling, 254–256 financial audit metadata utility, 250 transaction background activities, 250–251 prospects, 280 Sarbanes-Oxley Act provisions, 248–240 types, 249–251 Concept Map (C-map), semantic framework, 205, 209 Conceptual model, semantic framework, 204 Controlled vocabulary (CV), semantic framework, 200–201 Corporate forms, business metadata content, 15–16 Corporate Information Factory (CIF), implementation, 81–82 Corporate knowledge base, components, 93–94 Create Read Update Delete (CRUD), conflict resolution, 167 CRM, see Customer relationship management Cross selling, business metadata capture rationale, 92 CRUD, see Create Read Update Delete Customer relationship management (CRM), business metadata capture rationale, 92 Customer, definition, 56 CV, see Controlled vocabulary DASD, see Direct access storage device Data, definition, 176 Database management system (DBMS) historical perspective, 5 metadata storage, 19 system catalog as metadata resource, 124 Data Flux, data quality presentation, 190–191 Data Governance Council, metadata stewardship, 42–43 Data quality continuum, 190 Data Warehousing report, 49 definition, 177 presentation, 189–190 Data Stewardship Council, metadata stewardship, 43–44 Data warehouse historical perspective, 7–9 infrastructure, 160–161 metadata resource, 126 metadata warehouse features, 161–162 DBMS, see Database management system Decision table, business rule representation, 239–242 Decision tree, business rule representation, 239, 241 Index Definition components, 61–62 dictionary role in information quality, 186 guidelines, 60–61 importance, 59–60 miscellaneous guidelines, 64 usage notes, 62–64 Delivery, business metadata examples corporate dictionary, 147–148 integrated technical and business metadata delivery, 152–153 mashups, 149–150 technical use, 151–152 training, 148–149 visual analytic techniques, 150–151 indirect usage accessibility from multiple places, 142–143 application access, 147 interactive reports, 145–146 overview, 141–142 Web delivery, 143–144 information quality business metadata, 188–190 infrastructure considerations business intelligence environments, 163 graphical affinity, 163–164 legacy environment, 162–163 mashups, 164 principles, 140–141 prospects, 280 Description Logics (DL), semantic framework, 206 Dictionary, see Glossary Direct access storage device (DASD), historical perspective, 4–6 Disk storage, historical perspective, 4–6 DL, see Description Logics Documents, metadata resource, 123 Editing, metadata automation, 128–129 expansion of definition and descriptions, 129–131 granularization, 129 homonym resolution, 132–134 manual editing, 134–135 staging area, 134 synonym resolution, 131–132 Employee turnover, business metadata capture rationale, 91–92 289 Enterprise resource planning (ERP), metadata resource, 85, 122 Entity/relationship (ER) model, semantic framework, 203–204, 208, 210–211 ER model, see Entity/relationship model ERP, see Enterprise resource planning ETL, see Extract-transform-load Extract-transform-load (ETL), metadata resource, 85, 124–125 Federated metadata, integrated metadata management, 168 Financial audit metadata utility, 250 transaction background activities, 250–251 First-order logic (FOL), semantic framework, 206 FOL, see First-order logic Folksonomy knowledge capture, 118–119 self-organizing tags, 77 Forms, see Corporate forms Fourth generation language historical perspective, 6–7 metadata handling, 11 Funding, business metadata advantages and disadvantages of approaches, 52–53 centralized implementation, 51 localized implementation, 51–52 overview, 50–51 Glossary business functions, 60 information quality role, 186 semantic framework, 201 Governance Lite™, knowledge capture, 107–109, 111 Granularization, metadata, 129 Graphical affinity, business metadata delivery infrastructure, 163–164 Grid, metadata representation, 18 Groupware, knowledge socialization, 100–103, 279 Homonyms, resolution, 132–134 Industrial recognition, text, 227 Information quality business and technical metadata interaction, 177–186 business metadata delivery, 188–190 290 Index Information quality (Continued) definition, 177 dictionary role, 186 methodology, 187–188 Information Technology (IT) department challenges, 278–279 metadata responsibility, 38–39 Information, definition, 177 IT department, see Information Technology department KB, see Knowledge base KM, see Knowledge management Knowledge base (KB) definition, 267 building, 267 Knowledge management (KM) business metadata intersection artifact generation, 262–263 corporate dictionary example, 263 definition, 260 goals, 260–261 importance, 261 social issues graying work force, 269–270 socialization effect on knowledge, 270 tacit knowledge, see Tacit knowledge techniques, 267–268 Knowledge socialization collective intelligence, 97 experts, 97–98 groupware, 100–103, 279 knowledge management, 268, 270 technology fostering portal and collaboration servers, 100–103 social networking, 99 wikis, 103–106 Knowledge worker metadata capture, 94 search problem, 65–66 Legacy systems business metadata delivery infrastructure, 162–163 metadata resource, 125–126 Library card catalog, metadata analogy, 27–29 Life cycle, metadata, 45–48 Magnetic tape data storage, 4 languages for data reading, 10 Mashup business metadata delivery, 149–150, 164 knowledge capture, 115–116 Master data management (MDM) conflict resolution, 167 overview, 22 MDM, see Master data management Metadata definition, 9, 26–27 examples, 9 grid representation, 18 management importance, 32 metamodel, 158–160 system of record example, 283–284 usage matrix example, 285–286 Metadata project business metadata versus technical metadata, 83–84 buying versus building, 170–172 classification, 82–83 funding, see Funding, business metadata iterations of development, 84 local metadata tools, 85 metadata sources, 86–87 preexisting repositories, 172–173 rationale, 80–82 scope defining, 85–87 Metadata Stewardship Council, responsibilities, 44–45 Narrower term, definition component, 61, 63–64 National Cancer Institute (NCI), semantic vocabulary implementation, 214–216 NCI, see National Cancer Institute Null, data profiling, 183, 185–186 ODS, see Operational data store OLAP, see Online analytical processing On-line analytical processing, metadata resource, 85124 On-line transaction processing, metadata resource, 125–126 Ontology, semantic framework, 207 Operational data store (ODS), data warehousing, 8 Opportunity cost, search problem quantification, 69 OWL, see Web Ontology Language Ownership, definition, 40 Patterns, identification, 183–184, 186 PC, see Personal computer Index Personal computer (PC) historical perspective, 7 metadata handling, 11 Preferred term, definition component, 62 Punch cards historical perspective, 4 metadata, 9–10 Quality, see Data quality; Information quality Range of values, data profiling, 183, 186 RDF, see Resource Definition Framework Reference file overview, 21–22 updating, 22 Regulations, see Compliance Related term, definition component, 61 Reports interactive reports, 145–146 metadata resource, 14, 29–31, 122–123 Repository, see Metadata project Resource Definition Framework (RDF), semantic framework, 204–205 Reuse, metadata, 32–33 Sales, search problem quantification, 70 Sarbanes-Oxley Act, provisions, 248–240 Screen, business metadata content, 13–14 Search problem enterprise search, 279 information and knowledge workers, 65–66 information provider guidelines, 71–72 quantification, 67–70 search techniques, 71 tracking down information, 66–67 Self-organizing map, linking structured and unstructured data, 233 Self-organizing tags, taxonomy, 77 Semantics business metadata delivering definitions and relationships, 208–209 exposing semantics to business, 210–211 expression, 209–210 overview, 207–208 context sensitivity, 197–199 framework Concept Map, 205, 209 conceptual model, 204 controlled vocabulary, 200–201 Description Logics, 206 291 entity/relationship model, 203–204, 208, 210–211 first-order logic, 206 glossary, 201 ontology, 207 Resource Definition Framework, 204–205 taxonomy, 202 thesauri, 203 topic map, 205 UML, 205–206 Web Ontology Language, 204–205, 210 human/computer concept, 199–200, 278 importance, 196–197 practical issues integration, 211–212 National Cancer Institute semantic vocabulary implementation, 214–216 service-oriented architecture, 214 Web Services, 212–213 prospects for integration and discovery, 280 semantic Web, 195–196 spectrum, 200–201, 208 Semistructured data, examples and technologies, 222–223 Serial transfer, knowledge management, 267–268 Service-oriented architecture (SOA) integrated metadata management, 168 semantics integration, 214 SOA, see Service-oriented architecture SOAP, semantic interface, 212 Socialization, see Knowledge socialization Social networking, knowledge socialization, 99 Spreadsheets, metadata resource, 123 Stemmed words, text distillation, 225 Stewardship business metadata approaches, 44–45 artifacts, 44 Data Governance Council, 42–43 Data Stewardship Council, 43–44 historical perspective, 41–42 Metadata Stewardship Council, 44–45 definition, 40–41 Structured metadata characteristics, 16–18 examples and technologies, 219–220 linking structured and unstructured data abstraction, 230–231 examples, 231, 233 292 Index Structured metadata (Continued) integration, 230 unstructured data comparison, 221 Synonyms, resolution, 131–132 Tacit knowledge definition, 94, 264 note-taking as asset producing, 265–266 transfer nurturing, 266 Taxonomy basic rules, 73, 75 document categorization, 76 governance and taxonomy, 77 language and vocabulary, 75–76 lowest common denominator, 75 overview, 72 self-organizing tags, 77 semantic framework, 202 simplicity, 76 Team Room, knowledge socialization, 100–103 Technical metadata business metadata comparison, 12–13 conversion, 135–136 infrastructure for integration, 165–166 separation, 140 categories, 2 Technical metadata conversion to business metadata, 135–136, 182–186 profiling, 179–182 Text business metadata terms, 227–228 communications audits, 252–253 distillation, 224–229 extraneous words, 225 industrial recognition, 227 pulling, 223 relationship recognition, 228–229 stemmed words, 225 word counting, 226 Thesauri, semantic framework, 203 Topic map, semantic framework, 205 UML, semantic framework, 205–206 Unstructured metadata characteristics, 16–18 examples and technologies, 220–221 mining prospects, 281 structured data comparison, 221 text business metadata terms, 227–228 distillation, 224–229 extraneous words, 225 industrial recognition, 227 pulling, 223 relationship recognition, 228–229 stemmed words, 225 word counting, 226 Value/frequency report, data profiling, 181, 184–186 Web 2.0 knowledge capture folksonomy, 118–119 mashups, 115–116 overview, 115 semantic Web, 195–196 Web Ontology Language (OWL), semantic framework, 204–205, 210 Web Services, semantics interface, 212–213 Wiki governance, 106 knowledge capture, 104 limitations, 105–106 portal collaboration comparison, 105 wikinomics, 104 Wikipedia, 103–104, 212 Words, see Text 

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Business Metadata Praise for Business Metadata “Despite the presence of some excellent books on what is essentially “technical” metadata, up until now there has been a dearth of wellpresented material to help address the growing need for interaction at the conceptual and semantic levels between data professionals and the business clients they support. In Business Metadata, Bill, Bonnie, and Lowell provide the means for bridging the gap between the sometimes “fuzzy” human perception of data that fuels business processes and the rigid information management models used by business applications. Look to the future: next generation business intelligence, enterprise content management and search, the semantic web all will depend on business metadata. Read this book!” —David Loshin, President, Knowledge Integrity Incorporated These authors have written a book that ventures into new territory for data and information management. There are several books about metadata, but this is the first to offer in-depth discussion of the important topic of business metadata.

Data-Ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else
by Steve Lohr
Published 10 Mar 2015

The notion that computers should let you do something smart with data has been around since the dawn of computing. More recently, software programs for using data to make better-informed decisions were given the label “business intelligence.” It is a predecessor term to big data, and one still in use. Business intelligence tends to focus on collection, reporting, and basic analysis but not on the predictive or experimental features of data science. The concept dates back to 1958, presented in a paper titled, “A Business Intelligence System,” by Hans Peter Luhn, a computer scientist at IBM. Such a system, according to Luhn, was needed to cope with the postwar data boom in business, government, and the sciences.

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“Special equipment,” he conceded, would be required. Luhn closed with a prescient bit of qualified optimism. “Perhaps the techniques which ultimately find greatest use will bear little resemblance to those now envisioned,” he wrote, “but some form of automation will ultimately provide an effective answer to business intelligence problems.” IBM’s headquarters is so nestled in woods and a rocky ravine that you don’t see it until well after you have entered the corporate grounds. The lazy Z–shaped building looks deceptively small from the outside. It is a striking contrast to the headquarters it replaced, a suburban corporate big house on a hill, opened in 1963.

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But the family-owned Denihan is an example of a conventional company that has made real progress toward using technology to make more of its decisions aided by data. And the hotel company has been at it seriously for more than a decade. Indeed, Denihan was cited as one of a few examples in a 2009 book, Profiles in Performance: Business Intelligence Journeys and the Roadmap for Change, by Howard Dresner, a respected business and technology consultant. The Denihan experience combines leadership at the top, focused goals, and close cooperation between a small data team and the people managing hotels day in and day out. The investment wasn’t huge, and the progress has come in measured steps.

Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage
by Douglas B. Laney
Published 4 Sep 2017

And even when licensing information, most organizations will add value to it by generating and selling the insights or analysis instead of or alongside the raw information itself. However, evolving from traditional business intelligence (BI), represented by enterprise reporting or end user query tools, has been slow to materialize in many organizations. Not only have lagging organizations lost out on the opportunity to understand their businesses and markets better, but they have squandered opportunities to generate measurable economic benefits from (i.e., monetize) their information assets. In this chapter, we will explore the case for reaching beyond business intelligence, and how embracing these ideas can lead to improved economic benefits for your organization.

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Title: Infonomics : how to monetize, manage, and measure information as an asset for competitive advantage / Douglas B. Laney. Description: New York, NY : Routledge, 2018. | Includes bibliographical references and index. Identifiers: LCCN 2017011754 (print) | LCCN 2017032587 (ebook) | ISBN 9781315108650 (ebook) | ISBN 9781138090385 (hardback : alk. paper) Subjects: LCSH: Business intelligence. | Commercial statistics. | Information technology. Classification: LCC HD38.7 (ebook) | LCC HD38.7 .L347 2018 (print) | DDC 658.4/038—dc23 LC record available at https://lccn.loc.gov/2017011754 Visit the Taylor & Francis Web site at www.taylorandfrancis.com To Susan and Ethan Contents Acknowledgments Foreword Introduction Part I Monetizing Information as an Asset Chapter 1 Why Monetize Information Chapter 2 Prime Ways to Monetize Information Chapter 3 Methods for Monetizing Information Chapter 4 Analytics: The Engine of Information Monetization Part II Managing Information as an Asset Chapter 5 Information Management Maturity and Principles Chapter 6 Information Supply Chains and Ecosystems Chapter 7 Leveraging Information Asset Management Standards and Approaches Chapter 8 Applied Asset Management for Improved Information Maturity Part III Measuring Information as an Asset Chapter 9 Is Information an Asset?

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Federal Reserve had released the results of the second phase of its annual Comprehensive Capital Analysis and Review (CCAR) stress tests on major banks.5 Citigroup had passed with flying colors—the cleanest test of top U.S. banks—by correlating and analyzing 2,600 macroeconomic variables with revenue streams from dozens of business units with the help of machine intelligence technology from Ayasdi.6 They had uncovered variable permutations which were difficult to identify using basic business intelligence approaches, and reduced this process from three months to two weeks. In using information to demonstrably reduce risk and improve compliance, Citigroup had added billions in market value. Or consider how the Carolinas-centered mid-range upscale department store chain Belk is monetizing information to measurably optimize merchandising, marketing, and real estate investments.

Beautiful Data: The Stories Behind Elegant Data Solutions
by Toby Segaran and Jeff Hammerbacher
Published 1 Jul 2009

I didn’t realize it at the time, but with our ETL framework, Data Warehouse, and internal dashboard, we had built a simple “Business Intelligence” system. A Business Intelligence System In a 1958 paper in the IBM Systems Journal, Hans Peter Luhn describes a system for “selective dissemination” of documents to “action points” based on the “interest profiles” of the individual action points. The author demonstrates shocking prescience. The title of the paper is “A Business Intelligence System,” and it appears to be the first use of the term “Business Intelligence” in its modern context. In addition to the dissemination of information in real time, the system was to allow for “information retrieval”—search—to be conducted over the entire document collection.

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He also proposes “reporters” to periodically sift the data and selectively move information to action points as needed. INFORMATION PLATFORMS AND THE RISE OF THE DATA SCIENTIST Download at Boykma.Com 75 The field of Business Intelligence has evolved over the five decades since Luhn’s paper was published, and the term has come to be more closely associated with the management of structured data. Today, a typical business intelligence system consists of an ETL framework pulling data on a regular basis from an array of data sources into a Data Warehouse, on top of which sits a Business Intelligence tool used by business analysts to generate reports for internal consumption. How did we go from Luhn’s vision to the current state of affairs?

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CONTRIBUTORS Download at Boykma.Com 355 Download at Boykma.Com INDEX A accessibility, data collection considerations for, 19, 23 accuracy of data, 21, 29 ACID model of database transactions, 58 action points, 75 action research, 78 Amazon Dynamo system, 69 analysis of data (see data analysis) anchoring, fallacy involving, 217 Apache Hadoop project (see Hadoop system) Argus portal, 81 asymmetry of risk-taking, 208 asynchronous data collection, 4 author identification of corpus data, 239 Autonomy Corporation, 78 Azure SDS, 70 B base rate fallacy, 215 Bay Area housing market analysis (see housing market analysis) “best-effort” approach to database transactions, 58 biases in interpretation of data, 205, 217 Biewald, Lukas (author), 279–301 BigTable system, 68 binary data, 41 BitTorrent, 121 Blackburne, Ben (author), 243–258 blind URLs, 131 books and publications Building the Data Warehouse (Inmon), 76 “A Business Intelligence System” (Luhn), 75 The Code Book (Singh), 230 The Data Warehouse Toolkit (Kimball), 76 CHAPTER 0 The Fifth Discipline (Senge), 78 Secret Code Breaker (Raynard), 233 Bradley, Jean-Claude (author), 259–277 brains, as Information Platforms, 73 Brants, Thorsten (trillion-word data set published by), 219 browser compatibility, testing for, 24 Building the Data Warehouse (Inmon), 76 Business Intelligence system, 75 “A Business Intelligence System” (Luhn), 75 C Caesar ciphers, 228 cameras (imagers) for Phoenix Mars Lander system, 38, 53 Campaingr software, 3 cancer’s effects on DNA, 246 cartography, 86 Cassandra system, 70, 81 causality, not related to correlation, 210 CCD (charge-coupled device) imagers, 37 Census data website, 336 census data, project using (see sense.us website) Center for Embedded Networked Sensing at UCLA, 2 Center for Responsible Politics website, 336 charge-coupled device (CCD) imagers, 37 Cheetah system, 79 chemical data for research (see raw data, providing to users) ChemSpider, 266 Chicago Crime project, 168 CIELab color model, 95 cloud system, 56, 70 (see also PNUTS system) The Code Book (Singh), 230 code examples in this book, using, xiv 357 Download at Boykma.Com collecting data (see data collection) collective reconciliation, 344–348 color schemes in data visualization for customer survey project, 23 for Geograph archive, 93, 95 for PEIR system, 9, 10 for sense.us website, 184, 191 conditional probability, definition of, 220 confirmation bias, 208 consistency of data after updates, 57–64 consumer price index (CPI), 307 contact information for this book, xiv context-less directories, 113 Cooper, Brian F.

Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale
by Jan Kunigk , Ian Buss , Paul Wilkinson and Lars George
Published 8 Jan 2019

RAM, RAM server CPUs and RAM, Server CPUs and RAM-Threads and cores in Hadoop decoupling of, Multiple Clusters and Independent Storage and Compute erasure coding vs. replicationcomparison, summary of, Read performance deciding when to use erasure coding, Guidance locality optimization, Locality optimization network performance, Discussion read performance, Read performance GCP quotas for, Caveats and service limits Hadoop and the Linux storage stack, Hadoop and the Linux Storage Stack-Filesystemsblock-level filesystems, Filesystems erasure coding vs. replication, Erasure Coding Versus Replication-Low-Level Storage Linux page cache, The Linux Page Cache system calls, Important System Calls user space in Linux, User Space low-level storage, Low-Level Storage-Disk cachedisk layer, Disk Layer-Disk cache storage controllers, Storage Controllers-Guidelines server form factors, Server Form Factors-Guidancecomparison of, Form Factor Comparison guidelines for choosing, Guidance virtualization, The Traditional Approach workload profiles, Workload Profiles compute resources, availability in public cloud, Compute availability compute virtualization, Compute Virtualization-Anti-Affinity Groups compute-heavy instances, Instances compute-optimized VMs (Azure), Azure instance types computer room air conditioning units (CRAC units), Cooling confidentiality controls, Access Security configuration management software, Useful Tools(see also software configuration management (SCM) tools) configuration-only edge nodes, Interaction Patterns configurationslong-lived clusters in the cloud, Configuration and Templatingenvironment configuration, Environment configuration operating systems, Operating Systems operating systems for Hadoop, OS Configuration for Hadoop-OS Configuration for Hadoopautomated configuration example, Automated Configuration Example consensus, Apache ZooKeeper, Consensus, Consensusdistributed, Quorums consistencyblocked consistency operations for synchronous replication, Quorum spanning with two datacenters consistent view in EMRFS, Amazon Elastic MapReduce of data in backups, Consistency container groups (see cgroups) containerization, Basics of Virtualization for Hadoop containers, YARN, Installation Choicesblob storage in Azure, Azure storage options OpenShift container-based platform, OpenShift-Summary running in YARN, YARN running user notebook sessions in, Notebooks control plane, A Tour of the Landscape, Network Virtualization Conventional Shared Storage mode, HA configurations cooling (datacenters), Coolingfailure scenarios, Rack Awareness and Rack Failures coordinator daemons (Impala), Impala daemonslimiting number of coordinator nodes, Architecting for HA copy-on-write, Snapshots cores, Threads and cores in Hadoopcore count per CPU in Hadoop, CPU Specifications corporate KDCintegration with cluster KDC, Local cluster KDC and corporate user KDC using for service and user principals, Corporate KDC CPUscommodity servers with more than two, Commodity Servers CPU density, Server Form Factors CPU-heavy cloud instances, CPU-heavy instances server CPUs and RAM, Server CPUs and RAMcores and threads in Hadoop, Threads and cores in Hadoop role of x86 architecture, The role of the x86 architecture specifications for Hadoop, CPU Specifications validating, CPU Credentials Provider API, Temporary security credentials criminal access to cloud infrastructure, Environmental Risks cross-realm trusts, Cross-realm trusts, Option A: Cloud-Only Self-Contained ID Servicesone-way trust between local cluster KDC and corporate KDC, Local cluster KDC and corporate user KDC setting up, Setting up cross-realm trusts-One-way trusts between MIT KDCsone-way trust between MIT KDC and AD, One-way trust between MIT KDC and AD two-way trust between KDCs in different clusters, Local cluster KDC cubes, Analyst custom machine types (GCP), Instance types customer data keys, Encryption in AWS customer master keys, Encryption in AWS customer-managed keys, Encryption in GCP customer-supplied keys, Encryption in GCP D daemons (Impala), Impala daemonsclient protocols supported, Architecting for HA dataavailability in public cloud solutions, Data availability user data persisted in storage systems, Data Types data analysts (see analysts) data context, Multiple Clusters and Independent Storage and Compute Data Definition Language (DDL), Required Databasessynchronous DDL queries in Impala, Architecting for HA data encryption key (DEK), Encryption in Microsoft Azure data lakes, Commoditized Storage Meets the Enterprise, Azure storage options(see also Azure Data Lake Store) data link layer (Layer 2), Layer 2 Recommendations data plane, A Tour of the Landscape, Network Virtualization data plane authorization, Microsoft Azure data replication (see replication) data scientists, Revised Team Setup for Hadoop in the Enterprise, Data scientist, Summaryskill sets, Data scientist data sources, Suitable Data Sources data transfers, Data Transfers-Shufflesreplication, Replication shuffles, Shuffles data types needing backup, Data Types data warehouses, Case Study: A Typical Business Intelligence Projectin business intelligence solution with Hadoop, Solution Overview with Hadoop in traditional business intelligence project approach, The Traditional Approach scaling, The Traditional Approach databasesavailability in public cloud solutions, Databases backing up Hive Metastore, Hive Metastore backups, Databases benchmarks for evaluating relational database engines, Validating Other Components Cloudera Manager, supported options, Cloudera Manager database backup in Oozie backup workflow, Subflow: Database deciding which database to use, Database Considerations for Hadoop services, Service Databases-Database Integration Optionsintegration options, Database Integration Options required databases, Required Databases high availability, Database HA-Supported databasesclustering software for, Clustering replication, Replication supported databases, Supported databases managed relational databases from public cloud providers, Network Architecture Oozie storage of state in, Oozie pluggable and embedded, Many Distributed Systems datacenters, Datacenter Considerations-Summarybasic concepts, Basic Datacenter Concepts-Failure Domain Alignmentcooling, Cooling networks, Network power, Power rack awareness and rack failures, Rack Awareness and Rack Failures typical datacenter setup, Basic Datacenter Concepts cluster spanning, Cluster Spanning-Summarybandwidth impairment with, Bandwidth impairment quorum spanning with three datacenters, Quorum spanning with three datacenters quorum spanning with two datacenters, Quorum spanning with two datacenters rack awareness and, Nonstandard use of rack awareness failure of, Failure Scenarios Hadoop's difference from other workloads, Why Does It Matter ?

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Hadoop services, for example, scale horizontally across many servers but vertically integrate hardware, system-level software, and middleware infrastructure across all these servers into a single system. This requires an understanding of the cluster as a whole, rather than focusing on individual components. Case Study: A Typical Business Intelligence Project The type of organizational change that Hadoop drives is best described by an example. In the following, we compare the team setup of a business intelligence (BI) application on top of state-of-the-art data warehouse infrastructure against the proposed team setup when implementing the same solution on Hadoop. The comparison and technical description will be very high level so that we can focus on the organizational factors.

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skill profile, Big data engineer split responsibilities with other team roles, Split Responsibilities bigdata-interop project (Google), Hadoop integration binary releases of Hadoop, Installation Choices bind (LDAP), LDAP Authentication BitLocker, Encryption in Microsoft Azure bits per second (bps), translating to bytes per second (B/s), Measuring throughput blob storage (Azure), Azure storage options, Azure storage options, Blob storageencryption in, Encryption in Microsoft Azure in Hadoop, Azure storage options integration with Hadoop, Azure Blob storage block blobs (Azure), Azure storage options block encryption key (BEK), Encryption in Microsoft Azure block I/O prioritization, Linux kernel cgroups and, Requirements for Multitenancy block locality, Erasure Coding Versus Replicationlocality opitmization, Locality optimization block reports, HDFS blocks, HDFSblock size, The Linux Page Cache block-level filesystems, Filesystems-Filesystems different meanings of, Sequential I/O performance mlocked by the DataNode, Short-Circuit and Zero-Copy Reads placement of, using replication, Erasure Coding Versus Replication replication of, Replication bring your own key (BYOK), Options for Encryption in the Cloud, BYOK, Recommendations and Summary for Cloud Encryption brokers (Kafka), Kafka bucket policies (Amazon S3), Amazon Simple Storage Service bucketsin Amazon S3, AWS storage options, Caveats and service limits in Google Cloud Storage (GCS), Storage options business continuity team, Policies and Objectives business intelligence project case study, Case Study: A Typical Business Intelligence Project-Do I Need a Center of Excellence/Competence?center for excellence/competence in solution with Hadoop, Do I Need a Center of Excellence/Competence? DevOps and solution with Hadoop, Do I Need DevOps? new team setup for solution with Hadoop, New Team Setup solution overview with Hadoop, Solution Overview with Hadoop split responsibilities in solution with Hadoop, Split Responsibilities traditional solution approach, The Traditional Approach-The Traditional Approach typical team setup, Typical Team Setup-Systems engineer C C++, Apache Impala, Everything Is Java, Web UIsImpala and Kudu implementations, The role of the x86 architecture server certificate verification, Application Integration X.509 certificate format, Converting Certificates cablingcross-cabling racks, Network in stacked networks, Stacked network cabling considerations using high-speed cables in network Layer 1, Layer 1 Recommendations caches, Commodity Serverscache coherence, Commodity Servers disk cache, Disk cacheenabled and disabled, throughput testing, Disk cache HDFS, implementation of, Important System Calls Kerberos tickets stored in, Kerberos Clients L3 cache size and core count, CPU Specifications Linux page cache, The Linux Page Cache Linux, access for filesystem I/O, User Space simulating a cache miss, Sequential I/O performance storage controller cache, Controller cacheguidelines for, Guidelines read-ahead caching, Read-ahead caching throughput testing, Disk cache write-back caching, Write-back caching caching, Hadoop and the Linux Storage Stackenabling name service caching, OS Configuration for Hadoop HDFS, Short-Circuit and Zero-Copy Readscache administration commands, Short-Circuit and Zero-Copy Reads instructing Linux to minimize, The Linux Page Cache of Sentry roles and permissions by services, Impala Canonical Name (CNAME) records (DNS), DNS round robin CAP theorem, Quorum spanning with two datacenters catalog, Impala daemons catalog server, Catalog server categories (cable), Layer 1 Recommendations center of excellence or competence, Do I Need a Center of Excellence/Competence?

Applied Artificial Intelligence: A Handbook for Business Leaders
by Mariya Yao , Adelyn Zhou and Marlene Jia
Published 1 Jun 2018

As with the hiring process, the repetitive nature of these administrative tasks is well suited to automation. Companies like Talla use AI to take over the servicing of administrative questions and requests. Employees get answers to routine questions more quickly, while HR specialists get more time and mental firepower to devote to creating high-value deliverables. 15. Business Intelligence and Analytics Business intelligence (BI) creates meaning from data that your company collected. The goal is to leverage that meaning to guide future business decisions. For example, your company may want to know whether a specific product is selling well, and knowing that the 18- to 25-year-old demographic loves your product will affect how that product will be marketed in the future.

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General and Administrative Finance and Accounting Legal and Compliance Records Maintenance General Operations 14. Human Resources and Talent Matching Candidates to Positions Managing the Interview Process Intelligent Scheduling Career Planning and Retention Risk Analysis Administrative Functions 15. Business Intelligence and Analytics Data Wrangling Data Architecture Analytics 16. Software Development 17. Marketing Digital Ad Optimization Recommendations and Personalization 18. Sales Customer Segmentation Lead Qualification and Scoring Sales Development Sales Analytics 19.

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The last section of our book, “AI For Enterprise Functions,” highlights popular AI applications for common business functions. Chapter 12 summarizes some of the challenges of adopting AI solutions for enterprises. Chapters 13 and 14 introduce common AI applications in essential administrative functions like finance, legal, and HR, while Chapters 15 and 16 describe how machine learning can dramatically improve business intelligence, analytics, and software development. Chapters 17, 18, and 19 focus on the revenue-generating functions of sales, marketing, and customer service. Finally, Chapter 20 emphasizes the ethical responsibility that you, as business and technology leaders, have towards your workforce as well as towards ensuring that any technologies that you build have a benevolent impact on your customers, employees, and society as a whole.

Data Mining: Concepts, Models, Methods, and Algorithms
by Mehmed Kantardzić
Published 2 Jan 2003

The company concentrates on automatic intelligent analyses on a large-scale base, that is, for large amounts of structured data-like database systems and unstructured data-like texts. The open-source data-mining specialist Rapid-I enables other companies to use leading-edge technologies for data mining and business intelligence. The discovery and leverage of unused business intelligence from existing data enables better informed decisions and allows for process optimization. SIPNA Publisher: http://eric.univ-lyon2.fr/∼ricco/sipina.html Sipina-W is publicly available software that includes different traditional data-mining techniques such as CART, Elisee, ID3, C4.5, and some new methods for generating decision trees.

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Oracle Data Mining Vendor: Oracle (www.oracle.com) Oracle Data Mining (ODM)—an option to Oracle Database 11 g Enterprise Edition—enables customers to produce actionable predictive information and build integrated business intelligence applications. Using data-mining functionality embedded in Oracle Database 11 g, customers can find patterns and insights hidden in their data. Application developers can quickly automate the discovery and distribution of new business intelligence—predictions, patterns and discoveries—throughout their organization. Optimus RP Vendor: Golden Helix Inc. (www.goldenhelix.com) Optimus RP, uses Formal Inference-based Recursive Modeling (recursive partitioning based on dynamic programming) to find complex relationships in data and to build highly accurate predictive and segmentation models.

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FastStats™ Vendor: APTECO Limited (www.apteco.com) FastStats Suite, marketing analysis products, including data mining, customer profiling, and campaign management. IBM Intelligent Miner Vendor: IBM (www.ibm.com) DB2 Data Warehouse Edition (DWE) is a suite of products that combines the strength of DB2 Universal Database™ (DB2 UDB) with the powerful business intelligence infrastructure from IBM®. DB2 Data Warehouse Edition provides a comprehensive business intelligence platform with the tools your enterprise and partners need to deploy and build next generation analytic solutions. KnowledgeMiner Vendor: KnowledgeMiner Software (www.knowledgeminer.com) KnowledgeMiner, a self-organizing modeling tool that uses GMDH neural nets and AI to easily extract knowledge from data.

Data Mining: Concepts and Techniques: Concepts and Techniques
by Jiawei Han , Micheline Kamber and Jian Pei
Published 21 Jun 2011

To demonstrate the importance of applications as a major dimension in data mining research and development, we briefly discuss two highly successful and popular application examples of data mining: business intelligence and search engines. 1.6.1. Business Intelligence It is critical for businesses to acquire a better understanding of the commercial context of their organization, such as their customers, the market, supply and resources, and competitors. Business intelligence (BI) technologies provide historical, current, and predictive views of business operations. Examples include reporting, online analytical processing, business performance management, competitive intelligence, benchmarking, and predictive analytics. “How important is business intelligence?” Without data mining, many businesses may not be able to perform effective market analysis, compare customer feedback on similar products, discover the strengths and weaknesses of their competitors, retain highly valuable customers, and make smart business decisions.

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Without data mining, many businesses may not be able to perform effective market analysis, compare customer feedback on similar products, discover the strengths and weaknesses of their competitors, retain highly valuable customers, and make smart business decisions. Clearly, data mining is the core of business intelligence. Online analytical processing tools in business intelligence rely on data warehousing and multidimensional data mining. Classification and prediction techniques are the core of predictive analytics in business intelligence, for which there are many applications in analyzing markets, supplies, and sales. Moreover, clustering plays a central role in customer relationship management, which groups customers based on their similarities.

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The partitioning is not performed by humans, but by the clustering algorithm. Hence, clustering is useful in that it can lead to the discovery of previously unknown groups within the data. Cluster analysis has been widely used in many applications such as business intelligence, image pattern recognition, Web search, biology, and security. In business intelligence, clustering can be used to organize a large number of customers into groups, where customers within a group share strong similar characteristics. This facilitates the development of business strategies for enhanced customer relationship management.

Becoming Data Literate: Building a great business, culture and leadership through data and analytics
by David Reed
Published 31 Aug 2021

Samsung Europe is another example of a business that also in 2018 recognised the gap between its existing operations and the vision it had set for itself to “inspire the world, create the future” via richer digital experiences delivered through innovative technology and products. Internal processes and decision-making, however, were still rooted in legacy technologies. Although data-rich, much of this data existed in silos, meaning it took an average of 18 days for business intelligence (BI) teams to develop a data-driven insight. It also lacked any pan-European view of business performance across its 17 subsidiaries in over 34 countries. To tackle this, the European consumer and market insights team adopted a new harmonised data model that allowed it to aggregate multiple data sets and present a consistent view to the business.

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Table 2.3: Data roles within conventional organisational structures Function Role Task Analytics Customer churn analyst Churn propensity modelling Customer management Retention manager Churn propensity modelling Marketing Customer marketing manager Churn propensity modelling Board Chief customer officer Creating single view of the customer Data management Customer database manager Creating single view of the customer Business intelligence Customer analyst Net customer figure report Finance Chief financial officer Net customer figure report Compliance KYC manager Identity validation Ecommerce Channel manager Identity validation Information security Information security officer Identity validation Customer experience Cx manager Behavioural modelling Data science Data scientist Behavioural modelling Centralisations of roles, for example into a data and analytics centre of excellence, removes role and task duplication while supporting multiple internal customers (see Table 2.4).

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Business analysts were using Excel, which has inherent limits on the volume of data it is able to store and also in managing and sharing versions of spreadsheets. Migrating to a modern BI tool not only made the data more accessible and presented it in a user-friendly way, it also surfaced a critical root cause. As Jason Teoh, then head of business intelligence at Openreach’s fibre and network delivery division, noted at the time: “One of the things analysts started to understand was that it was not just about those overdue jobs, but also about the ones running at between 100 and 140 days. If they could stop those going over the threshold, it would help.

Keeping Up With the Quants: Your Guide to Understanding and Using Analytics
by Thomas H. Davenport and Jinho Kim
Published 10 Jun 2013

But whether or not you’re actually crunching the numbers yourself, it’s useful to know something about the three steps involved in solving the problem. * * * How to Find a Quant If you need a quantitative analyst to help you solve your problem, here are some ways to do it: If you work for a large company, you probably have some—so look in places like Market Research, Business Intelligence, or Operations Research. If you don’t have any, there are plenty of consultants to whom you can turn. Do an Internet search for “business analytics consultants” or look at the helpful list from KDnuggets (http://www .kdnuggets.com/companies/consulting.html). If you want some offshore analytics consultants, the best are to be found in India; check out such firms as Mu Sigma, Fractal Analytics, and Genpact.

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In “Key Software Vendors for Different Analysis Types,” we list some of the key vendors for reporting software. 5. Data analysis * * * Key Software Vendors for Different Analysis Types (listed alphabetically) REPORTING SOFTWARE BOARD International IBM Cognos Information Builders WebFOCUS Oracle Business Intelligence (including Hyperion) Microsoft Excel/SQL Server/SharePoint MicroStrategy Panorama SAP BusinessObjects INTERACTIVE VISUAL ANALYTICS QlikTech QlikView Tableau TIBCO Spotfire QUANTITATIVE OR STATISTICAL MODELING IBM SPSS R (an open-source software package) SAS * * * While all of the listed reporting software vendors also have capabilities for graphical display, some vendors focus specifically on interactive visual analytics, or the use of visual representations of data and reporting.

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SAS has sponsored new programs at Louisiana State and Texas A&M. The University of San Francisco has created a West Coast program as well, and New York University has begun one in New York. One recent survey found that fifty-nine universities were offering either degrees or majors in business analytics or business intelligence—thirty-seven at the masters level and twenty-two undergraduate programs.13 Schools are also beginning to offer courses in data science, and degree programs will follow soon. Quantitative Habits Attitudes are important, but so are habits. It has often been said that it is easier to act your way into a new way of thinking than to think your way into a new way of acting.

Zero History
by William Gibson
Published 6 Sep 2010

We can sit here …” He led them to an L-shaped bench of dull aluminum mesh, in the shadow of a hanging stairway, the sort of place that would have been a smoking-nest, when people smoked in office buildings. “You recall the Amsterdam dealer we bought your jacket from? His mysterious picker?” “Vaguely.” “We’ve gone back to that. Or, rather, a strategic business intelligence unit I’ve hired in the Hague has. An example of Sleight pushing me out of my comfort zone. I’ve never trusted private security firms, private investigators, private intelligence firms, at all. In this case, though, they have no idea who they’re working for.” “And?” Hollis, seated now, Milgrim beside her, was watching Bigend closely.

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They aren’t very fast, but if people see them, their first thought is that they’re hallucinating.” Milgrim nodded. “He’s coming,” he said. “Gracie.” “To London?” “She said he’ll be here soon.” “He has Sleight,” Bigend said, “so he knows that having a look at his pants was simply basic strategic business intelligence. It isn’t as though we’ve done anything to harm him. Or ‘Foley’ either, for that matter.” Milgrim looked from Bigend to Hollis, eyes wide. “A friend of mine has been in a traffic accident,” Hollis said. “I have to stay in town until I know how he is.” Bigend frowned. “Anyone I know?” “No,” said Hollis.

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“No,” said Bigend, “there isn’t.” “What she most particularly wanted to convey to you,” Milgrim said, “Winnie Tung Whitaker, is that Gracie believes you’re his competitor. Which means, to him, that you’re his enemy.” “I’m not his enemy,” said Bigend. “You had me steal the design of his pants.” “ ‘Business intelligence.’ If you hadn’t thrown Foley under some random Russians, this would all be much easier. And it wouldn’t be distracting me from more important things. I am, however, glad that we had this opportunity to discuss the matter in greater detail, privately.” “Bent cops are one thing,” said Milgrim.

The Start-Up of You: Adapt to the Future, Invest in Yourself, and Transform Your Career
by Reid Hoffman and Ben Casnocha
Published 14 Feb 2012

NAVIGATE PROFESSIONAL CHALLENGES WITH NETWORK INTELLIGENCE Entrepreneurs navigate the day-to-day issues of running a company by gathering intelligence: actionable, timely information on all facets of their business, including industry trends, opportunities, competitors’ activities, customer sentiment, promising young talent, and sales trends. In a business, intelligence serves as a GPS device. You need good intelligence to run the start-up of you. The preceding chapters should have prompted questions in your mind like: How desirable are my skills in the changing market? How do I know when I should pivot into a new industry niche? What are the best job opportunities and how do I exploit them? These are not easy questions. They’re certainly not questions you can answer by merely reflecting for a few minutes or filling out a worksheet. You, too, need business intelligence to navigate these challenges. You get it by talking to people in your network.

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They take stock of their assets, aspirations, and the market realities to develop a competitive advantage. They craft flexible, iterative plans. They build a network of relationships throughout their industy that outlives their start-up. They aggressively seek and create breakout opportunities that involve focused risk, and actively manage that risk. They tap their network for the business intelligence to navigate tough challenges. And, they do these things from the moment they hatch that nascent idea to every day after that—even as the companies go from being run out of a garage to occupying floors of office space. To succeed professionally in today’s world, you need to adopt these same entrepreneurial strategies.

Industry 4.0: The Industrial Internet of Things
by Alasdair Gilchrist
Published 27 Jun 2016

It will be possible to manage threats proactively by moving cargo from air to sea or vice versa to mitigate the threats of strike action. Similarly, urgent cargo routes can be altered in real-time if predicative analysis of all the global data shows a high risk of civil unrest or bad weather on route, which could seriously delay delivery. Predictive analysis through Big Data is becoming a required tool for business intelligence analysis; it is believed that over 80% of businesses will adopt it in one form or another in 2016. The promise that predictive analysis holds for global logistics is that they will be able to take proactive action to mitigate potential threats to their operations and keep their freight moving to the destination.

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Now that they have the template, what is stopping them from running off 2 million extras to sell on the black market? 55 56 Chapter 3 |TheTechnical and Business Innovators of the Industrial Internet The big point about Big Data is that it requires vast amounts of intellect to distil business value from it. Creating data lakes will not automatically facilitate business intelligence. If you do not know the correct question to ask of the data, how you can expect a sensible answer? This is where we have to understand how or if machines think and collaborate. M2M Learning and Artificial Intelligence Big Data empowers M2M learning and artificial intelligence, the larger the pool of data the more trustworthy the forecasts—or so it would seem.

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Previously, interest in advanced algorithms had been limited to certain business sectors such as insurance, marketing, and finance where risk and opportunity were the key drivers. However, with the advent of Big Data and the cloud resources to leverage it, has come interest from many other sectors in industry, in search of ways to improve decisionmaking, reduce risk, and optimize business outcomes. This has brought about a seismic shift away from traditional business intelligence, which focuses more on descriptive analysis that uses historical data to determine what has happened. Instead, business leaders are looking to advanced analytics, which complements descriptive analysis by taking the analysis further via diagnostic analysis by asking, not just what happened, but why it happened.

The Invisible Web: Uncovering Information Sources Search Engines Can't See
by Gary Price , Chris Sherman and Danny Sullivan
Published 2 Jan 2003

This book introduces readers to the basics of interface design and explains why a design evaluation should be tied to the use and purchase of information resources. 1999/224 pp/softbound/ISBN 0-910965-31-5 $29.95 1999/224 pp/hardbound/ISBN 0-910965-39-0 $39.95 397 448 Internet Business Intelligence How to Build a Big Company System on a Small Company Budget By David Vine According to author David Vine, business success in the competitive, global marketplace of the 21st century will depend on a firm’s ability to use information effectively—and the most successful firms will be those that harness the Internet to create and maintain a powerful information edge. In Internet Business Intelligence, Vine explains how any company can build a complete, low-cost, Internet-based business intelligence system that really works. If you’re fed up with Internet hype and wondering “Where’s the beef?

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This book helps readers identify overseas buyers, find foreign suppliers, investigate potential partners and competitors, uncover international market research and industry analysis, and much more. 2001/380 pp/softbound/ISBN 0-910965-46-3 $29.95 442 Millennium Intelligence Understanding and Conducting Competitive Intelligence in the Digital Age By Jerry P. Miller and the Business Intelligence Braintrust With contributions from 12 of the world’s leading business intelligence practitioners, here is a tremendously informative and practical look at the CI process, how it is changing, and how it can be managed effectively in the Digital Age. Loaded with case studies, tips, and techniques. 2000/276 pp/softbound/ISBN 0-910965-28-5 $29.95 Internet Prophets The Complete Guide to Enlightened E-Business Strategies By Mary Diffley Since the bursting of the dot.com balloon, companies are approaching e-business with a new wariness—and rightly so, according to author and entrepreneur Mary Diffley.

Demystifying Smart Cities
by Anders Lisdorf

There are different tools, both commercial and open source, but they all work on precompiled files that are being exposed to end users for download or browsing in a tabular format on the platform. It is usually possible to search for descriptions and tags in order to find the data. The data portals are typically used for external users. Business intelligence – The business intelligence (BI) tools have been a mainstay of reporting for decades already. These are being used internally and also run on prepared data. But BI tools usually connect to a Data Warehouse, which is a relational database, where data has been optimized for particular reporting needs. Today the end user often has a great degree of flexibility in how data can be viewed and filtered in the BI tool.

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Similarly, if one consumption style dominates, like APIs, a different solution is preferable. The major benefit of the store once/open consumption architecture is that data consistency is improved, and data lineage transparency is higher. Persist data in lowest granularity – In traditional business intelligence, attention has been given to finding the right grain for data. This is still important, but first data should be stored in the absolute lowest granularity. If a higher grain is needed, it can easily be aggregated by a subsequent process. The reason for this is that whatever is the current need, the future may require lower granularity, and if this is not even stored, it will be impossible to get to without rebuilding the solution.

Londongrad: From Russia With Cash; The Inside Story of the Oligarchs
by Mark Hollingsworth and Stewart Lansley
Published 22 Jul 2009

It appeared as if Russian paranoia had been caught by their British minders. Curtis relied on ISC Global heavily for his own security. The lawyer not only switched pay-as-you-go mobile phones on a regular basis, but the Park Lane office was swept for bugs every day. And for good reason. Despite his infrequent visits to London, he was under surveillance by business intelligence agencies working for commercial competitors to Yukos. In just one day in 2003 there were 7,000 attempts to hack into the computers at Curtis & Co. Bugging of meeting rooms was also a concern. ‘The problem with any listening device is the battery,’ said one top professional. ‘The power is the key to any successful bug.

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One of the litigants was Kenneth Dart, who had invested heavily in Yukos subsidiaries - and lost equally heavily - and who was now leading a class action lawsuit against the company on behalf of minority shareholders. In 2003 Dart hired a private security firm in the US to investigate Curtis - the firm’s first move was to approach UK business intelligence agencies. One such company that it initially approached was ISC itself - unaware that it was owned by Curtis. They offered ISC $1 million to unravel the restructuring of Yukos and the ultimate beneficiaries. The US security firm then turned to private investigators, tasking their operatives and Russian journalists to track Curtis’s movements, as well as those of his staff.

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To some extent, the oligarch encouraged him, but he did not trust the former FSB officer because of his incapability of telling the truth and his indiscretion. ‘If he had any information, he would leak it within three seconds,’ one former Berezovsky aide said. As Litvinenko become alienated from Berezovsky, the more he sought a new role in the murky world of corporate espionage and business intelligence. He began to boast about his expertise in investigating Russian organized crime and started consulting for London-based security and commercial intelligence companies specializing in investigating Russian businessmen. Their clients were international companies, law firms, and banks that needed due diligence to be carried out on Russian individuals and the Kremlin.

RDF Database Systems: Triples Storage and SPARQL Query Processing
by Olivier Cure and Guillaume Blin
Published 10 Dec 2014

See Basically available, soft state, eventually consistent (BASE) Basically available, soft state, eventually consistent (BASE), 28 BerkeleyDB, 29 Berlin SPARQL benchmark (BSBM), 77 BGP abstraction, 151 BI. See Business intelligence (BI) Bigdata, the system, 1, 6, 119, 146, 149 federation for, 178 HAJournalServer, 178 variety, 2 velocity, 2, 215 veracity, 2 volume, 1 BigTable database, 23, 28, 33, 34 Binary JSON (BSON), 31 Binary tuple (BT) index, 136 BitMat system, 6, 121, 156 Bit vectors, 95 Bnodes, 45 BRAHMS, 112 BSBM. See Berlin SPARQL benchmark (BSBM) B-trees, 14 B+tree secondary-memory data structure, 113 Burrows-Wheeler transform (BWT), 91 Business intelligence (BI), 17 BWT. See Burrows-Wheeler transform (BWT) C Cascade style sheets (CSS), 4 Cassandra database, 23, 28, 34, 138 Cassandra Query Language (CQL), 34 Chord, 174 CIA world factbook, 5 Cisco, 3 CLEAR operations, 59 Closed World Assumption (CWA), 193 Clustered index, 14 Clustrix, 38 CMSs.

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A ubiquitous example using OLTP databases is e-commerce applications. The goal of OLAP, together with data mining, is to analyze the data contained in a database such that decisions and predictions can be taken by end-users or computer agents. These systems are implemented in so-called data warehouses and are frequently used in fields such as business intelligence (BI). In general, transactions are rare in OLAP (e.g., write operations are not frequent), but when some are performed they usually involve a very large number of operations. The execution of a million rows on a weekly or monthly basis is not rare. Therefore, the state of a database is more stable because it changes less frequently than in the OLTP context.

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Some of their requisites concern the integration of new features: ­declarative Database Management Systems query languages, solutions for defining schemata, the ability to select different consistency characteristics (e.g., strong or eventual), and integrating integrity constraints to enhance data quality and business intelligence processing.The most successful NoSQL stores are all going this way. For instance, an important work has been conducted by the team at DataStax (the main contributor on the Cassandra database) on designing a declarative, SQL-influenced query language, namely CQL. Note that this language does not just provide a Data Manipulation Language (DML) but also a Data Definition Language (DDL) that enables us to create/drop keyspaces (i.e., databases), tables, and indexes.

European Founders at Work
by Pedro Gairifo Santos
Published 7 Nov 2011

At that time, the company's management team was in Paris but I decided to move our senior executives, along with a new CFO, to California to be closer to our customers and the US financial community. I also restructured the software development process, created a brand new internet-based business intelligence product and, over the next year and a half, we fixed the buggy product line which, in the end, was very attractive to customers. We started to grow again and over two years, our market cap went from $100 million to almost $5 billion. 1997 to 1999 was an amazing time for the company as we expanded into more and more countries.

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Liautaud: No, we managed to go through with the European flotation because our business was fairly untouched, relatively speaking. We continued to grow at thirty to forty percent a year, even through the downturn. Through that downturn however, we changed how we positioned our offerings to suit our customers' changing the requirements. Everyone was in some sort of cost-cutting mode and with the business intelligence that Business Objects offered, customers could optimize their costs and see where the inefficiencies lay. So, we switched our value proposition from “make your business better, add visibility and increase revenue” to “make your business more efficient, see where you waste money and cut your costs.”

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Santos: So, from that period on, to the actual period that Business Objects was sold, can you walk us through a bit of that period? Liautaud: We acquired Crystal in 2003, integrated the business in 2004, and reached a billion dollars in revenue in 2005. We continued to expand and bought a few small businesses. By 2007 we saw about $1.5 billion in revenue. Business intelligence was becoming more and more important to the IT industry and a number of the industry's biggest companies realized that they needed to be in it. Oracle decided to buy Hyperion in 2007. Actually Oracle had approached us before, but with price we didn't like. This acquisition triggered a wave of interest from the likes of IBM and SAP and in the summer of 2007, we had discussions with both.

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
by Eric Siegel
Published 19 Feb 2013

—Dennis R. Mortensen, CEO of Visual Revenue, former Director of Data Insights at Yahoo! “This book is an invaluable contribution to predictive analytics. Eric’s explanation of how to anticipate future events is thought provoking and a great read for everyone.” —Jean Paul Isson, Global VP Business Intelligence and Predictive Analytics, Monster Worldwide; coauthor, Win with Advanced Business Analytics: Creating Business Value from Your Data “Eric Siegel’s book succeeds where others have failed—by demystifying big data and providing real-world examples of how organizations are leveraging the power of predictive analytics to drive measurable change.”

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—Jared Waxman, Web Marketer at LegalZoom, previously at Adobe, Amazon, and Intuit “Siegel covers predictive analytics from start to finish, bringing it to life and leaving you wanting more.” —Brian Seeley, Manager, Risk Analytics, Paychex, Inc. “A wonderful look into the world of predictive analytics from the perspective of a true practitioner.” —Shawn Hushman, VP, Analytic Insights, Kelley Blue Book “An excellent exposition on the next generation of business intelligence—it’s really mankind’s latest quest for artificial intelligence.” —Christopher Hornick, President and CEO, HBSC Strategic Services “A must—Predictive Analytics provides an amazing view of the analytical models that predict and influence our lives on a daily basis. Siegel makes it a breeze to understand, for all readers.”

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Whereas forecasting estimates the total number of ice cream cones to be purchased next month in Nebraska, predictive technology tells you which individual Nebraskans are most likely to be seen with cone in hand. PA leads within the growing trend to make decisions more “data driven,” relying less on one’s “gut” and more on hard, empirical evidence. Enter this fact-based domain and you’ll be attacked by buzzwords, including analytics, big data, business intelligence, and data science. While PA fits underneath each of these umbrellas, these evocative terms refer more to the culture and general skill sets of technologists who do an assortment of creative, innovative things with data, rather than alluding to any specific technology or method. These areas are broad; in some cases, they refer simply to standard Excel reports—that is, to things that are important and require a great deal of craft, but may not rely on science or sophisticated math.

Principles of Web API Design: Delivering Value with APIs and Microservices
by James Higginbotham
Published 20 Dec 2021

Ask the following questions to assess the value that each API brings: ■ Does the API help provide a competitive advantage over other market offerings? ■ Does the API reduce the cost of doing business, perhaps by reducing manual processes? ■ Does the API create a new revenue stream or improve an existing revenue stream? ■ Is the API producing business intelligence, market insights, or decisioning factors? ■ Does the API automate repetitive tasks that free the organization for more critical business functions? If the answer to all the questions is ‘no’, then the value produced is low. Answering yes to one or more questions results in the API offering value to the business or marketplace.

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Automated Security Testing Each week, a new headline appears that indicates a company has been hacked and private information exposed. Security is a process, not a product, and a continual one at that. Security testing aims to answer the following questions: ■ Is the API protected against attacks? ■ Does the API offer opportunities for sensitive data to be leaked? ■ Is someone scraping my API and compromising business intelligence through data? While not typically associated with automated testing, security testing is an active process that includes design time review processes, development time static and dynamic code analysis, and run time monitoring. Design time and development time security testing is often made up of policies and tools that are designed to prevent leaking sensitive data through design reviews that identify potential concerns.

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This is common for many API vendors that consider an undocumented API as secure, such as SnapChat ■ Exposing the exact location, by latitude and longitude, of users because a previously private Tinder API was opened for end-users. A thorough security review prior to opening the API to developers would have identified that the mobile app, not the API, was responsible for hiding the actual physical location of their users These recent breaches span from low-reward results, such as disclosing business intelligence as a competitive advantage, to high-reward results that can disclose extremely sensitive data. One even jeopardized the safety of individuals by disclosing their exact location! Unfortunately, some API providers may take shortcuts in securing their internal APIs. Perhaps they mistakenly think that if they do not document the potential access to the API, no one will go looking for it.

The Silent Intelligence: The Internet of Things
by Daniel Kellmereit and Daniel Obodovski
Published 19 Sep 2013

I think on the software business analytics, it becomes easier and easier to build applications. To me, at the end of the day, M2M is really about data. You’re getting data off devices or sensors that have been hard to get, but once it’s in the cloud, it’s data. So all the data tools, if it’s big data or any kind of business intelligence software, all that stuff is applicable to it. So I think just the same tools and technologies, like Google or Facebook, really do a lot of analytics on their users, and the amount of data that they have, they’re very applicable into the M2M space. When deciding on which networks to run an M2M solution, we would like to point out the ubiquity versus granularity problem.

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Since investment in the Internet of Things has until now been more of a futuristic topic, and the understanding and definition of the market varies notably, forecasts are all over the place. For example, IDC, a market research firm, estimates the value of intelligent systems at $1.7 trillion already, growing to $2.4 trillion by 2017.30 It’s interesting if you look at the high-growth markets that are currently developing around cloud computing, big data, and business intelligence. These markets are in the double-digit billions, and are often not counted toward the M2M market. This shows how blurry the borders are, and that we can expect a number of additional growth segments that we do not see or envision today. The question many investors raise is: How does this growth come about?

Duped: Double Lives, False Identities, and the Con Man I Almost Married
by Abby Ellin
Published 15 Jan 2019

Then a silver-haired man of medium build in a suit and tie stood before us: Phil Houston, the Wizard himself, a twenty-five-year veteran of the CIA, a master polygrapher, and the coauthor of two best-sellers, Spy the Lie and Get the Truth. He’s also QVerity’s CEO.4 In 2001, after leaving “the Agency,” he cofounded Business Intelligence Advisors. BIA employed former and current CIA officers with the Agency’s approval. Apparently the CIA allowed moonlighting—everyone needs an extra buck now and then, even spies.5 Hedge funds, law firms, and Fortune 500 companies hired BIA to train investment analysts how to identify deception with their investment targets, and BIA charged them about $25,000 a day.6 BIA’s agents would sometimes attend investment conferences with their clients where publicly traded companies were presenting.

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See Animal Liberation Front All the Queen’s Horses (documentary film), 177 Allen, Woody, 69 almost-psychopath, 79 Alvarez, Jorge, 75–78, 80, 89 Amanda, 178–184, 217 American Association for Marriage and Family Therapy, 35 American Law Institute, 132 American Sign Language, 83 Ames, Aldrich, 109, 151, 201 amygdala, 84, 125–126, 213 Andersen, Donna, 156 Andreu, Betsy Kramar, 98 Andreu, Frankie, 98 Animal Liberation Front (ALF), 51 animals, 49–56, 82–83 Annals of Gullibility: Why We Get Duped and How to Avoid It (Greenspan), 151 anti-Semitism, 66 antisocial personality disorder, 74, 123–124 Antoine, Mario Ambrose, 134 Ariely, Dan, 92 Aristophanes, 103 Armstrong, Lance, 40, 43, 50, 98, 148 Army, U.S., 24 Ashley Madison, 35 Asperger’s, 60 attention deficit disorder, 60 Ault, Amber, 123–125 Barber, Maggie, 94–95, 112 Batali, Mario, 23 Baumeister, Roy, 145 behavior patterns, 215–216 The Believing Brain (Shermer), 148 Berg, A. Scott, 66 Bergman, Ingrid, 33 betrayal blindness, 156 bond, 131 gender and, 174–175 self-deception and, 130 self-trust from, 216 treachery and, 41–42 Betz-Hamilton, Axton, 172 Beyond Good and Evil (Nietzsche), 209 BIA. See Business Intelligence Advisors bias, 144, 148 the Bible, 39, 86 bin Laden, Osama, 1, 4–5, 118 Blair, Jayson, 150 Blink (Gladwell), 31 Blood Will Out (Kirn), 150 Bond, Charles F., 195 Bond, James, 37–39 Bouteuil, Astrid, 66 Bowers, Kathryn, 82 brain amygdala, 84, 125–126, 213 cognition areas and modularity, 105–108 cognitive dissonance and, 147–148 lies and, 84, 193 love and, 145–146 PTSD and, 123–126, 130, 184 trauma and, 213 Truthful Brain, 202 Brain Injury, 67 broapp.net, 41 Bronson, Po, 81 Brown, Jerry, 133 Brown, Sandra, 213, 214–215 Bruckheimer, Jerry, 19 Buddhism, 107 Bulger, Whitey, 40 Bundy, Ted, 203 Burstyn, Ellen, 64–65 Business Intelligence Advisors (BIA), 189–190 career, fraudulent credentials with, 41 CareerExcuse.com, 41 Carnal Abuse by Deceit: Why Lying to Get Laid Is a Crime (Short, J.), 131–132 Carver, Raymond, 35 Celebrity Sex Pass, 65 CEOs, psychopathy and, 74 chaos, truth and, 98–99 character disturbance, 78 supertraits, 213–215 trust and, 146, 160 Charlie Brown (fictional character), 148–149, 216 Chernow, Ron, 210 children, 101, 148 brains of, 84 double lives influencing, 129 as identity fraud victims, 172 lies and, 80–81 trust and, 146–147 Church, Angelica Schuyler, 210 CIA, 5, 24, 29, 37, 101 as paid liars, 22 spies, 109, 151 Cleckley, Hervey, 74 the Cliché, 137–143 Clinton, Bill, 97 Clinton, Hillary, 21, 87, 105 clusters of actions, 192 coercive control, 187–188 cognition areas, of brain, 105–108 cognitive dissonance, 147–148 coincidence of opposites (coniunctio oppositorum), 86 Collins, Diane, 178–184, 217 collusion, complicity and, 155–158 the Commander, 139, 155, 210–213 double life of, 1–10, 15–30, 92–93, 97–98, 115–121 exposed, 120–121, 218–219 investigative research into, 24–25 reasons for lies, 87–88 supertraits, 214 commission, lies of, 160 communication, nonverbal, 191–192 compartmentalization denial and, 149–150 double lives and, 96–97, 142 lies and, 99–100 relationships and, 110–113 self-integration, 103–104 undercover work and, 102 complicity, collusion and, 155–158 compulsive liars, 24, 27, 74–75 The Confidence Game (Konnikova), 143 coniunctio oppositorum (coincidence of opposites), 86 conscientiousness, 214, 215 ConsentAwareness.net, 130 control coercive, 187–188 question, 199 Converus, 202–203 corporations CEOs and psychopathy, 74 hypocrisy and, 106 with lies, 196 women and, 176–177 cowardice, fear and, 62 crime, 131–132, 135 coercive control as, 188 men and, 176 women and, 176–177 Crundwell, Rita, 171–172, 177 Cyr, Joseph, 72 Daniels, Stormy, 198 Dante Alighieri, 42, 105 “Dark Tetrad,” 73 Darville, Helen, 67–68 Darwin, Anne, 99–100 Darwin, John, 99–100 David, Larry, 139, 142 Dear Evan Hansen (play), 40 deaths, fake, 99–100 debt, in marriage, 31 Demara, Ferdinand Waldo, Jr., 71–73, 98 Demidenko, Helena, 67 Democratic National Convention (2008), 42 denial compartmentalization and, 149–150 of truth, 13, 128 DePaulo, Bella M., 59, 172–173, 195 depression, 124, 157 Derailed (Stapel), 34–35 Desai, Sonia, 123 detection, of lies language and, 193–195 methods for, 197, 202–203 polygraph, 198–202, 206 with unconscious mind, 204–205 Devine, Jack, 151 Diagnostic and Statistical Manual of Mental Disorders (DSM), 74, 124 Diana (Princess), 6 DiCaprio, Leonardo, 35 Dickens, Charles, 86 Dirks, Kurt T., 149 disorders attention deficit, 60 DSM, 74, 124 personality, 73, 74, 79, 123–124 PTSD, 123–126, 130, 184 disorientation, gaslighting and, 25, 119 Dolezal, Rachel, 68 domestic violence, 129, 130 Domingo, Plácido, 16 doppelgänger (double walker), 86 Dostoevsky, Fyodor, 86 double lives, 67–69 of Alvarez, 75–78, 80, 89 of the Commander, 1–10, 15–30, 92–93, 97–98, 115–121 compartmentalization and, 96–97, 142 of Demara, 71–73 escapism and, 63–64 families and, 108, 122–123, 126–130, 175–176 in marriage, 65–66, 126–130, 137–143, 163–171, 184–185, 209–210 double walker (doppelgänger), 86 The Double (Dostoevsky), 86 The Double Life of Charles A.

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See Business Intelligence Advisors bias, 144, 148 the Bible, 39, 86 bin Laden, Osama, 1, 4–5, 118 Blair, Jayson, 150 Blink (Gladwell), 31 Blood Will Out (Kirn), 150 Bond, Charles F., 195 Bond, James, 37–39 Bouteuil, Astrid, 66 Bowers, Kathryn, 82 brain amygdala, 84, 125–126, 213 cognition areas and modularity, 105–108 cognitive dissonance and, 147–148 lies and, 84, 193 love and, 145–146 PTSD and, 123–126, 130, 184 trauma and, 213 Truthful Brain, 202 Brain Injury, 67 broapp.net, 41 Bronson, Po, 81 Brown, Jerry, 133 Brown, Sandra, 213, 214–215 Bruckheimer, Jerry, 19 Buddhism, 107 Bulger, Whitey, 40 Bundy, Ted, 203 Burstyn, Ellen, 64–65 Business Intelligence Advisors (BIA), 189–190 career, fraudulent credentials with, 41 CareerExcuse.com, 41 Carnal Abuse by Deceit: Why Lying to Get Laid Is a Crime (Short, J.), 131–132 Carver, Raymond, 35 Celebrity Sex Pass, 65 CEOs, psychopathy and, 74 chaos, truth and, 98–99 character disturbance, 78 supertraits, 213–215 trust and, 146, 160 Charlie Brown (fictional character), 148–149, 216 Chernow, Ron, 210 children, 101, 148 brains of, 84 double lives influencing, 129 as identity fraud victims, 172 lies and, 80–81 trust and, 146–147 Church, Angelica Schuyler, 210 CIA, 5, 24, 29, 37, 101 as paid liars, 22 spies, 109, 151 Cleckley, Hervey, 74 the Cliché, 137–143 Clinton, Bill, 97 Clinton, Hillary, 21, 87, 105 clusters of actions, 192 coercive control, 187–188 cognition areas, of brain, 105–108 cognitive dissonance, 147–148 coincidence of opposites (coniunctio oppositorum), 86 Collins, Diane, 178–184, 217 collusion, complicity and, 155–158 the Commander, 139, 155, 210–213 double life of, 1–10, 15–30, 92–93, 97–98, 115–121 exposed, 120–121, 218–219 investigative research into, 24–25 reasons for lies, 87–88 supertraits, 214 commission, lies of, 160 communication, nonverbal, 191–192 compartmentalization denial and, 149–150 double lives and, 96–97, 142 lies and, 99–100 relationships and, 110–113 self-integration, 103–104 undercover work and, 102 complicity, collusion and, 155–158 compulsive liars, 24, 27, 74–75 The Confidence Game (Konnikova), 143 coniunctio oppositorum (coincidence of opposites), 86 conscientiousness, 214, 215 ConsentAwareness.net, 130 control coercive, 187–188 question, 199 Converus, 202–203 corporations CEOs and psychopathy, 74 hypocrisy and, 106 with lies, 196 women and, 176–177 cowardice, fear and, 62 crime, 131–132, 135 coercive control as, 188 men and, 176 women and, 176–177 Crundwell, Rita, 171–172, 177 Cyr, Joseph, 72 Daniels, Stormy, 198 Dante Alighieri, 42, 105 “Dark Tetrad,” 73 Darville, Helen, 67–68 Darwin, Anne, 99–100 Darwin, John, 99–100 David, Larry, 139, 142 Dear Evan Hansen (play), 40 deaths, fake, 99–100 debt, in marriage, 31 Demara, Ferdinand Waldo, Jr., 71–73, 98 Demidenko, Helena, 67 Democratic National Convention (2008), 42 denial compartmentalization and, 149–150 of truth, 13, 128 DePaulo, Bella M., 59, 172–173, 195 depression, 124, 157 Derailed (Stapel), 34–35 Desai, Sonia, 123 detection, of lies language and, 193–195 methods for, 197, 202–203 polygraph, 198–202, 206 with unconscious mind, 204–205 Devine, Jack, 151 Diagnostic and Statistical Manual of Mental Disorders (DSM), 74, 124 Diana (Princess), 6 DiCaprio, Leonardo, 35 Dickens, Charles, 86 Dirks, Kurt T., 149 disorders attention deficit, 60 DSM, 74, 124 personality, 73, 74, 79, 123–124 PTSD, 123–126, 130, 184 disorientation, gaslighting and, 25, 119 Dolezal, Rachel, 68 domestic violence, 129, 130 Domingo, Plácido, 16 doppelgänger (double walker), 86 Dostoevsky, Fyodor, 86 double lives, 67–69 of Alvarez, 75–78, 80, 89 of the Commander, 1–10, 15–30, 92–93, 97–98, 115–121 compartmentalization and, 96–97, 142 of Demara, 71–73 escapism and, 63–64 families and, 108, 122–123, 126–130, 175–176 in marriage, 65–66, 126–130, 137–143, 163–171, 184–185, 209–210 double walker (doppelgänger), 86 The Double (Dostoevsky), 86 The Double Life of Charles A.

Hacking Growth: How Today's Fastest-Growing Companies Drive Breakout Success
by Sean Ellis and Morgan Brown
Published 24 Apr 2017

At early stage start-ups, avoiding these silos from the start is advised, but as a start-up grows, more traditional marketing groups can be established alongside a dedicated growth team. And at larger, established firms, teams can complement the existing product, marketing, engineering, and business intelligence groups, collaborating with them and helping to open up more effective communication across them. As Sean’s experience with Dropbox shows, the process can be implemented by even the smallest of teams, which for many start-ups, especially in the early growth phase, should be run by the founder and comprise the entire company.

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For example, at Netflix, by examining the movies and shows that customers were watching, the company found that Kevin Spacey films and political drama series were both hugely popular with their customers. That insight gave the company confidence to green-light the development of House of Cards, which became not only a huge hit, but also a must-have experience for many subscribers.27 Similarly, at RJMetrics, a business intelligence company, the team found that users who edited a chart in the software during their free trial period were twice as likely to convert to paying customers as those who didn’t and that that number went up even higher when a trial user edited two charts. So what did RJMetrics do? They made the editing of a chart a key step in their new user orientation.28 PIVOTING TO THE UNEXPECTED These distinctive behaviors and preferences can be hard to uncover, in part because sometimes they are so unexpected; paradoxically, you often don’t know what you’re looking for until you find it.

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All Amazon shoppers in effect see their own version of Amazon with a unique experience tailored to their preferences. Some recommendation engines, such as Amazon’s, as well as those deployed by Google and Netflix, are incredibly complex, but many are based on relatively simple math. As Colin Zima, the chief analytics officer at Looker, a business intelligence software, explains, it can be relatively easy to generate recommendations based on a simple formula called a Jaccard index, or Jaccard similarity coefficient, which determines how similar two products are to each other. This helps to recommend additional items that a customer might want to buy because the software has calculated that the items, when purchased, are often purchased together.

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines
by Thomas H. Davenport and Julia Kirby
Published 23 May 2016

That’s why, when you look at the matrix in Figure 3.1 you see in the upper-left corner a cell that represents the intersection of “human support” and “analyzing numbers.” When we map the progression of smart machines across the decades, the story starts here. Chances are, if you are a decision-maker in a large organization, you have found yourself working with business intelligence software, data visualization tools, and hypothesis-driven analytics. In the overall pattern by which automation comes to knowledge work, such tools represent square one. Unfolding from that point to the squares farthest from it on the horizontal is the history and future of smart machines, moving from the least to the greatest of their increasingly unnerving capabilities.

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For health insurance giant UnitedHealthcare, she began to step up—not just looking at individual insurance policies, but forecasting the impact of new technologies and macroeconomic conditions on health-care costs. At Humana, another large firm (recently acquired by Aetna), Tourville continued her focus on modeling external trends, but she also led the company’s focus on analytics and data-driven decision-making. She was responsible there for the Business Intelligence and Informatics Competency Center, and had responsibility for $50 million of analytics-oriented projects and systems. At her current employer, Anthem—the second-largest company in the U.S. health insurance industry—she’s still stepping in and stepping up. She’s responsible for the “Commercial Health Care Economics” function, analyzing medical expense trends and understanding their movements, as well as the systems that report on such topics.

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Aaron Kechley at DataXu told us that improving reporting has been a big deal for his company for several years now. He said, “We learned the hard way that you can’t preconceive what people will want in a report—people want to process information in different ways.” As a result, DataXu has been investing heavily in the latest “business intelligence” tools—new metrics, custom dashboards, and tailored reports. In fact, one of DataXu’s primary offerings these days is a tool that actually shows marketers whether their digital ads are working or not. This approach, which goes beyond reporting, has been done for a while with traditional statistical analysis.

The Network Imperative: How to Survive and Grow in the Age of Digital Business Models
by Barry Libert and Megan Beck
Published 6 Jun 2016

Big data, for our purposes, is nothing more than large sets of information that can be analyzed to understand useful patterns, often, but not always, related to human behavior. Husband-and-wife team Roland Dickey (CEO) and Laura Dickey (CIO) run Dickey’s Barbecue Pit, with 514 restaurants across the United States. They wanted to bring big data to barbecue, so they partnered with an external business intelligence firm to provide and develop a custom solution they call Smoke Stack.1 Smoke Stack gathers and analyzes data from a range of sources, including point-of-sale systems, loyalty programs, customer surveys, and inventory systems, to provide a nearly real-time dashboard of sales and performance information.

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It developed a strong omnichannel platform that includes real-time inventory; in-store pickup for online purchases; a mobile app that integrates payment, loyalty programs, and local store inventories; and lightning-fast delivery options. Macy’s has recently partnered with Li & Fung to explore retailing in China. L2, a research firm that delivers business intelligence related to digital technology, rates Macy’s as a “genius” in its Digital IQ Index.14 Start with What You’re Missing As you begin the journey toward a leadership team that represents the interests, passions, and expectations of your networks, we encourage you to think about the networks themselves.

Big Data Glossary
by Pete Warden
Published 20 Sep 2011

It allows you to update the search index with much lower latency, has a more minimal REST/JSON-based interface and configuration options, and scales horizontally in a more seamless way. It doesn’t yet have the community or number of contributors of the more established project, though, and it is missing some of the broader features that Solr offers, so it’s worth evaluating both. Datameer Though it’s aimed at the well-known business intelligence market, Datameer is interesting because it uses Hadoop to power its processing. It offers a simplified programming environment for its operators to specify the kind of analysis they want, and then handles converting that into MapReduce jobs behind the scenes. It also has some user-friendly data importing tools, as well as visualization options.

The Data Journalism Handbook
by Jonathan Gray , Lucy Chambers and Liliana Bounegru
Published 9 May 2012

Many journalists seem to be unaware of the size of the revenue that is already generated through data collection, data analytics, and visualization. This is the business of information refinement. With data tools and technologies, it is increasingly possible to shed light on highly complex issues, be this international finance, debt, demography, education, and so on. The term “business intelligence” describes a variety of IT concepts that aim to provide a clear view on what is happening in commercial corporations. The big and profitable companies of our time, including McDonalds, Zara, and H&M, rely on constant data tracking to turn out a profit. And it works pretty well for them. What is changing right now is that the tools developed for this space are now becoming available for other domains, including the media.

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— Brian Boyer, Chicago Tribune At La Nacion we use: Excel for cleaning, organizing and analyzing data; Google Spreadsheets for publishing and connecting with services such as Google Fusion Tables and the Junar Open Data Platform; Junar for sharing our data and embedding it in our articles and blog posts; Tableau Public for our interactive data visualizations; Qlikview, a very fast business intelligence tool to analyze and filter large datasets; NitroPDF for converting PDFs to text and Excel files; and Google Fusion Tables for map visualizations. — Angélica Peralta Ramos, La Nacion (Argentina) As a grassroots community without any technical bias, we at Transparency Hackers use a lot of different tools and programming languages.

Bank 3.0: Why Banking Is No Longer Somewhere You Go but Something You Do
by Brett King
Published 26 Dec 2012

Maslow, “A Theory of Human Motivation”, Psychological Review 50 (1943): 370–96. 17 Countrywide.com 18 Matt Coffin, “The next generation of mortgage lead generation”, LowerMyBills.com. Additional sources: Forrester Research Inc, Federal Trade Commission 19 “Online mortgage sites offer net gains”, Australasian Business Intelligence, 18 September 2006 20 Mortgagebot’s Benchmarks 2011 Report 21 Google Finance Australia 22 See http://en.wikipedia.org/wiki/Apple_II 23 The likes of Forbes have measured mass market adoption or critical mass by the benchmark of 25 per cent of the population for developed economies such as the United States, the United Kingdom, France, Germany, Australia, etc. or 100 million persons globally (See also http://photos1.blogger.com/blogger/4015/329/1600/technology_adoption_11.jpg).

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As effectiveness has reduced, you increase the offer of the month to perhaps three or five different products that the staff member can choose from on the fly, depending on what he knows about the customer, but it is still hit and miss. You need to have intelligence built into the system in respect of the next best offer or the next best action for the customer. This requires the implementation of customer analytics, business intelligence and offering management solutions to take a range of offers each month, and match the right offer with the right client. It also requires each of the product teams to come up with a range of offers each month that can be triggered by key data points for specific customers. Most product teams are used to running just one acquisition-type campaign on a new product three or four times a year—and it takes a great deal of work with creative agencies and so forth to pull it off.

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You have to think about this and provide relevant offers. So don’t pitch a Gold credit card to someone who already has a Platinum card. Don’t pitch term deposits to a customer who is already a Premier account holder with a managed fund. Don’t pitch retirement plans to a student, etc. For this you need business intelligence and segmentation that create compelling, targeted offers that appeal. There are some other issues with selling behind the login, though—issues that have come as a result of marketers picking up some bad habits along the way. Combating banner blindness In a fairly brilliant piece of early usability testing, Jan Benway and David Lane of Rice University discovered in 1998 that users were starting to filter out “advertising banners”.

Chinese Spies: From Chairman Mao to Xi Jinping
by Roger Faligot
Published 30 Jun 2019

Given these circumstances, it comes as no surprise to learn that Huawei has developed a gigantic business intelligence apparatus to unearth everything about its competitors, its potential markets and the research and development of other companies it is interested in acquiring. According to my information, this apparatus also works to the benefit of the state apparatus, including the PLA—in which Ren still serves as an officer in the reserves—and of course, unavoidably in China, the CCP. According to its own documents, this business intelligence system—Huawei TopEng-BI—depends on the internal and external flow of information and information in liaison with all its subsidiaries and the following networks: a real-time data warehouse, an online analysis process, data-mining, an AI system, and a geographical information system.

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The notion of the “sea lamprey strategy” (ba mu man ji) comes from the fact that this slippery, greenish fish blends in with the seascape, clinging to the rocks, and then, having waited patiently to select its prey, closes in and latches on, siphoning off its blood through its multiple orifices. It is the perfect metaphor for Chinese espionage techniques. Huawei’s business intelligence The telecommunications empire Huawei Technologies was founded in 1987 by a former PLA officer, Ren Zhengfei, in the Shenzhen Special Economic Zone. It is an excellent example of a company that has mastered the “sea lamprey strategy”, and the perfect symbol of China profiting from and buying up the rest of the world.

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It has unprecedented systems in place for analyzing millions of calls, clients, VIP customers, competitors, monitoring systems, automated reports on device use, customer profiles, and data to be exploited. Officially, all of this is used for marketing purposes, including breaking into new markets. But the reality is that Huawei’s business intelligence systems, a programme like no other—except for the American NSA—represent one of the world’s largest organizations dealing in technological intelligence. Britain, thanks to research undertaken at the Government Communications Headquarters (GCHQ), has best understood the threat posed by Huawei due to its technological penetration of Western telephone manufacturers including British Telecom and Orange.

Microchip: An Idea, Its Genesis, and the Revolution It Created
by Jeffrey Zygmont
Published 15 Mar 2003

Yet even after it obtained ownership, the chip-maker haggled internally about what to do with the microprocessor. The rancor raged three months longer, until competitive cross-fertilization returned to settle the argument, traveling back from Dallas to Santa Clara. This time the stimulus wasn't a technical specification like the one Datapoint had spirited to TI. It was more generalized business intelligence, carried in the person of Ed Gelbach, who was hired away from Texas Instruments to serve as Intel's new marketing director in August 1971. The hire was a coup for Intel because TI was then the prime mover in semiconductors. Before Gelbach had left Dallas, TI had silently developed its own version of a programmable, single-chip computer.

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He didn't want the idea to leak out into the wider appliance world. Essex engineers spent a lot of time calling on customers, kibitzing with kindred engineers who made washers, dryers, air conditioners, refrigerators, ranges, and microwaves. The visits kept them current so that Essex could create ever more effective relays and switches. But business intelligence seeped both ways, and Fosnough worried that if the rest of Essex R&D knew what he was up to, some loose-lipped 138 MICROCHIP staffer might say too much as he bent over plans and product drafts at an appliance company. Once an appliance engineer got wind of it, he'd mention it to every other control company he traded with.

The Secret World of Oil
by Ken Silverstein
Published 30 Apr 2014

Gaddafi also counted on support from the Washington-based US-Libya Business Association, which was founded and funded by American oil companies. The group was headed by David Goldwyn, who worked at the Energy Department under Clinton and who, after retiring from government to run a consulting firm that provided “political and business intelligence” to industry, returned to public service as the State Department’s coordinator for international energy affairs during the Obama administration. A few others featured in this book include: • Former British prime minister Tony Blair, who netted about $150,000 for a twenty-minute speech in Azerbaijan, in which he said that President Ilham Aliyev was a leader with a “very positive and exciting vision for the future of the country.”

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According to its federal tax filings, the association sought to “educate the public on the importance of US–Libya trade and investment, and facilitate the commercial and diplomatic dialogue between the two countries.” At least seven of its eight directors were registered lobbyists for oil companies. David Goldwyn—who had served at the Energy Department under Bill Clinton, and who then ran a consulting firm that provided “political and business intelligence” to oil companies—was hired to head the group. The USLBA spent over $1 million between 2006 and 2009, of which more than six hundred thousand dollars was used to pay Goldwyn’s firm. In February 2007, the USLBA brought together government officials from both countries at a gala dinner, held at the Charlie Palmer steakhouse, to honor a senior official of “The Great Socialist People’s Libyan Arab Jamahiriya.”

The Genius Within: Unlocking Your Brain's Potential
by David Adam
Published 6 Feb 2018

Despite the theoretical attempt to pull the skills and abilities apart and spread the results across the population, the data puts them back into sticky clumps and hands them, fairly or not, more to some individuals than others. Still, the popularity of the idea of multiple intelligences – all shall have prizes! – has spawned a series of imitators, most of which, in scientific terms, are little more than fashionable labels. Entrepreneurs write and sell books on business intelligence and managerial intelligence. There is spiritual intelligence and existential intelligence and moral intelligence and sexual intelligence and leadership intelligence. There is people intelligence and cultural intelligence and narrative intelligence and creative intelligence. There is even a dark intelligence, made up of an unholy trinity of personality traits: narcissism, Machiavellianism and psychopathy.

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This attitude is common and it feeds on all those fears of IQ, typically presented as an elitist establishment idea and a private members’ club that turns away people at the door. Rival intelligences, their inventors claim, are more inclusive, more open and – crucially – more malleable and changeable. For what use are ideas like business intelligence and sexual intelligence if they cannot be increased in exchange for the price of a book, DVD or conference ticket? Rivals to IQ also trade on the idea they are more relevant, they measure separate and different abilities, which, although they are called intelligences, are more useful to have than ‘intelligence’.

The Finance Book: Understand the Numbers Even if You're Not a Finance Professional
by Stuart Warner and Si Hussain
Published 20 Apr 2017

ERP has made the production of MAPs much quicker and more efficient. Alongside the growth in ERP, technological developments such as CRM (customer relationship management), cloud computing and social media have increased the amount of data available for businesses to analyse. This has led to the emergence of new management accounting practices, such as: business intelligence: the interpretation of raw data to explain performance; business analytics: insights into performance from a continuous, iterative and methodical exploration of data; and big data: computational analysis of very large data sets to reveal patterns, trends, and associations, such as customer behaviour and interactions.

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abridged accounts, 2nd, 3rd accountants, 2nd, 3rd, 4th accounting computerised systems deadlines for depreciation equity group methods for opex and capex for prepayments and accruals for provisions and contingencies responsibilities standards, 2nd for stock accounts see also company accounts; financial accounts; management accounts abridged, 2nd, 3rd payable, 2nd receivable, 2nd statutory, 2nd value of audited accruals, 2nd accruals accounting accrued income, 2nd acid test ratio actual insolvency see cash flow insolvency administration adverse opinion amortisation, 2nd, 3rd angel investors, 2nd, 3rd annual yield AOL asset turnover (AT) asset-based finance, 2nd asset-based lending asset-based valuation, 2nd assets see also current assets; fixed assets in the balance sheet contingent indefinite (or infinite) life net, 2nd valuation, 2nd associate relationships audit and accruals accounting and the balance sheet committee, 2nd disclaimer and disclosure external financial, 2nd qualified report, 2nd requirements and revenue recognition and valuation value of average cost (AVCO), 2nd B&Q, 2nd BA bad debts, 2nd balance, trial balance sheet balance sheet insolvency banks, and audited accounts Beyond Budgeting Round Table (BBRT) BHS big data bonds borrowing costs, 2nd BP, 2nd budgeting, 2nd, 3rd business analytics business angels see angel investors business finance debt equity business intelligence business tax business valuation buyback agreements capex, 2nd capital see also equity in the balance sheet, 2nd, 3rd cost, 2nd, 3rd share venture working, 2nd, 3rd capital allowances, 2nd capital employed, 2nd capital expenditure see capex capital gain, 2nd, 3rd capital maintenance rules capital redemption reserve capital reserves, 2nd, 3rd Carcraft cash, 2nd cash accounting cash equivalents cash flow discounted, 2nd forecasting from operating activities cash flow insolvency cash flow statement cessation, and equity finance charges choice and accounting standards company accounts see also accounts; financial accounts; management accounts and accounting policies and accruals accounting audit report and the balance sheet and business valuation capital and reserves and the cash flow statement corporate governance debt finance debtors and creditors directors’ remuneration earnings per share and finance systems fixed assets gearing going concern disclosure goodwill and group accounting impairment and information in public domain and insolvency interest cover investment appraisal management accounts pack not published in no budgets in opex and capex prepayments and accruals pricing not in and profit and loss (P&L) profit planning not in profitability performance measures provisions and contingencies revaluation revenue recognition and sources of finance stock tax working capital computerised accounting systems confirmation statement, 2nd, 3rd consolidated accounts see group accounting constraint, budgeting constructive obligations contingencies contingent assets contracts long-term onerous contribution, 2nd contribution percentage of sales (CPS) ratio control and debt finance organisational ownership versus convertible bonds corporate governance, 2nd Corporate Voluntary Arrangement (CVA) corporation tax, 2nd, 3rd costs average, 2nd borrowing, 2nd capital, 2nd, 3rd classification debt finance depreciated replacement fixed, 2nd fixed assets versus net realisable value sales stock variable, 2nd cost-volume-profit analysis (CVP), 2nd covenants, 2nd CPS (contribution percentage of sales) credit control, 2nd, 3rd credit ratings creditors, 2nd crowd lending see peer-to-peer (P2P) lending ‘Crowdcube’ crowdfunding see equity crowdfunding cumulative preference shares current assets, 2nd see also assets CVA (Corporate Voluntary Arrangement) CVP (cost-volume-profit analysis), 2nd DCF (discounted cash flow), 2nd deadlines for business tax financial, 2nd debt bad, 2nd collection factoring, 2nd performance measures sale of and solvency debt finance, 2nd, 3rd, 4th debt-equity mix debtor and creditor days, 2nd, 3rd debtors deferred income, 2nd deferred tax, 2nd, 3rd demand yield see price customization depreciated replacement costs depreciation, 2nd, 3rd directors and corporate governance and going concern disclosure and insolvency personal details remuneration, 2nd responsibilities, 2nd and viability statement disclosure disclaimer, audit disclosure requirements, 2nd discounted cash flow (DCF), 2nd discounting prices and revenue recognition disposal, profit/loss on distributable profits dividend cover dividend policy dividend yield dormant businesses double taxation, 2nd downward revaluation draft financial accounts dynamic pricing see price customisation earned income earnings before interest, tax, depreciation and amortisation (EBITDA), 2nd earnings per share earnings statement see profit and loss (P&L) earnings/profit after tax (EAT/PAT) earnings/profit before interest and tax (EBIT/PBIT) earnings/profit before tax (EBT/PBT) EAT/PAT (earnings/profit after tax) eBay EBITDA (earnings before interest, tax, depreciation and amortisation), 2nd EBIT/PBIT (earnings/profit before interest and tax) EBT/PBT (earnings/profit before tax) effective tax rate employment, 2nd Enron enterprise resource planning (ERP) systems, 2nd, 3rd equity, 2nd, 3rd, 4th see also capital equity accounting equity crowdfunding, 2nd equity finance, 2nd ERP (enterprise resource planning) systems, 2nd, 3rd errors ethical pricing exceptional items executive directors expansion and equity finance expenditure, prepayments and accruals, 2nd expenses, 2nd external audit, 2nd Exxon Mobil fair value FAR (fixed asset register) FASB (Financial Accounting Standards Board) FIFO (first-in first-out) finance department in company accounts finance systems importance personnel, 2nd finance director finance personnel, 2nd finance sources in the balance sheet debt, 2nd equity finance systems Financial Accounting Standards Board (FASB) financial accounting team financial accounts see also accounts; company accounts budgeting and forecasting contrasted with management accounts draft investment appraisal profit planning profitable pricing reconciliation with management accounts financial deadlines, 2nd financial health business valuation debt and solvency insolvency and going concern risk investor ratios profitability performance measures working capital and liquidity management financial reporting standards (FRS) financial risk, 2nd, 3rd financial statements and insolvency key elements business tax capital and reserves debtors and creditors goodwill group accounting impairment prepayments and accruals provisions and contingencies revaluation revenue recognition stock tangible fixed assets and depreciation primary balance sheet cash flow statement profit and loss (P&L) requirement for financing activities, 2nd first-in-first-out (FIFO), 2nd fixed asset register (FAR) fixed assets see also assets amortisation, 2nd in the balance sheet cost of versus current assets depreciation impairment, 2nd revaluation fixed charge, 2nd fixed costs, 2nd fixed term loans floating charge forecasting, 2nd foreign currency translation reserve formal insolvency procedures forward ratios fraud, 2nd FRS (financial reporting standards) full statutory accounts Funding Circle GAAP (Generally Accepted Accounting Principles), 2nd gain see capital gain gearing, 2nd, 3rd, 4th Generally Accepted Accounting Principles (GAAP), 2nd going concern, 2nd, 3rd, 4th goods goodwill, 2nd, 3rd, 4th, 5th GPM (gross profit margin), 2nd Greggs auditor’s report balance sheet business valuation capital and reserves cash flow statement corporate governance debtors and creditors directors’ remuneration dividend policy dividends earnings per share finance systems Financial Accounts 2015 and supporting notes going concern disclosure group accounting impairment lack of debt, 2nd opex and capex profit and loss (P&L) profitability performance measures provisions revaluation revenue recognition, 2nd stock stock days tangible fixed assets tax gross profit, 2nd, 3rd gross profit margin (GPM), 2nd group accounting, 2nd growth Her Majesty’s Revenue and Customs (HMRC) IAS (International Accounting Standards) IASB (International Accounting Standards Board) IFRS (international accounting/financial reporting standards), 2nd impairment, 2nd, 3rd, 4th income, 2nd, 3rd, 4th income statement see profit and loss (P&L) income-based valuation method incorporation incremental budgeting, 2nd incurred expenses indefinite (or infinite) life assets, 2nd, 3rd individual shareholders information in the public domain initial public offering (IPO), 2nd insolvency, 2nd, 3rd, 4th institutional investors insurance intangible assets, 2nd interest, tax relief interest cover, 2nd interest rates Internal Rate of Return (IRR), 2nd, 3rd International Accounting Standards Board (IASB) International Accounting Standards (IAS) international accounting/financial reporting standards (IFRS), 2nd international taxation inventory see stock investing activities, 2nd investment appraisal definition, 2nd minimising versus maximising return on investor ratios investors, 2nd invoice factoring invoice finance IPO (initial public offering), 2nd IRR (Internal Rate of Return), 2nd, 3rd JIT (just in time), 2nd journals, 2nd just in time (JIT), 2nd Kingfisher, 2nd land large businesses audit disclosure requirements financial statements ledgers, debtor and creditor legal obligations of directors, 2nd, 3rd in financial statements of group companies for tax leverage, 2nd, 3rd liabilities in the balance sheet provisions and contingencies valuation limited liability liquidation liquidity liquidity ratio loans, fixed term long-term assets see fixed assets long-term contracts long-term liabilities losses, tax relief Majestic Wine management accounting team management accounts, 2nd, 3rd see also accounts; company accounts management accounts pack (MAP) margin of safety, 2nd margins, 2nd market to book ratio, 2nd mark-ups, 2nd maturity, and equity finance mergers and acquisitions micro businesses mixed costs, 2nd modifications, audit multiple obligations MySpace Naked Wines National Insurance Contributions (NIC), 2nd NBV (net book value), 2nd negative goodwill net assets, 2nd net book value (NBV), 2nd Net Present Value (NPV), 2nd, 3rd net realisable value (NRV) net working capital, 2nd Next NIC (National Insurance Contributions), 2nd nominal ledger, 2nd non-controlling interest (NCI), 2nd non-current assets see fixed assets non-current liabilities non-executive directors (NEDs) non-revenue producing activities, in profit and loss (P&L) NPV (Net Present Value), 2nd, 3rd NRV (net realisable value) obligations, provisions for onerous contracts operating activities, 2nd, 3rd operating expenditure see opex operating expenses operating profit, 2nd operating profit margin (OPM), 2nd, 3rd operating risk, 2nd opex, 2nd OPM (operating profit margin), 2nd, 3rd opportunity cost of money ordinary shareholders/shares, 2nd overdraft facilities overtrading owner’s equity see reserves ownership versus control and debt finance and equity finance P&L (profit and loss) P2P (peer-to-peer) lending, 2nd Panko, R.

CTOs at Work
by Scott Donaldson , Stanley Siegel and Gary Donaldson
Published 13 Jan 2012

It's now part of the enterprise, and if you don't have it, you'd better do it now because it cuts costs and makes IT very flexible. Another one is the ability to handle large data. A lot of companies are going to be facing issues regarding enormous amounts of data, how to present it, and how to create business intelligence solutions out of it. There are not a lot of standards yet how to handle these petabytes of data, how to access it in an optimal, cost effective way. There are some open source applications to manage big data, and some very well-known large companies are starting to support those standards.

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Siegel: So, just to follow up on that. Are you guys in the role of establishing a data dictionary for all that or is that something you hand off to someone else? Mosca: We are involved with the data dictionary, master data management, all of the attributes as well. Yeah, it's ultimately going to be a large business intelligence initiative. Siegel: Okay. So you're hired in this case to get down to the blade-of-grass level. Mosca: Absolutely. G. Donaldson: Dmitry, what do you see as some of the important issues confronting your industry today from a technology perspective? Cherches: Security and hacking are going to become more of a daily normal event.

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Cherches: Security and hacking are going to become more of a daily normal event. Unfortunately, many still believe that it's a lot easier to steal versus innovate. So, the CTO is responsible for making sure data is protected and mobile devices are not creating a threat. Being able to manage data and doing a lot of business intelligence from the data. That area is starting to become fairly standard. The technology is fairly affordable and if you don't have good KPI's (Key Performance Indicators), if you don't have good alerts that you can deliver to business stakeholders, you need to look into it ASAP. There's a big chance that a business executive wants to know when on this particular day some indicator is not right.

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It)
by Salim Ismail and Yuri van Geest
Published 17 Oct 2014

Dashboards Extending the notion that decision-making in companies should be driven by data rather than by intuition, Dashboards offer an intuitive way to present complex information in a simple and cogent way. John Seely Brown and John Hagel have observed that although all of our large organizations are set up to scale efficiencies, in this new economy what we actually need to scale is learning. And while some very good business intelligence (BI) systems exist out there, they are set up largely to measure scaling of efficiency. What is needed now are new dashboards that measure the learning capability of organizations. And if those learning dashboards don’t emerge soon, big companies should consider requiring that their newly minted chief data officers (the hottest new C-Level position) build them.

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Key Opportunity Implications and Actions Externally driven IT Leverage external community (developers) and partnerships (startups, SaaS, companies) for new services/products and open platforms with open APIs (remix datasets, open source standards) and provide own metadata (access, remixing). Business intelligence (BI) Data management systems that use methodologies, processes, architectures and technologies to transform raw data into meaningful and useful business information (more effective strategic, tactical and operational insights and decision-making). A key heuristic: if you operate in a highly uncertain environment, make it simple (not too many variables); if you operate in a predictable environment, make it complex (use more variables to manage BI).

Super Founders: What Data Reveals About Billion-Dollar Startups
by Ali Tamaseb
Published 14 Sep 2021

After my cousin was diagnosed, we started researching cancer. We didn’t really know anything about healthcare, let alone cancer, but we went all in. We started off looking at health insurance ideas. We started looking at second-opinion services for cancer patients; my cousin had been misdiagnosed twice. We considered a business-intelligence tool for cancer centers. We did over a year of ideation while we were employees at Google before we formally started Flatiron Health, in June 2012. I believe that if Flatiron had been our first company, it would have been a total failure. We needed the credibility of the Invite Media acquisition and of carrying the Google business card.

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Within a few years of our selling Invite Media, it was a giant company revenue-wise, processing billions of dollars of ads. The company was a rocket ship, and it turned out we undersold. So from day one at Flatiron Health, we had big expectations and big hopes. But our expectations were dashed pretty quickly. We were a business-intelligence tool for cancer centers, doing analytics for private practices and hospitals, but it turns out hospitals and cancer centers don’t really have big budgets. You’re never going to build a business selling that. So we pivoted into giving away the software and tools for free to those centers, and in return using their data for commercialization and research purposes.

Service Design Patterns: Fundamental Design Solutions for SOAP/WSDL and RESTful Web Services
by Robert Daigneau
Published 14 Sep 2011

This logic can be encapsulated within a Service Connector (168) that translates the client’s message to the canonical form, then sends the transformed message to the bus. Once the bus has received a message, it may Virtual Service A Client Virtual Service B Virtual Service C Service Registry Message Store Enterprise Service Bus Wire Tap Target Service A Target Service B Business Intelligence Applications Target Service C Figure 6.15 ESBs provide a layer of indirection that enables services to be added, upgraded, replaced, or deprecated while minimizing the impact on client applications. A Q UICK R EVIEW OF SOA I NFRASTRUCTURE P ATTERNS 223 use a Message Translator to convert the canonical message to the format defined in the service’s contract.

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This removes the responsibility from the target service, and ensures that policies are consistently enforced throughout the company. Since the bus “sees” all of the messages carried between clients and services in real time, a Wire Tap [EIP] can be established to forward information from the bus to business intelligence applications. The Orchestration Engine SOA Infrastructure Patterns ESBs often route messages to services that connect to Orchestration Engines. These are centralized infrastructures that direct the activities of long-running or complex workflows (see Figure 6.17). The activities, or tasks, found in these workflows are often performed by services, though they need not be web services that use HTTP.

Bad Data Handbook
by Q. Ethan McCallum
Published 14 Nov 2012

Should I have been surprised by this? Absolutely not. What the government had done was something I had seen during the early days at OpenTable. We emphasized creating systems and focusing on the market rather than the technology; we raced ahead without giving the underlying architecture enough attention. In addition, business intelligence was still in its infancy. A good report would often just consist of output in a spreadsheet. At that point, data mining and predictive modeling were completely absent from the strategy. Then there was the perception component. Data that was of slightly questionable quality, or data that could possibly have contained any errors, could not be used.

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We know what we have, and we’ve tried answering specific questions, but we’re stuck. Case study: A Fortune 500 technology company had a process that generated data based on day-to-day operations. Historically, their data warehousing team performed most analytics tasks, such as traditional Business Intelligence (BI). This team’s primary responsibility was to develop relational database-driven tools, though they had also been dabbling in less-conventional analytics projects. They decided to bring in a data scientist to help with the latter endeavor. The belief was that the data scientist would magically find a golden nugget hidden in their data, which they could easily translate into some results.

Digital Bank: Strategies for Launching or Becoming a Digital Bank
by Chris Skinner
Published 27 Aug 2013

This means that the way in which you guard against data failings from external attack is by having the obvious data protections: firewalls, secure sign-on, dual authentication with triangulation of access, real-time business events monitoring and so on. What I mean by this is that banks should be moving towards much improved real-time tracking and business intelligence about their information flows, and this will alert them to any security breach. After all, most banks know that they will be breached. In fact, they know they cannot stop a breach. It will happen. The real question then is how you deal with it and how fast. That’s the key. This is why complex event monitoring of business intelligence flows with real-time alerts is a key focal point. The ability for a bank to keep its finger on the pulse of every transaction across its global operations will be the key to protecting against internal and external threats.

Squeezed: Why Our Families Can't Afford America
by Alissa Quart
Published 25 Jun 2018

She believed that “robotic” software services had lowered the document review wages for humans so that all that’s left is work in these kinds of precarious and underpaid sites. “Doc monkeys” are typically now earning just $17 to $20 an hour, while shouldering upward of $200,000 in student debt. They usually have law degrees. In a sample ad for such a job, a company called Business Intelligence Associates (BIA) offered $20 an hour to both recent law school grads and licensed attorneys for temporary work doing document review, trial prep, and support work. The ad claimed that the perks included a “great work-life balance.” However undesirable, jobs like these may now be the only ones available to recent law school graduates.

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See also Adjunct faculty; Hyper-educated working poor Brake, Deborah L., 28–29 Branded: The Buying and Selling of Teenagers (Quart), 216 Brandeis University, 217 Bravo TV, 211–12 Breaking Bad (TV show), 208, 219 Breastfeeding, 19–20, 23, 27 Brightly Cleaning, 259–60 Britain hospital birth costs, 24 parental leave, 26 social mobility in, 112–13 Brodkin, Karen, 126 Brown, Tamara Mose, 119–20 Brown University, 184 Budig, Michelle, 17 Buery, Richard, 83, 254–56 Bureau of Labor Statistics (BLS), 68–69, 116, 169, 209–10, 235 Business Intelligence Associates (BIA), 233 California. See also San Francisco; Silicon Valley family leave, 278n California Nurses Association, 233–34 Campos, Paul, 105 Canada basic income project, 242, 256 day care, 80 parental leave, 26 Cardiff University, 90 Care.com, 159, 254 Career navigators, 165–68, 186–87 Careers, second acts.

The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do
by Erik J. Larson
Published 5 Apr 2021

The actual term first appeared in print in 1997 in a scientific context—in a NASA paper describing challenges to visualizing data using existing computer graphics technology. It didn’t catch on, however, until it became popular in the next decade as a catchall term for business and computing. The modern concept of big data appears to have surfaced first in business intelligence discussions, notably in a 2001 Gartner Group report on business intelligence challenges. The report highlighted “three Vs”—volume, velocity, and variety—to describe features of large datasets that would become increasingly important as computational resources continued to get more powerful and cheaper. Yet the report did not actually use the term big data.5 Nevertheless, the term started appearing everywhere by the end of the 2000s, and by 2014 Forbes captured the hype and confusion with an article titled “12 Big Data Definitions: What’s Yours?”

PostgreSQL 9 Admin Cookbook: Over 80 Recipes to Help You Run an Efficient PostgreSQL 9. 0 Database
by Simon Riggs and Hannu Krosing
Published 23 Oct 2010

Some are not because they aren't so much technical tasks but more just planning and understanding of your environment. You might also find time to consider the following things: ff ff 240 Data quality: Are the contents of the database accurate and meaningful? Could the data be enhanced? Business intelligence: Is the data being used for everything that can bring value to the organization? 10 Performance & Concurrency In this chapter, we will cover the following: ff Finding slow SQL statements ff Collecting regular statistics from pg_stat* views ff Finding what makes SQL slow ff Reducing the number of rows returned ff Simplifying complex SQL ff Speeding up queries without rewriting them ff Why queries do not use an index ff How do force a query to use an index ff Using optimistic locking ff Reporting performance problems Introduction Performance and concurrency are two problems that are often tightly coupled—when concurrency grows, performance usually degrades, in some cases a lot.

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There are usually two main reasons for wanting to do this, and often those reasons are combined: ff 300 High availability: Reducing the chances of data unavailability by having multiple systems each holding a full copy of the data. Chapter 12 ff Data movement: Allowing data to be used by additional applications or workloads on additional hardware. Examples are Reference Data Management, where a single central server might provide information to many other applications, and also Business Intelligence/Reporting Systems. Of course, both of those topics are complex areas, and there are many architectures and possibilities for doing each of those. What we will talk about here is data movement, where there is no transformation of the data—we simply copy the data from one PostgreSQL database server to another.

Chaos Engineering: System Resiliency in Practice
by Casey Rosenthal and Nora Jones
Published 27 Apr 2020

Coinciding disaster tests with major holidays and significant cultural events (major sporting events, shopping holidays, etc.) should be avoided. Be wary of interfering with your business’ supporting monthly or quarterly processes, unless these are specifically what you are intending to test. Finance, accounting, and business intelligence reports may not be the first things that come to mind when you are considering impact beforehand, but you don’t want to be the person that took out the payroll system the night before payday. The common advice at Google is to “look both ways” before kicking off a DiRT test. Immediately before the test starts do your best to watch out for unplanned coincidental events that might impact your test, being especially wary of ongoing production issues that your test may exacerbate.

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That process has legal implications and must not be affected by efforts or tooling like Chaos Engineering. On the other hand, with the rise of digital banks and neobanks,1 the way customers interact with their money is changing. Financial capabilities powered by blockchain, AI, machine learning, and business intelligence has exposed the need for highly robust and scalable systems that the cloud infrastructure provides. This drives an evolution in software development methodologies and the need to bake in the right engineering practices into their way of working. Just like automated deployments have improved feature velocity, and immutable infrastructure makes sure the deployed servers are never altered, the systems need to be continuously validated for reliability.

Thinking with Data
by Max Shron
Published 15 Aug 2014

People may behave rationally according to microeconomic models, but they might also have grudges that the models didn’t account for. Still, when the analogy holds, mathematical modeling is a very powerful way to reason. Special Arguments Every discipline has certain argument strategies that it shares with others. The special arguments of data science overlap with those of engineering, machine learning, business intelligence, and the rest of the mathematically inclined disciplines. There are patterns of argument that occur frequently in each of these disciplines that also pop up in settings where we are using data professionally. They can be mixed and matched in a variety of ways. Optimization, bounding cases, and cost/benefit analysis are three special arguments that deserve particular focus, but careful attention to any case study from data science or related disciplines will reveal many more.

PostgreSQL Administration Essentials
by Hans-Jurgen Schonig
Published 14 Oct 2014

Strauch is a 20-year veteran of software consulting at companies such as IBM, Sears, Ernst & Young, and Kraft Foods. He has a Bachelor's degree in Business Administration and leverages his technical skills to improve the business' self-awareness. His interests include data gathering, management, and mining; maps and mapping; business intelligence; and application of data analysis for continuous improvement. He is currently focused on development of an end-to-end data management and mining at Enova International, a financial services company located in Chicago. In his spare time, he enjoys the performing arts, particularly music, and traveling with his wife, Marilyn.

The Industries of the Future
by Alec Ross
Published 2 Feb 2016

Whereas land was the raw material of the agricultural age and iron was the raw material of the industrial age, data is the raw material of the information age. The Internet has become an ocean of jumbled, chaotic information, but now there is a way to connect this information and draw actionable business intelligence from it. Big data is transitioning from a tool primarily for targeted advertising to an instrument with profound applications for diverse corporate sectors and for addressing chronic social problems. At the same time, the industries of the future will both be created within the current geopolitical structure and transform it.

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We can now chew through enough data fast enough in a way that people can afford . . . and storage got cheap, so we can store lots of data . . . and then we can actually process it fast enough to make use of it.” Increases in data gathering and growth in computing power complement each other. The more data there is, the more investment there is in powerful computers and abundant storage to chew through the data and draw business intelligence from it. The more powerful computers are, the easier it is to gather large amounts of data and produce larger and more in-depth data sets. Big data is inherently contradictory. It is both intimate and expansive. It examines small facts and aggregates these finite facts into information that can be both comprehensive and personalized.

The Einstein of Money: The Life and Timeless Financial Wisdom of Benjamin Graham
by Joe Carlen
Published 14 Apr 2012

Graham's 1937 publication, The Interpretation of Financial Statements (cowritten with Spencer B. Meredith, then a security-analysis instructor at the New York Stock Exchange), addresses this issue (among others) item by item. According to its preface, the purpose of the book is to enable one to read the financial statements of a business “intelligently”52 so that one becomes “better equipped to gauge its future possibilities.”53 For example, when discussing the potentially misleading reporting of a company's intangible assets (i.e., nonphysical resources such as goodwill, intellectual property, etc.), Graham writes that “little if any weight should be given to the figures at which intangible assets appear on the balance sheet.”54 Instead, Graham counseled that “it is the earning power of these intangibles, rather than their balance sheet valuation, that really counts.”55 Similarly, Graham assails corporate reporting of property values (“the same misleading results which were obtained before the war by overstating property values are now sought by the opposite stratagem of understating these assets”56), the “book value” item on the balance sheet, meant to represent the value of all of the assets available for the security in question (“if the company were actually liquidated the value of the assets would most probably be much less than the book value [of the stock]”57), reported earnings figures (“look out for booby traps in the per-share [earnings] figures”58), and more.

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Of course, understanding financial statements is integral to a successful application of Graham's basic investment principles, so such a book made eminent sense. While portions of Security Analysis pertain to this topic, Graham and Meredith's 1937 publication, The Interpretation of Financial Statements, addresses this topic exclusively. According to its preface, the purpose of the book is to enable one to read the financial statements of a business “intelligently” so that one is “better equipped to gauge its future possibilities.”30 The Interpretation of Financial Statements is packed with the discerning analytical wisdom associated with Graham. For example, when discussing the potentially misleading reporting of a company's intangible assets, Graham writes: “In general, it may be said that little if any weight should be given to the figures at which intangible assets appear on the balance sheet….

NumPy Cookbook
by Ivan Idris
Published 30 Sep 2012

Rosario Acquisition Editor Usha Iyer Lead Technical Editor Ankita Shashi Technical Editors Merin Jose Rohit Rajgor Farhaan Shaikh Nitee Shetty Copy Editor Insiya Morbiwala Project Coordinator Vishal Bodwani Proofreader Clyde Jenkins Indexer Monica Ajmera Mehta Production Coordinators Arvindkumar Gupta Manu Joseph Cover Work Arvindkumar Gupta Manu Joseph About the Author Ivan Idris has an MSc in Experimental Physics. His graduation thesis had a strong emphasis on Applied Computer Science. After graduating, he worked for several companies as a Java Developer, Data Warehouse Developer, and QA Analyst. His main professional interests are business intelligence, big data, and cloud computing. He enjoys writing clean, testable code, and interesting technical articles. He is the author of NumPy 1.5 Beginner's Guide. You can find more information and a blog with a few NumPy examples at ivanidris.net. I would like to dedicate this book to my family and friends.

Blackwater: The Rise of the World's Most Powerful Mercenary Army
by Jeremy Scahill
Published 1 Jan 2007

Pizarro also sold his services to defense and weapons companies—in both the United States and in Europe—seeking to break into Latin American markets. He would tell these companies, “Well, let’s say you pay me $10,000 a month times three months, I will provide you with enough information and enough business intelligence so your sales-people will know exactly which doors to knock, to which officers they’re supposed to address, and how and when and for how much and for how long.” Pizarro said he made enough money selling “business intelligence” that he decided in early 2003 to “step away from the company and enjoy the money, enjoy my free time.” Leaving the day-to-day operations of Red Tactica to his business partners, Pizarro began writing for a German magazine focused on military technology.

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Eventually, Pizarro struck up a relationship with virtually every defense and military attaché from “friendly” Latin American nations and earned a reputation as a go-to guy for Latin American countries seeking to purchase specialized weapons systems from major defense companies. Pizarro hotly denied that he was an arms dealer and scoffed at the label. Instead, he said, he was selling “business intelligence” to Latin American officials he characterized as essentially paying him to do their jobs. “A military attaché by definition is a gift, is a reward, is a promotion, is a vacation in Washington. You’re not supposed to actually work,” Pizarro said. “That is in the Latino world. For us, if you’re a general and you get promoted to a senior general, you get a year of vacation, a paid vacation with your entire family in Washington, D.C.

Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data
by Dipanjan Sarkar
Published 1 Dec 2016

Automated Text Classification Text Classification Blueprint Text Normalization Feature Extraction Bag of Words Model TF-IDF Model Advanced Word Vectorization Models Classification Algorithms Multinomial Naïve Bayes Support Vector Machines Evaluating Classification Models Building a Multi-Class Classification System Applications and Uses Summary Chapter 5:​ Text Summarization Text Summarization and Information Extraction Important Concepts Documents Text Normalization Feature Extraction Feature Matrix Singular Value Decomposition Text Normalization Feature Extraction Keyphrase Extraction Collocations Weighted Tag–Based Phrase Extraction Topic Modeling Latent Semantic Indexing Latent Dirichlet Allocation Non-negative Matrix Factorization Extracting Topics from Product Reviews Automated Document Summarization Latent Semantic Analysis TextRank Summarizing a Product Description Summary Chapter 6:​ Text Similarity and Clustering Important Concepts Information Retrieval (IR) Feature Engineering Similarity Measures Unsupervised Machine Learning Algorithms Text Normalization Feature Extraction Text Similarity Analyzing Term Similarity Hamming Distance Manhattan Distance Euclidean Distance Levenshtein Edit Distance Cosine Distance and Similarity Analyzing Document Similarity Cosine Similarity Hellinger-Bhattacharya Distance Okapi BM25 Ranking Document Clustering Clustering Greatest Movies of All Time K-means Clustering Affinity Propagation Ward’s Agglomerative Hierarchical Clustering Summary Chapter 7:​ Semantic and Sentiment Analysis Semantic Analysis Exploring WordNet Understanding Synsets Analyzing Lexical Semantic Relations Word Sense Disambiguation Named Entity Recognition Analyzing Semantic Representations Propositional Logic First Order Logic Sentiment Analysis Sentiment Analysis of IMDb Movie Reviews Setting Up Dependencies Preparing Datasets Supervised Machine Learning Technique Unsupervised Lexicon-based Techniques Comparing Model Performances Summary Index Contents at a Glance About the Author About the Technical Reviewer Acknowledgments Introduction Chapter 1:​ Natural Language Basics Chapter 2:​ Python Refresher Chapter 3:​ Processing and Understanding Text Chapter 4:​ Text Classification Chapter 5:​ Text Summarization Chapter 6:​ Text Similarity and Clustering Chapter 7:​ Semantic and Sentiment Analysis Index About the Author and About the Technical Reviewer About the Author Dipanjan Sarkar is a data scientist at Intel, the world’s largest silicon company, which is on a mission to make the world more connected and productive. He primarily works on analytics, business intelligence, application development, and building large-scale intelligent systems. He received his master’s degree in information technology from the International Institute of Information Technology, Bangalore, with a focus on data science and software engineering. He is also an avid supporter of self-learning, especially through massive open online courses, and holds a data science specialization from Johns Hopkins University on Coursera.

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This involves using NLP, information retrieval, and machine learning techniques to parse unstructured text data into more structured forms and deriving patterns and insights from this data that would be helpful for the end user. Text analytics comprises a collection of machine learning, linguistic, and statistical techniques that are used to model and extract information from text primarily for analysis needs, including business intelligence, exploratory, descriptive, and predictive analysis. Here are some of the main techniques and operations in text analytics:. Text classification Text clustering Text summarization Sentiment analysis Entity extraction and recognition Similarity analysis and relation modeling Doing text analytics is sometimes a more involved process than normal statistical analysis or machine learning.

Tomorrow's Lawyers: An Introduction to Your Future
by Richard Susskind
Published 10 Jan 2013

Online Dispute Resolution (ODR) When the process of actually resolving a legal dispute, especially the formulation of the solution, is entirely or largely conducted across the Internet, then we have some form of online dispute resolution (known in the trade as ODR—see Chapter 10 for more detail and some examples). For litigators whose work is premised on the conventional, court-based trial process, ODR, such as e-negotiation and e-mediation, is a challenge to the heart of their business. Intelligent Legal Search Emerging systems, if properly primed, are now able, in terms of precision and recall, to outperform paralegals and junior lawyers when reviewing and categorizing large bodies of documents. This is disruptive, not simply for law firms which have profited from employing human beings to wade through roomfuls of paperwork (whether on transactions or dispute-related projects), but also for legal process outsourcers who currently offer similar services.

Presentation Zen Design: Simple Design Principles and Techniques to Enhance Your Presentations
by Garr Reynolds
Published 14 Aug 2010

The numbers in this simple bar chart are easy to read quickly, but the numbers become harder to see when a picture graph is used to show the same information. (Images in slides from iStockphoto.com.) You can always recognize truth by its beauty and simplicity. —Richard Feynman, physicist Stephen Few’s Graph Design IQ Test Stephen Few is one of the leading authorities in the field of data visualization and business intelligence. Through his company, Perceptual Edge, he focuses on the effective analysis and presentation of quantitative business information. Stephen is a remarkable presenter and a highly sought after speaker, trainer, and consultant. He is the author of several books on data information visualization, including his latest best-seller Now You See It: Simple Visualization Techniques for Quantitative Analysis (Analytics Press, 2009).

The Dhandho Investor: The Low-Risk Value Method to High Returns
by Mohnish Pabrai
Published 17 May 2009

Fulbright, William Funeral service companies G Gates, Bill GEICO George, Abraham Geus, Arie de Gibran, Kahlil Goodwin, Leo Google Graham, Benjamin Greenblatt, Joel. See also Magic Formula; Value Investment Club Gujarat, India Guru Focus H Harazim, Tom I Indexes Information sources/business publications Innovation, see Copycat businesses Intelligent Investor, The (Graham) Intrinsic value: calculating company’s life expectancy and discount to distressed businesses and margin of safety and odds and investing and selling of stock and K Karmet Steel Works Kelly, John Larry Jr. Kelly Formula: Berkshire Hathaway’s Washington Post investment and few bets investing and investment selling and Knightsbridge Tankers Limited Kroc, Ray L Lampert, Eddie Level 3 Communications Little Book That Beats the Market, The (Greenblatt).

Python Network Programming Cookbook
by M. Omar Faruque Sarker
Published 15 Feb 2014

Tom Stephens has worked in software development for nearly 10 years and is currently working in embedded development dealing with smartcards, cryptography, and RFID in the Denver metro area. His diverse background includes experience ranging from embedded virtual machines to web UX/UI design to enterprise Business Intelligence. He is most passionate about good software design, including intelligent testing and constantly evolving practices to produce a better product with minimal effort. Deepak Thukral is a polyglot who is also a contributor to various open source Python projects. He moved from India to Europe where he worked for various companies helping them scale their platforms with Python.

The Price of Tomorrow: Why Deflation Is the Key to an Abundant Future
by Jeff Booth
Published 14 Jan 2020

Virtual and augmented reality (mixed reality) will offer a different, more immersive connection with our technology, and it will change the way many things are done. Take, for example, a startup in Vancouver called LlamaZOO, which is in a new category of data collection called spatial data that is at the intersection of digital twinning (an exact twin of the physical world that is digital), mixed reality, and business intelligence. By twinning the real world via satellite imagery, drones, and lidar, and adding global positioning, mapping, and other data streams, the company uses mixed reality to reduce the cost of planning and work in the physical world. It allows for remote analyzing of massive amounts of data without traversing faraway sites with people.

CIOs at Work
by Ed Yourdon
Published 19 Jul 2011

And a certain portion of our customer base really likes getting information that way, so I think there’s a great opportunity in that mobility space, especially to engage our customers in more two-way communication. The second area where there’s a huge opportunity for us is on data analytics. Business intelligence is going to be huge because we have so many sensors out on our network now that we can better predict when an outage will occur. We’ll never be able to predict them all because you don’t know where lightning’s going to hit. You don’t know when somebody’s going to run into a pole in their car.

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Index A AdKnow ledge, Inc., 87 Agile development methodology, 62 Amazon, 314, 319 America COMPETES Act, 304 American Airlines, 47, 72 American Defense Department, 84 American Marketing Society, 113 American Production Inventory Control Society, 211 Ames, 320 AMR Corp, 47 Android, 43 Annapolis, 340 Apple, 97, 101, 217, 242, 295 Computer, 35 Genius Bar, 8 Archipelago Holdings Inc., 87 Arizona Public Service (APS) Company, 66, 211, 223 Arizona State University, 227 ARPANET, 19, 117, 135 Art of Computer Programming, 2 Atlanta-based Southern Company, 191 AT&T, 191, 249 B Ballmer, Steve, 39 Bank of Boston, 47 Baylor-Grapevine Board of Trustees, 47 Bedrock foundation, 249 Bell Atlantic Mobile, 231 Bell Labs, 2, 249 BlackBerry, 60, 96, 116, 121, 171, 184, 246, 261, 296, 317 Blalock, Becky, 182, 191, 215 adaptability, 192 Air Force brat, 191, 192 Atlanta-based Southern Company, 191 banking industry, 203 Boucher, Marie, 196 brainstorm, 202 24/7 business, 199 business intelligence, 204 cloud computing, 205 cognitive surplus, 206 cognitive time, 206 Coker, Dave, 196 communication and education, 200 Community and Economic Development, 194 consumer market, 202 cybersecurity, 207, 209 data analytics, 204, 205 disaster recovery, 209 distributed generation, 204 distribution organization, 201 Egypt revolution, 198 farming technology, 206 finance backgrounds/marketing, 200, 209 Franklin, Alan, 193 Georgia Power, 191 Georgia Power Management Council, 193 global society, 206 Google, 198 incredible technology, 195 Industrial Age, 206 Information Age, 206 InformationWeek's, 196 infrastructure, 202 intellectual property, 196 intelligence and redundancy, 207 Internet, 198, 206 leapfrog innovations, 205 mainframe system, 207 marketing and customer service, 193, 200 MBA, finance, 192 microfiche, 207 microwave tower, 207 mobile devices, 203 mobility and business analytics, 205 Moore's Law, 205 new generation digital natives, 197 flexible and adaptable, 199 innovation and creativity, 199 superficial fashion, 198 Olympic sponsor, 193 out pushing technology, 202 reinforcement, 201 sense of integrity, 200 Southern Company, 194, 198, 201, 207 teamwork survey, 201 technology lab, 202 undergraduate degree, marketing, 192 virtualization, 205 VRU, 203 Ward, Eileen, 196 wire business, 201 world-class customer service, 203 Bohlen, Ken, 211 American Production Inventory Control Society, 211 Apple, 217 APS, 211, 223 ASU, 227 benchmarking company, 216 chief innovation officer, 229 Citrix, 217 cloud computing, 218, 219 cognitive surplus, 220 DECnet, 212 Department of Defense, 222 distributed computing, 217 energy industry, 214 gizmo/whiz-bang show, 216 GoodLink, 217 hard-line manufacturing, 218 home computing, 219 home entertainment, 219 Honeywell, 219 HR generalists, 215 information technology department, 211 Intel machines, 217 John Deere, 213 just say yes program, 223 Lean Six Sigma improvement process, 211 Linux, 220 MBA program, 214 mentors, 213 national alerts, 224 North American universities, 228 paradigm shifts, 218, 220 PDP minicomputers, 212 Peopleware, 226 prefigurative culture, 221 R&D companies, 218 Rhode Island, 226 role models, 213 San Diego Fire Department, 224 security/privacy issues, 217 skip levels, 223 smart home concepts, 219 smartphone, 217 social media, 225 Stead, Jerry, 214 Stevie Award, 211 Storefront engineering, 212 traditional management, 219, 226 Twitter, 224 vocabulary, 221 Waterloo operations, 213 Web 2.0 companies, 227 Web infrastructure, 215 wikipedia, 220 Y2K, 222 Botnets, 23 Brian's and Rob Pike's, 2 Bristol-Myers Squibb, 33 Broadband networks, 241 Brown, 227 Bryant, 227 BT Global Services, 253 BT Innovate & Design (BTI&D), 253 Bumblebee tuna, 130 C Career writing technology, 67 CASE tools, 232 Cash, Jim, 50 Christensen, Clyde, 212 Chrome, 14, 18 Chrysler Corporation, 175 Citibank, 337 Citicorp, 313 Citrix, 217 Client-server-type applications, 59 Cloud computing, 218, 219, 239, 240, 261, 262, 310, 311, 313 Cloud technology, 62 CNN, 54 COBOL, 250 Cognitive surplus, 20, 79, 206, 291 College of Engineering, University of Miami, 113 Columbia University, 1 Community and Economic Development, 194 Computer Sciences Corporation, 35 Computerworld magazine, 196 Consumer-oriented technology, 22 Content management system, 133 Corporate information management (CIM) program, 309 Corporate Management Information Systems, 87 Corvus disk drive, 36 Customer Advisory Boards of Oracle, 191 Customer-relationship management (CRM), 56 Cutter Business Technology Council, 173 D Dallas Children's Medical Center Development Board, 48 DARPA, 19 DDoS attacks and security, 81 DECnet, 212 Dell Platinum Council, 113 DeMarco, Tom, 16, 226 Department of Defense, 222, 329, 332 Detroit Energy, 252 Digital books, 30 Digital Equipment, 48 Distributed computing, 217 Dodge, 189 Dogfooding, 11, 37, 38, 236 DTE Energy, 173 DuPont Dow Elastomers, 151 E Educational Testing Service (ETS), 151 E-government, 282, 285 Electrical distribution grid, 182 Elementary and Secondary Education Strategic Business Unit, 151 Elements of Programming Style, 2 Ellyn, Lynne, 173 advanced technology software planning, 175 Amazon, 184 artificial intelligence group, 175 Association for Women in Computing, 173 benchmark, 180, 181 BlackBerries, 184 Burns, Ursula, 175 Chrysler, 176 Cisco, 186 cloud computing, 183, 184 component-based architecture, 186 corporate communications customer service, 185 Crain's Detroit Business, 173 cyber security threats, 177 degree of competence, 187 diversity and sophistication, 182 DTE Energy, 173 energy trading, 176 engineering and science programs, 188 enterprise business systems policy, 186 executive MBA program, 176 Facebook, 185 fresh-out-of-the-university, 187 General Electric, 174 Google, 184 Grace Hopper, 174 grid re-automation, 182 Henry Ford Hospital, 174 internal social media, 185 International Coaching Federation, 178 iPads, 184 IP electrical grids, 182 iPod applications, 182 IT budgets, 186 IT responsibilities, 176 Java, 186 level of sophistication, 179 lobbying efforts, 181 medical computing, 175 Miller, Joan, 174 Mulcahey, Anne, 175 Netscape, 175 neuroscience leadership, 189 object-oriented programming, 186 Oracle, 186 peer-level people, 179 people system, 177 policies and strategies, 180 Radio Shack, 180 remote access capacity, 189 security tool and patch, 183 sense of community, 180 Shipley, Jim, 174 smart grid, 177, 182 smart meters, 182 smart phone applications, 183 swarming, 179 technical competence, 178, 179 Thomas, Marlo, 174 Twitter, 185 UNITE, 181 vendor community, 186 virtualization, 183, 184 Xerox, 175 E-mail, 9 Employee-relationship management (ERM), 56 Encyclopedia, 115 Encyclopedia Britannica, 292 ERP, 123 F Facebook, 244 Ellyn, Lynne, 185 Sridhara, Mittu, 73, 84 Temares, Lewis, 116, 121, 131 Wakeman, Dan, 169 Federal information technology investments, 299 Flex, 236 Ford, 102 Ford, Monte, 47 agile computing, 59 agile development, 62, 66 airplanes, 51 American Airlines, 47 Arizona Public Services, 66 Bank of Boston, 47 Baylor-Grapevine Board of Trustees, 47 BlackBerry, 60 board of Chubb, 51 board of Tandy, 51 business organizations, 63 business school, dean, 50 career writing technology, 67 client-server-type applications, 59 cloud technology, 62 CNN, 54 common-sense functionality, 49 consumer-based technology, 60 CRM, 56 Dallas Children's Medical Center Development Board, 48 Digital Equipment, 48 ERM, 56 financial expert, 69 frequent-flier program, 57 frontal lobotomy, 57 Harvard Business Review, 50 HR policies, 65 IBM, 48 information technology, 47, 52 Internet, 54 Internet-based protocol, 59 iPhone, 52 IT stuff, 58 Knight Ridder, 51 legacy apps, 59 mainframe-like applications, 59 management training program, 64 marketing and technical jobs, 48 Maynard, Massachusetts mill, 48 MBA program, 50 mentors, 49 Microsoft, 50 mobile computing, 62 New York Times, 53 operations center, 54 PDP-5, 49 PDP-6, 49 Radio Shack, 51 revenue management, 57 role models, 49 security paradigms, 62 self-service machine, 57 Silicon Valley companies, 68 smartphones, 54 social networking, 51, 53, 56, 58 stateful applications, 59 techie department, 48 The Associates First Capital Corporation, 47 transmission and distribution companies, 47 wireless network, 59 YouTube, 65 Fort Worth, 226 Free software foundation, 19 Fried, Benjamin, 1, 241 agile development, 25 agile methodologies, 26 Apple Genius Bar, 8 ARPANET, 19 Art of Computer Programming, 2 Bell Labs, 2 books and records, accuracy, 25 botnets, 23 Brian's and Rob Pike's, 2 cash-like principles, 29 CFO, 4 check writers, 18 chrome, 14, 18 classic computer science text, 1 cognitive surplus, 20 Columbia University, 1 compensation management, 7 competitive advantage, 9, 18 computer science degree, 1 computer scientists, 6 consumer-driven computing, 12 consumer-driven software-as-a-service offerings, 12 consumer-driven technology, 12 consumer-oriented technology, 14, 22 corporate leadership, 25 cost centers, 4 DARPA, 19 decision makers, 17 decision making, 13 360-degree performance management, 7 detroit energy, 30 digital books, 30 document workbench, 2 dogfooding, 11 e-books, 29 Elements of Programming Style, 2 e-mail, 9 end-user support, 7 engineering executive group, 4 European vendors, 6 file servers and print servers, 17 Folger Library editions, 30 free software foundation, 19 German company, 13 German engineering, 13 Gmail, 15 Godot, 26 Google, 1 books, 29 products, 5, 10 software engineers, 6 hiring managers, 6 HR processes and technologies, 6 IBM model, 13 instant messaging, 9 Internet age, 6 interviewers, training, 6 iPad, 29 iPhone, 29 IPO, 3 IT, engineering and computer science parts, 4 Knuth's books, 2 Linux machine, 8 Linux software, 19 machine running Windows, 8 Macintosh, 8 Mac OS, 9 macro factors, 11 Managing Director, 1 mentors, 1 microcomputers, 18 Microsoft, 5 Minds for Sale, 20 Morgan Stanley, 1–3, 5, 16 nonacademic UNIX license, 2 nontechnical skills, 5 oil exploration office, 17 open-source phone operating system, 20 outlook, 15 PARC, 19 performance review cycles, 7 personal computer equipment, 15 post-Sarbanes-Oxley world, 25 project manager, 13 quants, 24 rapid-release cycle, 26 R&D cycle, 24 regression testing, 27 role models, 1 shrink-wrapped software, 14 signature-based anti-virus, 22 smartphone, 20, 27 social contract, 8 society trails technology, 21 software engineering tool, 13 software installation, 14 supply chain and inventory and asset management, 10 SVP, 4 telephony, 17 ten things, 13 TMRC, 19 TROFF, 2 typesetter workbench, 2 UI designer, 14 university computing center, 28 videoconferencing, 12 Visicalc, 24 Wall Street, 23 Walmart, 6 waterfall approach, 25 XYZ widget company, 5 YouTube video, 20 G Gates, Bill, 39, 50 General Electric, 134 General Foods, 309, 326–328 General Motors, 33, 321, 329, 332 George Mason School of Information Technology, 309 Georgia Power Company, 191–193, 196 Georgia Power Management Council, 193 German company, 13 German engineering, 13 German manufacturing company, 232 Gizmo/whiz-bang show, 216 Gmail, 15 GoodLink, 217 Google, 1, 84, 85, 117, 217, 219, 220, 222, 235, 241, 263, 302, 319 apps, 314 books, 29 commercial products, 10 model, 293 Government Accountability Office (GAO), 305 4G program, 250 4G smartphone, 235 GTE, 231 Gupta, Ashish aspiration, organization, 256 bandwidth and network infrastructure, 267 BlackBerry, 261 business and customer outcomes, 274 capital investment forums, 269 career progression, 255 cloud-based shared infrastructure model, 263 cloud computing, 261, 262 collaboration, 272 communications infrastructure, 258 compute-utility-based model, 262 control and integrity, 268 core competency, 255 core network infrastructure, 267 core strengths, 256 cost per unit of bandwidth, 267 customer demands, 268 data protection, 261, 262 decision-making bodies, 269 demographics, 272, 273 device convergence, 263 dogfooding, 259 employee flexibility, 260, 264 engagement and governance, 269 enterprise market segment, 261 equipment management, 260 executive MBA, 256 fourth-generation LTE networks, 267 functional service departments, 270 Global Services, distributed organization, 257 Google, 263, 275 Google Apps, 266 handheld devices, 265 hastily formed networks, 258 IMF, 266 innovation and application development, 265 iPad, 257, 260, 261, 266,267 iPhone, 266 Japan, 257, 258 London Business School, 253 management functions, 257 management sales functions, 257 market segments, 259 MBA, General Management, 253 measurements, 271 messaging with voice capability, 264 mini-microcomputer model, 261 mobile communications network, 258 mobile-enabling voice, 259 mobile phone network, 260 mobile traffic explosion, 265 network infrastructures, 265 network IT services, 254 network quality, 257 new generation digital natives, 271 disadvantages, 273 Google, 273 opportunities, 273 Olympics, 263 opportunities, 275 organizational construct, 272 outsourced network IT services, 259 outsourcing, 271 per-use-based model, 262 portfolio and business alignment, 274 Portfolio & Service Design (P&SD), 253 primary marketing thrust, 264 product development thrust, 264 product management team, 259 project and program management, 255 resource balance, 270 scalability, 262 security, 262 Selley, Clive, 254, 255 service delivery organization, 254 single-device model, 264 smart devices, 267 smart phones, 266 telecommunications capability, 259 upward-based apps, 264 virtualization, 261 voice-over-IP connections, 258 Windows platform, 261 Gurnani, Roger, 231 accounting/finance department, 233 analog cellular networks, 250 AT&T, 249 bedrock foundation, 249 Bell Atlantic Mobile, 231 Bell Labs, 249 blogs, 244 broadband networks, 241 business benefits, 237 business device, 240 business executives, 238 business leaders, 248, 249 business relationship management, 248 buzzword, 239 CASE tools, 232 cloud computing, 239, 240 COBOL, 250 consumer and business products, 231 consumer electronics devices, 241 consumer telecom business, 233 customer-engagement channel, 244 customer forums, 244 customer support operations, 251 customer-touching channels, 236 degree of control, 246 distribution channel, 250 dogfooding, 236 ecosystem, 243, 249 enterprise business, 233 ERP systems, 236 face-to-face communications, 244 FiOS product, 235 flex, 236 "follow the sun" model, 239 German manufacturing company, 232 4G program, 250 4G smartphone, 235 hardware/software vendors, 247 information assets, 245 information technology strategy, 231 intellectual property rights, 244 Internet, 235, 239 iPhone, 243 Ivan, 232 Lowell, 232 LTE technology-based smartphone, 235 marketing, 251 MIT, 246 mobile technology, 234 Moore's law, 242 MP3 file, 235 network-based services, 240 Nynex Mobile, 233 P&L responsibility, 251 PDA, 238 personal computing, 235 product development, 234, 251 role models, 232 sales channels, 251 smartphones, 238 state-level regulatory issues, 251 state-of-the-art networks, 243 telecom career, 232 telephone company, Phoenix, 234 Verizon Communication, 231, 232 virtual corporations, 241 Web 2.0, 244 Williams Companies, 232, 233 WillTell, 233 wireless business, 233 H Hackers, 19 Harmon, Jay, 213 Harvard Business Review, 50 Harvard Business School, 331 Heller, Martha, 171 Henry Ford Hospital, 174 Hewlett-Packard piece, 129 Home computing, 219 Honda, 102 Honeywell, 219 Houghton Mifflin, 134, 136 I IBM, 48, 250 manpower, 311 model, 13 Indian IT outsourcing company, 255 Information technology, 52 Intel machines, 217 International Coaching Federation, 178 Internet, 9, 44, 54, 117, 235, 239, 316, 322 Internet-based protocol, 59 Interoperability, 341 iPads, 2, 94, 97, 184, 257, 260, 264, 267, 288, 289, 295, 296 IP electrical grids, 182 iPhones, 43, 52, 96, 101, 170, 181, 260, 264,296 iPod, 101 IT lifecycle management process, 37 Ivan, 232 J John Deere, 213 K Kansas, 226 Kernigan, Brian, 2 Knight Ridder, 51 Knuth, Donald, 2, 29 Kraft Foods Inc, 309 Krist, Nicholas, 28 Kundra, Vivek Clever Commute, 305 cognitive surplus, 303 command and control systems, 301 consumerization, 302 consumption-based model, 300 cyber-warfare, 301 Darwinian pressure, 302 desktop core configuration, 306 digital-borne content, 301 digital oil, 300, 307 digital public square, 304 enterprise software, 303 entrepreneurial startup model, 306 frugal engineering, 306 Google, 302 government business, 302 innovator's dilemma, 307 iPad, 302 IT dashboard, 302 leapfrog technology, 306 massive consumerization, 301 megatrends, 301 parameter security, 302 Patent Office, 305 pharmaceutical industry, 304 phishing attacks, 301 policy and strategic planning, 299 security and privacy, 301 server utilization, 300 social media and technology, 300, 306 storage utilization, 300 Trademark Office, 305 Wikipedia, 303 L LAN, 259 Lean Six Sigma improvement process, 211 Levy, Steven (Hackers), 19 Linux, 220 machine, 8 open-source software, 19 Lister, Tim, 226 London Business School, 73, 253, 256 Long-term evolution (LTE), 235 Lowell, 232 M MacArthur's intelligence officer, 327 Macintosh, 8 Mainframe computers, 118 Mainframe-like applications, 59 Marriott's Great America, 35 McDade, 327 McGraw-Hill Education, 133, 147, 150 Mead, Margaret, 221 Mendel, 311 Microcomputers, 18 Microsoft Corporation, 5, 11, 33, 36, 38, 41, 44, 46, 50, 156, 217, 223, 236, 250, 293 Microsoft Higher Education Advisory Group, 113 Microsoft's operational enterprise risk management, 33 Middlesex University, 189 Miller, Joan Apple products, 295 authority and accuracy, 292 award-winning ICT programs and services, 277 back locked-down information, 289 big-scale text issues, 294 big-time computing, 279 BlackBerry, 296 business management training, 281 business skills, 281 central government, 283 cognitive surplus, 291 community care project, 278 community development programs, 277, 278 computers, constituency office, 294 confidential information, 284 data management, 281 decision making, 286 democratic process, 288 economics degree, 278 e-government, 282, 285 electronic communication, 289 electronic-enabled public voice, 286 electronic information, 288 electronic media, 286 electronic records, 280, 284 electronic services, 294 e-mail, 289, 290, 295 forgiving technology, 296 front-office service, 282, 283 Google, 292 Google's cloud service, 290 Government 2, 287 Health and Social Care, 284 House business, 294 House of Lords, 288 ICT strategy, 289, 290 information management, 278 insurance company, 278 Internet information, 285 iPad, 288, 289, 296 IT data management, 279 management principle, 280 local government, 283 mainframe environment, 289 member-led activity, 287 messages, 289 Microsoft, 293 Microsoft's cloud service, 290 mobile electronic information, 284 mobile technology, 289 national organization, 284 network perimeters, 290 official government information, 285 on-the-job training, 281 organizational planning, 278 Parliamentary ICT, 277 project management, 279 public sector, 282 public transportation, 285 quango-type organizations, 283 representational democracy, 286 security, 290, 291 social care organization, 279 social care services, Essex, 278 social care systems, 284 social networking, 285 sovereignty, 291 sustainability and growth, 293 technical language, 294 technology skills, 281 transactional services, 285 transferability, 291 Web-based services, 285 Wikipedia, 291, 292 X-factor, 286 Minds for Sale, 20 Mitchell & Co, 333 MIT Media Labs, 149 Mobile computing, 62 Mobile technology, 234 Mooney, Mark, 133 artificial intelligence, 134 back-office legacy, 136 balancing standpoint, 145 BBC, 140 Bermuda Triangle, 135 BlackBerry shop, 142 Bureau of National Standards, 136 business model, 140 career spectrum, 144 cloud computing, 148 competitive intelligence and knowledge, 143 Connect, 141 customer-facing and product development, 135 customer-facing product space, 137 customer space and product development, 136 digital products development, 144 digital space and product, 146 educational and reference content, 139 educational products, 141 entrepreneur, 150 General Electric, 134 GradeGuru, 140 handheld devices, 142 hard-core technical standpoint, 146 hardware servers, 142 Houghton Mifflin, 134, 136 HTML, 138 industrial-strength product, 141 intellectual content, 148 Internet, 148 iPad, 138, 139, 142 iPhone, 142, 143 iTunes, 138 Klein, Joel, 147 learning management systems, 137 long-term production system, 141 Marine Corps, 134 McGraw-Hill Education, 133, 147 media development, 144 media space, 138, 142 mobile computing, 139 MOUSE, 150 online technology, 138 open-source capabilities, 142 Oracle quota-management system, 143 people's roles and responsibilities, 137 Phoenix, 149 product development, 149 publishing companies, 142 publishing systems, 137 Reed Elsevier, 133, 136 Salesforce.com, 144, 149 scalability testing, 145 senior business leaders, 146 social network, 148 soft discipline guidelines, 141 solar energy, 149 Strassmann, Paul, 135 technical skill set, 143, 144 testing systems integration, 145 The Shallows, 139 transactional systems, 142 trust and integrity, 145 TTS, QuickPro, and ACL, 144 Vivendi Universal, 134 War and Peace today, 139 Moore's law, 242 Morgan Stanley, 2, 3, 16 N NASA, 309, 333, 334 National Institute of Standards and Technology (NIST), 173 Naval Postgraduate School, 134 Netscape, 175 New Brunswick model, 282 News Corp., 147 New York Stock Exchange (NYSE), 87, 116, 223, 278 New York Times, 53 North American universities, 228 NSA/CIA software, 134 Nynex Mobile, 233 O Oil exploration office, 17 Open-source phone operating system, 20 Outlook, 15 P Pacer Software, 135 Paradigm shifts, 218, 220 Parks and Recreation Department, 126 PDP minicomputers, 212 Peopleware, 226 Personal computing, 235 Personal digital assistant (PDA), 238 Petri dish, 44 Phoenix, 211 Plauger, Bill, 2 Q Quants, 24 R Radio Shack, 51 Reed Elsevier, 133, 136 Reed, John, 335 Rubinow, Steve, 87 AdKnowledge, Inc., 87 agile development, 110 Agile Manifesto, 110 Archipelago Holdings Inc., 87 attributes, 108 capital market community, 91 cash/actual trading business, 88 channel marketing departments, 92 cloud computing, 97 CNBC, 89 collaborative technology, 95 collective intelligence, 95 communication skills, 102, 106 conference organizations, 99 consumer marketplace, 94 data center, 90 decision making, 105, 108 economy standpoint, 100 e-mail, 100 Fidelity Investments, 105 financial services, 92 IEEE, 101 innovative impression, 94 Internet, 98 iPad, 97 iPod device, 91 labor laws, 110 listening skills, 106 logical progression, 104 Mac, 96 mainframe, 104 management and leadership, 104, 105 market data system, 89 micro-second response time, 89 mobile applications, 94 multidisciplinary approach, 103 multimedia, 97 multi-national projects, 110 multiprocessing options, 99 network operating system, 103 NYSE Euronext, 87 open outside system, 88 parallel programming models, 99 personal satisfaction, 109 PR function, 106 proclaimed workaholic, 109 real estate business, 88 regulatory and security standpoint, 96 Rolodex, 94 Rubin, Howard, 99 server department, 97 software development, 89 sophisticated technology, 101 technology business, 88 technology integration, 91 trading engines, 90 typewriter ribbon, 94 virtualization, 98 Windows 7, 96 younger generation video games, 93 visual interfaces, 93 Rumsfeld, Donald, 222 S San Diego Fire Department, 224 Santa Clara University, 36 SAS programs, 131 Scott, Tony, 10, 33, 236 Android, 43 Apple Computer, 35 architectural flaw, 44 BASIC and Pascal, 35 Bristol-Myers Squibb, 33 Bunch, Rick (role model), 34 business groups, 42 COO, 39 Corporate Vice President, 33 Corvus disk drive, 36 CSC, 35 Defense department, 45 dogfooding, 37, 38 games and arcades, 35 General Motors, 33 IBM's role, 37 information systems management, 36 integrity factor, 40 Internet, 44 iPhone, 43 IT lifecycle management process, 37 leadership capability, 40 leisure studies, 34 macro-architectural threats, 44 Marriott's Great America, 35 math models, 36 Microsoft Corporation, 33, 36, 38, 41, 44, 46 Microsoft's operational enterprise risk management, 33 parks and recreation, 34 Petri dish, 44 playground leader, 42 product groups, 42 quality and business excellence team, 33 Santa Clara University, 36 Senior Vice President, 33 smartphone, 43 social computing, 38 Sun Microsystems, 36 theme park industry, 35 University of Illinois, 34 University of San Francisco, 36 value-added business, 33 Walt Disney Company, 33 Senior Leadership Technology and Product Marketing, 71 Shakespeare, 30 Shirky, Clay, 220 Sierra Ventures, 191 Silicon Valley companies, 68 Silicon Valley software factories, 323 Skype, 118 Smart Grid Advisory Committee, 177 Smartphones, 20, 27, 43, 54, 217, 238 Social care computer electronic record system, 279 Social computing, 38, 320 Social networking, 51, 53, 56, 58 Society trails technology, 21 SPSS programs, 131 Sridhara, Mittu, 71 Amazon, 76 American Airlines, 72 back-end computation and presentation, 80 banking, 77 B2B and B2C, 85 business/product departments, 82 business work context, 74 buzzword, 77 career aspiration, 73 career spans, 73 coders, 72 cognitive surplus, 79 competitive differentiation, 74 computing power, 78 contribution and energy, 85 convergence, 75 CPU cycles, 78 cross-channel digital business, 71 cultural and geographic implementation, 72 customer experience, 84, 85 customer profile, 76 data visualization, 79, 80 DDoS protection, 81 economies of scale, 77 elements of technology, 72 encryption, 82 end customer, 83 entertainment, 75 ERP system, 72 Facebook, 84 finance and accounting, 73 foster innovation and open culture, 81 friends/mentors/role models, 74 FSA, 76 gambling acts, 81 games, 79 gaming machines, 80 GDS, 72 global organization, 71 Google, 75, 84, 85 Group CIO, Ladbrokes PLC, 71 industry-standard technologies, 77 integrity and competence, 83 IT, 74, 82 KickOff app, 71 land-based casinos, 79 live streaming, 78 London Business School, 73 mobile computing, 78 multimedia, 84 new generation, 84 on-the-job training, 73 open-source computing, 79 opportunity, 80, 83 PCA-compliant, 81 personalization, 76 real-time systems, 74 re-evaluation, 81 reliability and availability, 77 security threats, 80 smart mobile device, 75 technology-intense customer, 85 top-line revenue, 74 trader apps, 82 true context, 73 underpinning business process, 76 virtualization, 78 Visa/MasterCard transactions, 78 Web 3.0 business, 76 web-emerging web channel, 76 Wikipedia, 79, 85 Word documents and e-mail, 82 work-life balance, 84 young body with high miles, 72 Zuckerberg, Mark, 73 Stead, Jerry, 214 Storefront engineering, 212 Strassmann, Paul, 228, 309 agile development, 340 Amazon EC2, 314 America information processors, 322 Annapolis, 340 AT&T, 332 backstabbing culture, 339 BlackBerry, 317 block houses, 319 CFO/CEO position, 337 CIM program, 309 Citibank, 337 Citicorp, 313, 339 cloud computing, 310, 311, 313 coding infrastructure, 341 communication infrastructure, 341 corporate information management, 329 Corporate Information Officer, 309 counterintelligence, 320 cyber-operations, 338 Dell server, 314 Department of Defense, 329, 332 Director of Defense Information, 309 employee-owned technology, 316 enterprise architecture, 316 exfiltration, 313 financial organizations, 320 firewalls and antiviruses, 312 General Foods, 309, 326–328 General Motors, 321, 329, 332 George Mason School of Information Technology, 309 Google apps, 314 government-supported activities, 326 Harvard Business School, 331 HR-related issues, 331 IBM manpower, 311 infiltration, 313 Internet, 316, 322 interoperability, 315, 317, 341 Kraft Foods Inc, 309 MacArthur's intelligence officer, 327 Machiavellian view, 327 mash-up, 316 military service, 331 NASA, 309, 333, 334 police department, economics, 312 powerpoint slides, 324 Radio Shack, 319 senior executive position, 334 service-oriented architecture, 316 Silicon Valley software factories, 323 social computing, 320 Strassmann's concentration camp, 318 structured methodologies, 342 U.S.

I'm Feeling Lucky: The Confessions of Google Employee Number 59
by Douglas Edwards
Published 11 Jul 2011

There were other glimpses of Larry's thinking. He and Eric shared a list of possible strategies that included Google as the publisher of all content, where users would pay us and we would reimburse the creators of everything from books to movies to music. Google as a provider of market research and business intelligence based on what we knew about the world. Google as an infrastructure platform and communications provider tying email and web data together. Google as the leader in machine intelligence backed by all the world's data and massive computing power that learned as it went along. He had no small plans.

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Alan understood exactly what was at stake, and he knew the opposition. Overture had been born as GoTo in Pasadena, right down the street from the Jet Propulsion Laboratory. Even though they drew talent from Cal Tech and were plenty smart, they weren't Silicon Valley smart. They weren't really a tech company, and they didn't have the business intelligence Alan saw at Google. On the other hand, they had come up with the idea of marrying ads with search and implemented it before Google had. When Omid called the AOL business-development unit run by David Colburn, AOL agreed to start talks with Google. Not because they intended to give us their business, but because they could use Google as a bludgeon to beat a better deal out of Overture when their contract came up for renewal.

97 Things Every Programmer Should Know
by Kevlin Henney
Published 5 Feb 2010

Upon graduating from Edinburgh University in 1987, he worked on the REKURSIV project before becoming a freelance contractor. Today, his primary software interests are agile practices and the resuscitation of legacy code. Chapter 1 Steve Berczuk Steve Berczuk is a software engineer at Humedica, where he develops business intelligence solutions for the healthcare industry. He has been developing software applications for over 20 years, and is the author of Software Configuration Management Patterns: Effective Teamwork, Practical Integration (Addison-Wesley Professional). In addition to developing software, he enjoys helping teams deliver more effectively through the use of agile methods and software configuration management.

The Aristocracy of Talent: How Meritocracy Made the Modern World
by Adrian Wooldridge
Published 2 Jun 2021

The large group of researchers who flooded into academia after the Second World War were professionals who measured their lives in terms of Ph.D.s earned and articles accepted. Yet even as academics became more obsessed with producing professional articles, universities became more important in controlling entry into the professional life, including business life. BUSINESS INTELLIGENCE In Chapter Ten we noted that American business played a significant part in nurturing the meritocratic spirit. Giant businesses such as the railroads required specialized middle-managers to keep the trains running on time, and great universities such as the University of Pennsylvania and Harvard established business schools to produce them.

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W. 319 Bush, George P. 319 Bush, George W. 316, 318, 319, 331–2 business and academic qualifications 340 American corporations 197, 252, 253 American robber barons 17, 193, 196–7 consultancies 253, 322, 352 corporate disasters and scandals 337–8 family 370 and global elite 321–3 meritocratic companies 2, 3 public 370 regulators 343 screening for class privilege 318 women in senior positions 273, 275 business intelligence 252–5 business schools 308 America 253–4 business theory 254–5 Butler, Nicholas Murray 197–8 Butler, R. A. B. 235 Butler, Samuel 152 Byron, Lord 142 C. H. Stoelting Company 214 California, University of 239, 251, 303 Callixtus III, Pope (Alfonso de Borgia) 47 Calvin, John 111 Calvinism, and ambition 34 Cambridge, Prince George, Duke of 160 Cambridge University 32 and elite schools 8, 307 examinations 149–51 ‘gentleman commoners’ 231 Girton College 268, 270–71 grammar-school students 246 King’s College 103 Newnham College 268, 269–70 Royal Commission (1850) 159 and school examinations 161 women admitted to men’s colleges 275 Cameralists, Austria 122–3 Cameron, David 2, 3, 48, 392 Campbell, James 69 Cao Xueqin, The Dream of the Red Chamber 83 Caplan, Gordon 315 ‘captains of learning’ (US) 223–7 Cardwell, Edward 160 Carlson, Tucker 6, 334 Carlyle, Thomas 127 on literary genius 141–2 Sartor Resartus 173 Carmichael, Stokely, Black Power 298 Carnegie, Andrew 193, 196, 230 Carnegie Foundation 254 Catherine of Braganza, wife of Charles II 41 Catherine the Great, of Russia 43, 123–4 and aristocracy 129 Catholic Church, Voltaire on 119 central banks 385 chain of being, notion of 28, 123 Challand, Albert 206 Chamberlain, Houston Stewart 87 Chamberlain, Neville 394 Charlemagne 26 and Jews 93 and succession 42 Charles the Bald, king of France 42 Charles Frederick, Grand Duke of Baden 124 Charles II, king of England 41, 52, 144 and patronage 55 Charles II, king of Spain 46 Charles V, emperor 45, 110 Charles VI, king of France 40–41 Charterhouse School 104 Chatham House 343 Chauncey, Henry 224, 226–7 Chauvin, Derek 346 chess grandmasters 86 Chesterton, G.

Everydata: The Misinformation Hidden in the Little Data You Consume Every Day
by John H. Johnson
Published 27 Apr 2016

In his litigation work, he guides companies and outside counsel on the appropriate use and interpretation of complex data sets, and has served as an expert witness in some of today’s most high-stakes corporate lawsuits. On the business analytics side, Dr. Johnson helps companies translate their complex internal data sets into strategic, actionable information across a variety of business settings including human resources, finance, marketing, manufacturing, and business intelligence. Both aspects share the need to understand—and properly apply—large, complex sets of data. He applies this same skill to his writing and speaking, where he helps audiences avoid common pitfalls people make when confronted with data, so they can become more confident and discerning consumers of data and make better decisions in their professional and personal lives.

Numpy Beginner's Guide - Third Edition
by Ivan Idris
Published 23 Jun 2015

Shrawane Copy Editors Charlote Carneiro Vikrant Phadke Sameen Siddiqui About the Author Ivan Idris has an MSc in experimental physics. His graduaton thesis had a strong emphasis on applied computer science. Afer graduatng, he worked for several companies as a Java developer, data warehouse developer, and QA Analyst. His main professional interests are business intelligence, big data, and cloud computng. Ivan enjoys writng clean, testable code and interestng technical artcles. He is the author of NumPy Beginner's Guide , NumPy Cookbook , Learning NumPy Array , and Python Data Analysis . You can fnd more informaton about him and a blog with a few examples of NumPy at http://ivanidris.net/ wordpress/ .

Subscribed: Why the Subscription Model Will Be Your Company's Future - and What to Do About It
by Tien Tzuo and Gabe Weisert
Published 4 Jun 2018

It has clearly mastered the ability to migrate customers from acquired companies onto a single platform for improved accuracy and efficiency of its back-office systems. OPTIMIZE YOUR PRICING AND PACKAGING Over the course of the entire lifetime of a subscription business, do you know how much time the average management team devotes to planning their pricing? According to business intelligence platform ProfitWell, the average amount of time a company spends per year on pricing is less than ten hours. That’s nuts, especially considering the huge impact that pricing has on your bottom line—it can be much more impactful than similar amounts of effort spent on acquisition or retention.

The AI-First Company
by Ash Fontana
Published 4 May 2021

Managed by those that otherwise manage engineers. Data engineer: some difference. Managed by those that otherwise manage engineers but may require coordination by those managing a company’s data assets, such as a chief data officer. Data analyst: little difference. Management by analytics or business intelligence leaders, or by a general manager within a business unit. Data scientist: different. Management by nonanalytics leaders is difficult because the work is more experimental and involves advanced analytical methods. Management by engineering is difficult because most of the work is mathematics rather than engineering.

The Future of Money: How the Digital Revolution Is Transforming Currencies and Finance
by Eswar S. Prasad
Published 27 Sep 2021

The Dominant Dollar On the dollar’s role as an invoicing currency in international trade, see Gopinath (2016) and chart 26 in “The International Role of the Euro,” European Central Bank, June 2019. Data on the relative importance of various currencies in international payments come from the SWIFT RMB Tracker. The numbers are based on the May 2021 report (which has data through April 2021): https://www.swift.com/our-solutions/compliance-and-shared-services/business-intelligence/renminbi/rmb-tracker/rmb-tracker-document-centre. According to SWIFT calculations (also reported in the SWIFT RMB Tracker), in some months the share of the euro in international payments is slightly larger (although still below that of the US dollar) when payments within the eurozone (which are presumably denominated entirely in euros) are excluded.

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The Renminbi Gains Ground, Then Stalls For a discussion of the renminbi’s rise and stall, see Prasad (2019a, 2020). Data on the renminbi’s share of global payments are from the SWIFT RMB Tracker, May 2021 (which has data through April 2021): https://www.swift.com/our-solutions/compliance-and-shared-services/business-intelligence/renminbi/rmb-tracker/document-centre. For market perceptions of the August 2015 policy shift on the exchange rate, see, for instance, Gabriel Wildau, “Renminbi Devaluation Tests China’s Commitment to Free Markets,” Financial Times, August 12, 2015, https://www.ft.com/content/65d07e26-40d0-11e5-9abe-5b335da3a90e.

Sex, Lies, and Pharmaceuticals: How Drug Companies Plan to Profit From Female Sexual Dysfunction
by Ray Moynihan and Barbara Mintzes
Published 1 Oct 2010

For many researchers, all this activity was bringing what they regarded as long-overdue recognition to women’s sexual suffering, and legitimacy to its study. For the drug companies, it was a strategic part of the planning for what was being billed as the next billion-dollar market. A forward-looking business intelligence report in 2003 named FSD drugs as an area of great future growth for the pharmaceutical industry, part of the burgeoning ‘lifestyle’ market including medicines for baldness, smoking cessation and obesity.38 The report was prepared for industry insiders and, with a hefty price tag, was never intended for public consumption.

Designing Search: UX Strategies for Ecommerce Success
by Greg Nudelman and Pabini Gabriel-Petit
Published 8 May 2011

Formerly, as UX Manager at WebEx, Pabini led UX strategy, design, and user research for online meeting and collaboration applications. She designed the award-winning Meeting Center and Training Center, setting the industry standard for online meeting software. About the Contributors Pete Bell is the co-founder of Endeca, a company with more than ten years of experience designing search & business intelligence solutions for more than 600 companies. Pete writes and speaks frequently about information science and user experience for audiences like User Interface Engineering, the Simmons Graduate School of Library and Information Science, the Dublin Core Metadata Initiative conference, and the Boston Museum of Science.

The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers
by Ben Horowitz
Published 4 Mar 2014

Without a well thought out, disciplined process for titles and promotions, your employees will become obsessed with the resulting inequities. If you structure things properly, nobody other than you will spend much time thinking about titles other than Employee of the Month. WHEN SMART PEOPLE ARE BAD EMPLOYEES In business, intelligence is always a critical element in any employee, because what we do is difficult and complex and the competitors are filled with extremely smart people. However, intelligence is not the only important quality. Being effective in a company also means working hard, being reliable, and being an excellent member of the team.

Kanban: Successful Evolutionary Change for Your Technology Business
by David J. Anderson
Published 6 Apr 2010

The business case had been stellar but its candidacy was suspect from the beginning, and some had questioned the quality of the data in the business case. After several attempts, this feature had been selected and was duly implemented. It was one of the larger features processed through the RRT system, and many people got involved and noticed it. Two months after launch, our Director of Business Intelligence did some data mining on the revenue generated. It was a fraction of what had been promised in the original business case, and the estimated payback period against the effort expended was calculated at 19 years. Due to the transparency that Kanban offered us, many stakeholders became aware of this, and there was discussion about how precious capacity had been wasted on this choice when a better choice might have been made instead.

The Four: How Amazon, Apple, Facebook, and Google Divided and Conquered the World
by Scott Galloway
Published 2 Oct 2017

Excellence, grit, and empathy are timeless attributes of successful people in every field. But as the pace and variability of work increase, success will be at the margins, separating successful people from the herd. As I described at the beginning of this book, my sixth company is L2, a business intelligence (fancy term for research) firm that has grown to 140 people in seven years. Seventy percent of our employees are under thirty; the average age is twenty-eight. L2 employees are often recruited by aspirational firms. They are kids: raw, having had little time to shape their working personalities beyond the nature and the nurture of their youth.

The Happiness Industry: How the Government and Big Business Sold Us Well-Being
by William Davies
Published 11 May 2015

Thanks to the security offered by its corporate behemoth of a patron, JWT became an international player, and its particular style of marketing expertise went global. The capacity of US businesses to export to global markets, which surged after World War Two, was greatly aided by the fact that such networks of business intelligence had already permeated much of the capitalist world. Knowledge of foreign consumers was already on hand. Following the acquisition of the GM contract, JWT set about accumulating consumer insight on an unprecedented scale. In less than eighteen months, over 44,000 interviews were done around the world, many in relation to cars, but also on topics such as food and toiletry consumption.2 This was the most ambitious project of mass psychological profiling ever attempted.

Big Data: A Revolution That Will Transform How We Live, Work, and Think
by Viktor Mayer-Schonberger and Kenneth Cukier
Published 5 Mar 2013

Besides, the amounts may be changing so quickly that a specific figure would be out of date the moment it appeared. Similarly, Google’s Gmail presents the time of recent messages with exactness, such as “11 minutes ago,” but treats longer durations with a nonchalant “2 hours ago,” as do Facebook and some others. The industry of business intelligence and analytics software was long built on promising clients “a single version of the truth”—the popular buzz words from the 2000s from the technology vendors in these fields. Executives used the phrase without irony. Some still do. By this, they mean that everyone accessing a company’s information-technology systems can tap into the same data; that the marketing team and the sales team don’t have to fight over who has the correct customer or sales numbers before the meeting even begins.

Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies
by Reid Hoffman and Chris Yeh
Published 14 Apr 2018

Twitter had to tighten its API access rules to reduce the call volume. Whatever metrics you choose, when the organization is still small, the data can generally spread via osmosis between individual employees, supplemented by a regular review during weekly company meetings. You don’t need fancy business intelligence (BI) tools or a dedicated team. Once your organization reaches the Village stage, however, osmosis won’t work. Your people are working on multiple threads, and the organization (which has exceeded Dunbar’s number) is now too big for everyone to know one another. Using a common dashboard will allow you not only to see how the threads interlock but also to coordinate the work of different groups.

How to Fix Copyright
by William Patry
Published 3 Jan 2012

In a 2009 interview, MPAA Director of Special Projects Robert Bauer was quoted as saying, “Our job is to isolate the forms of piracy that compete with legitimate sales, treat those as a proxy for unmet consumer demand, and find a way to meet that demand.”35 One way was recently suggested by Ben Karakunnel, director of business intelligence at Warner Brothers’ antipiracy unit, as reported in the paidContent. org website: In the international markets, illegal WB content in which pirates dub or subtitle themselves is increasingly popular. For one unspecified program Karakunnel used as an example, it wasn’t until the third day after its initial airdate that one such pirate-created translated version accounted for 23 percent of pirated files of that particular program.

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences
by Rob Kitchin
Published 25 Aug 2014

Similarly, with data analytics, clients usually receive the analysis, but not the underlying data. Data consolidation and re-sale, and associated data analysis and value-added services, are a multibillion-dollar industry, with vast quantities of data and derived information being rented, bought and sold daily across a variety of markets – retail, financial, health, tourism, logistics, business intelligence, real estate, private security, political polling, and so on. These data concern all facets of everyday life and include public administration, communications, consumption of goods and media, travel, leisure, crime, social media interactions, and so on. Specialist data brokers have been around for a long time, collating data from media subscriptions (e.g., newspapers, magazines), mail-order retailers, polls, surveys, travel agencies, conferences, contests, product registration and warranties, payment processing companies, government records, and so on (CIPPIC 2006).

Python for Finance
by Yuxing Yan
Published 24 Apr 2014

Rozario, Swati Kumari, Arwa Manasawala, Ruchita Bhansali, Apeksha Chitnis, and Pramila Balan as well as the external reviewers, Martin Olveyra, Mourad MOURAFIQ, and Loucas Parayiannis, for their valuable advice, suggestions, and criticism. Finally, and most importantly, I thank my wife, Xiaoning Jin, for her strong support, my daughter, Jing Yan, and son, James Yan, for their understanding and love they have showered on me over the years. About the Reviewers Jiri Pik is a finance and business intelligence consultant working with major investment banks, hedge funds, and other financial players. He has architected and delivered breakthrough trading, portfolio and risk management systems, and decision-support systems across industries. His consulting firm, WIXESYS, provides their clients with certified expertise, judgment, and execution at the speed of light.

No Ordinary Disruption: The Four Global Forces Breaking All the Trends
by Richard Dobbs and James Manyika
Published 12 May 2015

Upgrading to premium membership—monthly prices start at $59.99 per month for the Business Plus account—affords the user greater insight into who has been looking at his or her profile, the ability to send more messages to potential leads, and the use of more advanced search filters.60 A third model is monetization of big data, either through innovative business-to-business offerings (for example, crowd-sourcing business intelligence or outsourced data science services) or through developing more relevant products, services, or content for which consumers are willing to pay. LinkedIn, for example, makes 20 percent of its revenue from subscriptions, 30 percent from marketing, and 50 percent from talent solutions, a core part of which is selling targeted talent intelligence and tools to recruiters.61 You will have to keep experimenting in order to capture more consumer surplus for your business.

Running Money
by Andy Kessler
Published 4 Jun 2007

“I’ll bet Uncle Witty owned Quarter-deck, Level 3, 4-Systems, and the worst of them all, 3-Con.” “Take it easy,” I told Nick. “Some people never learn.” “Did you hear about Micro-tragedy? Could see that blowup from miles away?” It was Nick Moore again for my afternoon dose of cynicism. Microstrategy was one of those scary software blowups. They sold “business intelligence” software, one of those vague names only a McKinsey consultant could love, so companies could track business trends. The stock was hot, and growth was through the roof, until someone figured out that they were signing three-year contracts and reporting the revenues right away, a Bozo no-no of accounting.

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
by Erik Brynjolfsson and Andrew McAfee
Published 20 Jan 2014

“Computer and Dynamo: The Modern Productivity Paradox in a Not-Too-Distant Mirror,” Center for Economic Policy Research, no. 172, Stanford University, July 1989, http://www.dklevine.com/archive/refs4115.pdf. 10. For instance, Materials Resource Planning (MRP) systems, which begat Enterprise Resource Planning (ERP), and then Supply Chain Management (SCM), Customer Relationship Management (CRM), and, more recently, Business Intelligence (BI), Analytics and many other large-scale systems. 11. Todd Traub, “Wal-Mart Used Technology to Become Supply Chain Leader,” Arkansas Business, http://www.arkansasbusiness.com/article/85508/wal-mart-used-technology-to-become-supply-chain-leader (accessed July 20, 2013). 12. This is consistent with a similar analysis by Oliner and Sichel (2002), who wrote, “both the use of information technology and the efficiency gains associated with the production of information technology were central factors in [the productivity] resurgence.”

The End of College: Creating the Future of Learning and the University of Everywhere
by Kevin Carey
Published 3 Mar 2015

MicroStrategy analyzed the inventory data and told Victoria’s Secret it could make a lot more money by varying inventory accordingly. That kind of work was enough to make MicroStrategy a thriving technology company. Saylor played the venture capital game shrewdly, keeping the lion’s share of the company’s stock for himself. He also had grander visions than mere business intelligence. At MIT he had learned about the structure of scientific revolutions, how inventions like the printing press had profoundly changed not just the act of learning but whole systems of politics and philosophy. The world was on the verge of another such change, Saylor believed, and MicroStrategy software would be the key tool for making sense of the new information—not just for businesses but for every single person on earth.

Netflixed: The Epic Battle for America's Eyeballs
by Gina Keating
Published 10 Oct 2012

Hastings exacerbated the tension by baldly stating at a board meeting, in response to a director’s question about succession, and in front of a surprised Randolph and McCarthy, that he considered Kilgore his successor. Hastings invariably seemed to come down on Kilgore’s side when she demanded more money from the company’s tight budget for marketing and “business intelligence” programs. He also allowed her to consolidate marketing and public relations functions under her control, creating a fiefdom inside of Netflix that went unchallenged for more than a decade. Her loyal number two was Jessie Becker, a fellow alumnus of the University of Pennsylvania’s Wharton School and Stanford University’s business school who unquestioningly carried out Kilgore’s dictates, just as McCord carried out Hastings’s wishes.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann
Published 17 Apr 2017

• How many more bananas than usual did we sell during our latest promotion? • Which brand of baby food is most often purchased together with brand X diapers? 90 | Chapter 3: Storage and Retrieval These queries are often written by business analysts, and feed into reports that help the management of a company make better decisions (business intelligence). In order to differentiate this pattern of using databases from transaction processing, it has been called online analytic processing (OLAP) [47].iv The difference between OLTP and OLAP is not always clear-cut, but some typical characteristics are listed in Table 3-1. Table 3-1. Comparing characteristics of transaction processing versus analytic systems Property Main read pattern Transaction processing systems (OLTP) Small number of records per query, fetched by key Analytic systems (OLAP) Aggregate over large number of records Main write pattern Random-access, low-latency writes from user input Bulk import (ETL) or event stream Primarily used by End user/customer, via web application Internal analyst, for decision support What data represents Latest state of data (current point in time) History of events that happened over time Dataset size Gigabytes to terabytes Terabytes to petabytes At first, the same databases were used for both transaction processing and analytic queries.

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Specialization for different domains While the extensibility of being able to run arbitrary code is useful, there are also many common cases where standard processing patterns keep reoccurring, and so it is worth having reusable implementations of the common building blocks. Tradition‐ ally, MPP databases have served the needs of business intelligence analysts and busi‐ ness reporting, but that is just one among many domains in which batch processing is used. Another domain of increasing importance is statistical and numerical algorithms, which are needed for machine learning applications such as classification and recom‐ mendation systems.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann
Published 16 Mar 2017

How many more bananas than usual did we sell during our latest promotion? Which brand of baby food is most often purchased together with brand X diapers? These queries are often written by business analysts, and feed into reports that help the management of a company make better decisions (business intelligence). In order to differentiate this pattern of using databases from transaction processing, it has been called online analytic processing (OLAP) [47].iv The difference between OLTP and OLAP is not always clear-cut, but some typical characteristics are listed in Table 3-1. Table 3-1. Comparing characteristics of transaction processing versus analytic systems Property Transaction processing systems (OLTP) Analytic systems (OLAP) Main read pattern Small number of records per query, fetched by key Aggregate over large number of records Main write pattern Random-access, low-latency writes from user input Bulk import (ETL) or event stream Primarily used by End user/customer, via web application Internal analyst, for decision support What data represents Latest state of data (current point in time) History of events that happened over time Dataset size Gigabytes to terabytes Terabytes to petabytes At first, the same databases were used for both transaction processing and analytic queries.

…

Specialization for different domains While the extensibility of being able to run arbitrary code is useful, there are also many common cases where standard processing patterns keep reoccurring, and so it is worth having reusable implementations of the common building blocks. Traditionally, MPP databases have served the needs of business intelligence analysts and business reporting, but that is just one among many domains in which batch processing is used. Another domain of increasing importance is statistical and numerical algorithms, which are needed for machine learning applications such as classification and recommendation systems.

Consent of the Networked: The Worldwide Struggle for Internet Freedom
by Rebecca MacKinnon
Published 31 Jan 2012

For more on DPI and its origins, see Hal Abelson, Ken Ledeen, and Chris Lewis, “Just Deliver the Packets,” in Essays on Deep Packet Inspection, Office of the Privacy Commissioner of Canada, 2009, http://dpi.priv.gc.ca/index.php/essays/just-deliver-the-packets; and Ralf Bendrath, “Global Technology Trends and National Regulation: Explaining Variation in the Governance of Deep Packet Inspection,” paper presented at the International Studies Annual Convention, New York City, February 2009, International Studies Association, http://userpage.fu-berlin.de/~bendrath/Paper_Ralf-Bendrath_DPI_v1-5.pdf. 59 the Egyptian government had purchased DPI technology from a company called Narus: Timothy Karr, “One US Corporation’s Role in Egypt’s Brutal Crackdown,” Huffington Post, January 28, 2011, www.huffingtonpost.com/timothy-karr/one-us-corporations-role-_b_815281.html (accessed June 27, 2011). 59 Narus signed a multimillion-dollar deal in 2005 with Giza Systems of Egypt: Trevor Lloyd-Jones, “Narus Signs Regional Licence with Giza Systems,” Business Intelligence Middle East, September 14, 2005, www.bi-me.com/main.php?id=2047&t=1 (accessed June 27, 2011). See also p. 10 of a Giza Systems newsletter, mentioning the licensing of Narus technology to Saudi Arabia and Libya: www.gizasystems.com/admin%5CNewsLetter%5CPDF%5C8.pdf. 59 Karr’s Free Press and the Paris-based Reporters Without Borders also lodged inquiries with Cisco Systems: Timothy Karr and Clothilde Le Coz, “Corporations and the Arab Net Crackdown,” March 25, 2011, www.fpif.org/articles/corporations_and_the_arab_net_crackdown (accessed June 27, 2011). 60 a business offer for an intrusion and surveillance software product called FinFisher: Eli Lake, “British Firm Offered Spy Software to Egypt,” Washington Times, April 25, 2011, www.washingtontimes.com/news/2011/apr/25/british-firm-offered-spy-software-to-egypt; Hussein uploaded the original document online: http://moftasa.posterous.com/finfisher-intrusion-software-spy-on-email-sof.

Top Secret America: The Rise of the New American Security State
by Dana Priest and William M. Arkin
Published 5 Sep 2011

Commercial real-estate agent Lane, the building’s owner, had his engineers reinforce the steel beams to meet government specifications for security. The senior vice president of a local real estate firm has become something of a snoop himself when it comes to his NSA neighborhood. At fifty-five, he has lived and worked in its shadow all his life and has schooled himself on its growing presence in his community. He collects business intelligence. He has his own network of informants, executives like himself hoping to make a killing off an organization many of his neighbors don’t know a thing about. Lane takes note when the NSA or another secretive government organization leases another building, hires more contractors, and expands its outreach to the local business community.

The Trade of Queens
by Charles Stross
Published 16 Mar 2010

"Paulet, Paulette, Powell-et? How do you spell it, it's a first name. . . ." He read for a long time, swearing occasionally at Miriam's spidery handwriting and her copious list of contacts—She's a journalist, it's what she does—until he hit paydirt a third of the way through: Milan, Paulette. Business intelligence division, the Weatherman. That was where Miriam had worked, last time he looked. "Bingo," Mike muttered. There was a cell number and a street address out in Somerville. He made a note of it; then, systematic to the end, he went back to the cassette tape. The next message was a call from Steve Schroeder—his voice familiar—asking Miriam to get in touch.

Forty Signs of Rain
by Kim Stanley Robinson
Published 29 May 2004

Bantam Books® is a registered trademark of Random House, Inc., and the colophon is a trademark of Random House, Inc. Visit our website at www.bantamdell.com Library of Congress Cataloging in Publication Data Robinson, Kim Stanley. Forty signs of rain / Kim Stanley Robinson. p. cm. eISBN 0-553-89817-5 1. Scientists—Fiction. 2. Legislators—Fiction. 3. Washington (D.C.)—Fiction. 4. Business intelligence—Fiction. I. Title. PS3568.O2893F67 2004 2003063683 v1.0 eBook Info Title: Forty Signs of Rain Creator: Kim Stanley Robinson Publisher: Random House Contributor: None Date: 2004-06-01 Format: OEB Identifier: 0-553-89817-5 Type: Subject: Description: Language: en Rights: Copyright © 2004 by Kim Stanley Robinson

The King of Oil: The Secret Lives of Marc Rich
by Daniel Ammann
Published 12 Oct 2009

It was during this time that Azulay met Ehud Barak, who was the head of Israeli military intelligence and would later become the prime minister of Israel.7 In 1983–84, after having resigned from the Mossad, Azulay was advising a Spanish bank on how to deal with Basque terrorists when he was introduced to Marc Rich. Rich engaged his services during the time of Giuliani’s indictment against him. “He had security problems. He had business intelligence problems,” Azulay remembers and explains how he went about determining Rich’s security vulnerabilities. “It’s not magic. It’s about accurately evaluating each situation. For example, when Marc was invited to somewhere, I used to ask, ‘How did the invitation come about? Who made the invitation?

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity
by Amy Webb
Published 5 Mar 2019

The result is a first-of-its-kind film shown inside a special theatrical set, one that (along with the retinal projection system) produces a completely original—and entirely immersive— storytelling experience. AI is helping organizations of all stripes be more creative in their approach to management. The G-MAFIA powers predictive models for business intelligence, helping to find efficiencies, cost savings, and areas for improvement. Human resources departments use pattern recognition to evaluate productivity and morale—and to effectively solve for bias in hiring and promotions. We no longer use resumes; our PDRs show our strengths and weaknesses, and AI programs scan our records before recommending us to human hiring managers.

WEconomy: You Can Find Meaning, Make a Living, and Change the World
by Craig Kielburger , Holly Branson , Marc Kielburger , Sir Richard Branson and Sheryl Sandberg
Published 7 Mar 2018

Nonprofits and corporate purpose projects can use the same monitoring and evaluation (M&E) tools available to track business targets. Because each action plan is unique—with highly focused goals—I'll briefly review some general best practices. 67 percent of companies are involved in a partnership with a nonprofit or charity.4 Monitoring tools, or what you might call “business intelligence,” track your program's vitals. How were resources spent? How many participants signed up? How many team members were involved? Did participants experience the intended outcome? If these questions seem like no-brainers, you'd be surprised to learn I've encountered many companies that forget how to be well-run companies when they pick a cause.

Cataloging the World: Paul Otlet and the Birth of the Information Age
by Alex Wright
Published 6 Jun 2014

As Krajewski writes, “As soon as a box of index cards reaches a critical mass of entries and cross-references, it offers the basis for a special form of communication, a proper poetological procedure of knowledge production that leads users to unexpected results.”34 Those results—what today we might call business intelligence—offered the prospect of a powerful competitive advantage for corporations, which in turn began to see the long-term potential in applying the lessons of the library catalog to their business operations. They found a willing partner in Melvil Dewey. In 1876, Dewey established a company called the Library Bureau, whose mission was to sell to libraries and other organizations supplies such as catalog cards, drawers, “bureau boxes,” and other material to help companies implement his scheme.

Amazon: How the World’s Most Relentless Retailer Will Continue to Revolutionize Commerce
by Natalie Berg and Miya Knights
Published 28 Jan 2019

It is also a lesson in putting the needs of the customer at the heart of innovation any business could learn from. In the 2010 Amazon Annual Report, Bezos wrote: Look inside a current textbook on software architecture, and you’ll find few patterns that we don’t apply at Amazon. We use high-performance transactions systems, complex rendering and object caching, workflow and queuing systems, business intelligence and data analytics, machine learning and pattern recognition, neural networks and probabilistic decision making, and a wide variety of other techniques. And while many of our systems are based on the latest in computer science research, this often hasn’t been sufficient; our architects and engineers have had to advance research in directions that no academic had yet taken.

Legacy: Gangsters, Corruption and the London Olympics
by Michael Gillard
Published 24 Jul 2019

The jowly investigator in his mid-fifties is part of the newer breed of corporate gumshoe, the accountants whose playgrounds are the boardroom battles when clients find themselves on the wrong end of a criminal or civil prosecution. Hill was a partner at PKF, a forensic accountancy firm, where he ran the business intelligence team carrying out due diligence inquiries for clients, a fancy name for all manner of investigative techniques, some that straddle the line of legality and plausible deniability. The unwritten agreement between private investigators and their corporate clients in this new world is the same as it ever was: don’t get caught, and if you do, you’re on your own.

The Unusual Billionaires
by Saurabh Mukherjea
Published 16 Aug 2016

Sharma told me, ‘As I had a background in creating various start-ups for ICICI Bank, I knew it was best to go to an organization which had well-developed skills for each of the specialist areas.’ Sridharan built a team for retail credit policy and retail credit underwriting. He strengthened this by adding teams for analytics and business intelligence with large-scale capability for analytics and modelling. What started off in 2010 with a couple of members is now a 100-member team, doing analytics not only for retail lending but also for retail liability, corporate banking and wholesale banking. In 2012, Sharma focused on the rebranding of Axis Bank with the tag line, Badhti ka naam zindagi (life is about progress).

Augmented: Life in the Smart Lane
by Brett King
Published 5 May 2016

In about the same time that it has taken for the iPhone and smartphones to dominate every corner of society, we will see smart, autonomous cars exploding onto the scene. Business Insider has estimated that we’ll have 10 million cars with self-driving features on the road by 2020. The exponential curve of this technology means that there will be close to 100 million self-driving cars on the road just ten years after that. Figure 8.2: Business Intelligence projections on self-driving vehicles (Credit: BI) Within 15 years, we can expect that major cities and local authorities will be giving strong preferences to self-driving cars. Within 20 years, cities like London and New York won’t just have congestion charges, there will also be charges for traditional, human piloted vehicles to enter the city centres, or more probably even banning them from city streets.

Rush Hour: How 500 Million Commuters Survive the Daily Journey to Work
by Iain Gately
Published 6 Nov 2014

Although the potential for lawsuits is dizzying, car-makers nonetheless bow to what they perceive to be their customers’ wants and compete to offer them better facilities for eating and drinking during their commutes. The automobile is now an official ‘Food and beverage venue’ in the opinion of Food and Drug Packaging, and home to a majority of the 4.4 billion ‘on-the-go’ ‘eating occasions’ that took place in the USA in 2008.*4 According to Datamonitor, the business intelligence provider, eating ‘is no longer considered a primary activity’ in the sense of being an end in itself, but rather is something we do while driving or working or watching television. Food on the go has become ‘ingrained in our lives’, and fast-food manufacturers have altered their products to suit the new Zeitgeist.

SQL Hacks
by Andrew Cumming and Gordon Russell
Published 28 Nov 2006

The wizard, shown in Figure 10-2, includes options to pull the source data from an ODBC source, and that includes pretty much every SQL product available. If you want to use this with MySQL, be sure to get the MySQL ODBC driver from http://dev.mysql.com/downloads/connector/odbc. Figure 10-2. PivotTable Wizard The wizard is intimidating at first, but with a little experimentation you can use it to produce useful business intelligence. The real value to this kind of report comes when the data is updated regularly and you can compare the most recent data with corresponding historical reports. This means that you have to produce the reports regularly; that is much easier if you automate the process. 10.1.1. Include Missing Values In Figure 10-1 you might notice that the location East is missing.

Trees on Mars: Our Obsession With the Future
by Hal Niedzviecki
Published 15 Mar 2015

Now you can see what crimes have taken place, and what crimes might take place, so you are able to visualize key information spatially, allow the user to interact with information spatially, draw a circle around an area of interest, what crimes actually take place in this specific region, detailed reports on the crimes and their status.”5 The police department (PD) in Richmond, Virginia, is a pioneer in this kind of predictive policing approach. They’ve been applying the BI or business intelligence approach to policing since the early 2000s. By 2007, they were able to report, “Using predictive analysis and BI technology, we are applying information-based policy to predict the likelihood of crime and prevent future crimes from occurring. . . . Already, our officers have arrested 16 fugitives and confiscated 18 guns based on this system’s guidance.

Freedom
by Daniel Suarez
Published 17 Dec 2009

However, these young MBAs had no idea that they were really taking this meeting with him. They were bringing a problem that needed solving, even if they didn't realize it. They were the messengers. His firm would get the contract. It would be for an infrastructure security assessment or a market risk analysis, or something similar. Korr Business Intelligence Services did not advertise, and they did not submit proposals. They were the junior partners of a security consultant to the engineering department of a construction division of a real estate subsidiary of a financial group. They had no signage out front and no listing for their firm in the lobby directory.

Reset
by Ronald J. Deibert
Published 14 Aug 2020

Social media companies derive revenues by allowing third-party developers, applications, data brokers, and other services in their orbit to access their customer data, through either application programming interfaces (APIs) or other privately contracted data feeds. Analytic companies take the data harvested by the sensors of frontline companies, subject them to analysis, and then sell or trade business intelligence to advertisers and other companies. Orbiting further out are still other companies that provide the algorithms, software, techniques, and tradecraft used by the social media analytics firms. (Further out yet are those that supply the basic hardware, software, and energy required to keep it all operating.)

Understanding Sponsored Search: Core Elements of Keyword Advertising
by Jim Jansen
Published 25 Jul 2011

BTL can actually lead to a sale, and BTL promotions are highly measurable, giving marketers valuable insights into their return on investment (ROI) (Source: Wikipedia) (see Chapter BAM!). BtoB/B2B (Business-to-Business): businesses whose primary customers are other businesses (Source: IAB) (see Chapter 6 BAM!). BtoC/B2C (Business-to-Consumer): businesses whose primary customers are consumers (Source: IAB) (see Chapter 6 BAM!). Business intelligence: business practices that foster customer care, loyalty, and/or customer support. Also refers to a category of software and tools designed to gather, store, analyze, and deliver data in a user-friendly format to help organizations make more informed business decisions (Source: modified from WebTrends and IAB) (see Chapter 6 BAM!).

Beautiful security
by Andy Oram and John Viega
Published 15 Dec 2009

Participants can run benchmarks of service quality in relation to software cost, corporate vulnerabilities, and threat profiles. The potential scope is enormous, as shown through the MeMe map in Figure 9-1, taken from a security industry source. Extensible (”hackable”) and tailorable Customer service; long tails and idea viruses Decentralized connectivity Trading platform Business intelligence Business peer groups Aligned to business goals Platform that connects people, process, and technology Information security as a business discipline Useful business-oriented services Focus on the whole space (information) and not just IT security Facilitate and allow modern business models (outsourcing) Business process management Genuinely useful Cost-effective, scalable, and fair Social networking where specialists openly add value Community drives content Workflow Community determines form via demand Open architecture for connectivity Fun and serious (“Wears it all well”) “Anti-widget” Design and style matter FIGURE 9-1.

Culture & Empire: Digital Revolution
by Pieter Hintjens
Published 11 Mar 2013

According to Alex Constantine, author of "Mockingbird: The Subversion Of The Free Press By The CIA", in the 1950s, "some 3,000 salaried and contract CIA employees were eventually engaged in propaganda efforts." Officially, the program was ended in 1976 by incoming CIA Director George H. W. Bush. Inserting teams into existing media companies is one strategy. Another is to create your own business intelligence groups from the ground up. This is how large firms promote legislation, by funding "industry round tables" and "researchers" who push a pre-agreed message. The Spider has undoubtedly invested in many businesses, from armaments to drugs, and media. It's both profitable and convenient. Take as example The Economist, a respected and influential newspaper that was, ironically founded at the height of the patent debate in Britain as an anti-patent free-trade voice.

The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats
by Richard A. Clarke and Robert K. Knake
Published 15 Jul 2019

Cyber War Risk Insurance Act (CWRIA): A proposal made in this book for a Cyber War Risk Insurance Act modeled along the lines of an existing government program to backstop commercial insurance in the event of a major terrorist attack. Data Lake: A virtual repository in which current and perhaps past data is stored. The information contained within a data lake can be queried and is often useful for business intelligence or analytical purposes. Defense Advanced Research Projects Agency (DARPA): A U.S. Defense Department office that funds university and laboratory investigations and experiments into new concepts, and known, inter alia, for funding the research that led to the creation of the internet. Defense Industrial Base (DIB): Those privately owned and operated corporations that manufacture weapons and supporting systems utilized by the armed forces.

Reactive Messaging Patterns With the Actor Model: Applications and Integration in Scala and Akka
by Vaughn Vernon
Published 16 Aug 2015

Enterprise Applications The software applications needed by organizations to run their day-to-day operations are broad and varied. Depending on the kind of business, you can anticipate some of the required application software. Do any of the following application categories overlap with your enterprise? Accounting, Accounts (Financial and others), Aerospace Systems Design, Automated Trading, Banking, Budgeting, Business Intelligence, Business Process, Claims, Clinical, Collaboration, Communications, Computer-Aided Design (CAD), Content/Document Management, Customer Relationship Management, Electronic Health Record, Electronic Trading, Engineering, Enterprise Resource Planning, Finance, Healthcare Treatment, Human Resource Management, Identity and Access Management, Invoicing, Inventory, IT and Datacenter Management, Laboratory, Life Sciences, Maintenance, Manufacturing, Medical Diagnosis, Networking, Order Placement, Payroll, Pharmaceuticals, Publishing, Shipping, Project Management, Purchasing Support, Policy Management, Risk Assessment, Risk Management, Sales Forecasting, Scheduling and Appointment Management, Text Processing, Time Management, Transportation, Underwriting However incomplete the list, much of this software can be acquired as licensed commodities.

The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win
by Gene Kim , Kevin Behr and George Spafford
Published 14 Jul 2013

Chris and William also get up and follow him. I meet them halfway. “Well?” I ask. “Well,” Wes replies when he’s close enough to speak softly and be heard. “We’ve found our smoking gun. We just discovered that Brent made a change to the production database a couple of weeks ago to support a Phoenix business intelligence module. No one knew about it, let alone documented it. It conflicts with some of the Phoenix database changes, so Chris’ guys are going to need to do some recoding.” “Shit,” I say. “Wait. Which Phoenix module?” “It’s one of Sarah’s projects that we released after the project freeze was lifted,” he replies.

Catch and Kill: Lies, Spies, and a Conspiracy to Protect Predators
by Ronan Farrow
Published 14 Oct 2019

CHAPTER 46 1 a colorful profile: Miriam Shaviv, “IDF Vet Turned Author Teases UK with Mossad Alter Ego,” Times of Israel, February 8, 2013. 2 Dead Cat Bounce: Seth Freedman, Dead Cat Bounce (United Kingdom, London: Cutting Edge Press, 2013), loc 17 of 3658, Kindle. 3 who conducted the interviews, and why: Mark Townsend, “Rose McGowan: “Hollywood Blacklisted Me Because I Got Raped,” Guardian, October 14, 2017. 4 a ready pipeline of trained operatives: Adam Entous and Ronan Farrow, “Private Massod for Hire,” The New Yorker, February 11, 2019. 5 veterans of a secret Israeli intelligence unit: Haaretz staff, “Ex-Mossad Chief Ephraim Halevy Joins Spy Firm Black Cube,” Haaretz, November 11, 2018. 6 once pitched Black Cube’s services: Adam Entous and Ronan Farrow, “Private Massod for Hire,” The New Yorker, February 11, 2019. 7 more than 100 operatives, speaking 30 languages: Yuval Hirshorn, “Inside Black Cube—the ‘Mossad’ of the Business World,” Forbes Israel, June 9, 2018. 8 “the exclusive supplier to major organizations and government ministries”: Hadas Magen, “Black Cube—a “Mossad-style” Business Intelligence Co,” Globes, April 2, 2017. 9 “new information concerning the HW&BC affair”: Email from Sleeper1973, October 31, 2017. CHAPTER 47 1 That last arrangement: Agreement between Boies Schiller Flexner LLP and Black Cube, July 11, 2017. 2 two Black Cube operatives were jailed in Romania: Yuval Hirshorn, “Inside Black Cube—the ‘Mossad’ of the Business World,” Forbes Israel, June 9, 2018. 3 “it’s complicated”: Email from David Boies to Ronan Farrow, November 4, 2017. 4 “via a girl named ‘Ana’”: Email from Sleeper1973 to Ronan Farrow, November 1, 2017.

The Nature of Software Development: Keep It Simple, Make It Valuable, Build It Piece by Piece
by Ron Jeffries
Published 14 Aug 2015

The “technical” perspective may even be split into “development” and “operations.” Most of the time, these constituencies look at different measurements collected by different means. Imagine the difficulty in planning when marketing uses tracking bugs on web pages, sales uses conversions reported in a business intelligence tool, operations analyzes log files in Splunk, and development uses blind hope and intuition. Could this crew ever agree on how the system is doing? It’d be much better to integrate the information so all parties can see the same data through similar interfaces. Different constituencies require different perspectives.

Radical Technologies: The Design of Everyday Life
by Adam Greenfield
Published 29 May 2017

Even if the car hadn’t been booked on the corporate account, it is also equipped with GPS, and that unit’s accuracy buffer has been set such that it correctly identifies the location at the moment it pulls up to the curb, and tags the booking with the name of the house the executive works for. Here can be gleaned solid, actionable business intelligence: both the cycling of particular enterprises and sectors of the economy, and by extension possibly even some insight into the rhythms of something as inchoate and hard to grasp as taste. What might we learn from the American backpacker? The pedometer app on his phone is sophisticated enough to understand his pause of eleven minutes in a location in the Rue de Rivoli as a visit to the WHSmith bookstore, but other facets of his activity through the day slip through holes in the mesh.

How to Build a Billion Dollar App: Discover the Secrets of the Most Successful Entrepreneurs of Our Time
by George Berkowski
Published 3 Sep 2014

As you drive towards becoming a robust, revenue-focused company, you need to ensure that you’re making more decisions based on data; you need to take advantage of all the information that you have at hand. While your off-the-shelf analytics solutions are pretty powerful, you will invariably hit a number of limitations. There’s a good chance these limitations are going to become very annoying and will start to affect business decisions. Business intelligence tools such as QlikView give you the ability to generate insight from all the data you have collected on the back-end and pull it quickly into dashboards and reports. Think of it as one level more powerful than your daily analytics tools, and a lot more user-friendly than manipulating raw data with SQL queries (SQL is a special programing language for managing and interrogating data that is stored in relational databases) – and yes, that’s meant to sound hard.

Samsung Rising: The Inside Story of the South Korean Giant That Set Out to Beat Apple and Conquer Tech
by Geoffrey Cain
Published 15 Mar 2020

His former colleagues would repeat those words, with either favor or disdain: “Eric was an engineer.” But he also had degrees in physics and business from California’s Harvey Mudd College and Harvard Business School. Before Samsung, he’d been CTO of the Dun & Bradstreet Corporation, a firm that crunched data on people’s credit history, and CEO of Pilot Software, a business intelligence vendor. “Now, the CEO wanted me to unify the entire brand under one umbrella,” Eric said. “Samsung’s brand was scattered. Each regional office was doing its own thing. If we wanted to become a global company, we needed to direct these efforts from headquarters.” Samsung needed one giant marketing cannon with which to blast its messages out; right now, the brand sprayed its messages more like a BB gun, spewing little metallic balls one after another, without overarching purpose, direction, and vision.

The Art of SQL
by Stephane Faroult and Peter Robson
Published 2 Mar 2006

TFP_MNE" ELSE NULL END ) || CASE WHEN 'Y' = 'Y' THEN TO_CHAR ( TRUNC ( t2."ACC_PCI" ) ) ELSE NULL END ) || CASE WHEN 'N' = 'Y' THEN t2."ACC_E2K" ELSE NULL END ) || CASE WHEN 'N' = 'Y' THEN t2."ACC_EXT" ELSE NULL END ) || CASE ... It seems obvious from this sample's select list that at least some "business intelligence" tools invest so much intelligence on the business side that they have nothing left for generating SQL queries. And when the where clause ceases to be trivial—forget about it! Declaring that it is better to avoid joins for performance reasons is quite sensible in this context. Actually, the nearer you are to the "text search in a file" (a.k.a. grep) model, the better.

Designing Interfaces
by Jenifer Tidwell
Published 15 Dec 2010

Fitbit What Arrange data displays into a single information-dense page, updated regularly. Show users relevant, actionable information, and let them customize the display as necessary. Use when Your site or application deals with an incoming flow of information from something—web server data, social chatter, news, airline flights, business intelligence information, or financials, for example. Your users would benefit from continuous monitoring of that information. Why This is a familiar and recognizable page style. Dashboards have a long history, both online and in the physical world, and people have well-established expectations about how they work: they show useful information, they update themselves, they usually use graphics to display data, and so on.

Rise of the Machines: A Cybernetic History
by Thomas Rid
Published 27 Jun 2016

BlackNet was specifically interested in buying trade secrets (“semiconductors”); production methods in nanotechnology (“the Merkle sleeve bearing”); chemical manufacturing (“fullerines and protein folding”); and design plans for things ranging from children’s toys to cruise missiles (“3DO”). Oh, and BlackNet was also interested in general business intelligence—for instance, on mergers and buyouts. “Join us in this revolutionary—and profitable—venture,” the message concluded.62 May soon claimed credit for the letter. He had come up with BlackNet in the summer of 1993, as an example of what could be done, “an exercise in guerrilla ontology,” as he called it.63 The goal was to find a way to ensure fully anonymous, untraceable, two-way exchanges of information.

Gnuplot in Action: Understanding Data With Graphs
by Philipp Janert
Published 2 Jan 2010

I first started using gnuplot when I was a graduate student, and it has become an indispensable part of my toolbox: one of the handful of programs I can’t do without. Recently, I’ve also started to contribute a few features to the gnuplot development version. I provide consulting services specializing in corporate metrics, business intelligence, data analysis, and mathematical modeling through my company, Principal Value, LLC (www.principal-value.com). I also teach classes on software design and data analysis at the University of Washington. I hold a Ph.D. in theoretical physics from the University of Washington. Author online Purchase of Gnuplot in Action includes free access to a private web forum run by Manning Publications where you can make comments about the book, ask technical questions, and receive help from the author and from other users.

Lean Analytics: Use Data to Build a Better Startup Faster
by Alistair Croll and Benjamin Yoskovitz
Published 1 Mar 2013

When everyone rallies around the OMTM and is given the opportunity to experiment independently to improve it, it’s a powerful force. Solare Focuses on a Few Key Metrics Solare Ristorante is an Italian restaurant in San Diego owned by serial entrepreneur Randy Smerik. Randy has a background in technology and data, once served as the general manager for business intelligence firm Teradata, and has five technology exits under his belt. It’s no surprise that he’s brought his data-driven mindset to the way he runs the business. One evening at the restaurant, Randy’s son Tommy—who manages the bar—yelled out, “24!” Since we’re always looking for stories about business metrics, we asked him what the number meant.

Super Continent: The Logic of Eurasian Integration
by Kent E. Calder
Published 28 Apr 2019

Society for Worldwide Interbank Financial Telecommunication (SWIFT), “RMB Tracker November 2011,” November 25, 2011; “RMB Tracker January 2014,” Janu- Notes to Chapter 10 303 ary 23, 2014; “RMB Tracker January 2015,” January 28, 2015, https://​www​.swift​.com/ ​our​-solutions/​compliance​-and​-shared​-services/​business​-intelligence/​renminbi /​r mb​ -tracker. 45. The October 2016 weights for the five basket currencies were US dollar (41.73 percent); euro (30.93 percent); Chinese RMB (10.92 percent); Japanese yen (8.33 percent); and British pound sterling (8.09 percent). See International Monetary Fund, “IMF Adds Chinese Renminbi to Special Drawing Rights Basket,” IMF News, September 30, 2016, http://​www​.imf​.org/​en/​News/​Articles/​2016/​09/​29/​AM16​-NA093016IMF​-Adds​ -Chinese​-Renminbi​-to​-Special​-Drawing​-Rights​-Basket. 46.

Masters of Management: How the Business Gurus and Their Ideas Have Changed the World—for Better and for Worse
by Adrian Wooldridge
Published 29 Nov 2011

As Clive Crook, a former colleague of mine at The Economist, has put it, “CSR is the tribute that capitalism everywhere pays to virtue.”6 This tribute to virtue is paid in gold as well as hot air. No major consultancy is complete without a CSR practice. Some consultancies sell nothing but CSR: a group called the Ethical Corporation provides “business intelligence” on CSR to more than three thousand multinational companies, publishes a CSR-themed magazine and website, puts on a huge conference every year, and compiles an ever-expanding library of case studies on corporate irresponsibility, including studies of Exxon Valdez, Toyota, and McDonald’s.7 There are CSR performance indexes (such as the Dow Jones Sustainability Index); CSR professorships (more than half of U.S.

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
by Valliappa Lakshmanan , Sara Robinson and Michael Munn
Published 31 Oct 2020

Unlike the other roles discussed here, research scientists spend most of their time prototyping and evaluating new approaches to ML, rather than building out production ML systems. Data analysts evaluate and gather insights from data, then summarize these insights for other teams within their organization. They tend to work in SQL and spreadsheets, and use business intelligence tools to create data visualizations to share their findings. Data analysts work closely with product teams to understand how their insights can help address business problems and create value. While data analysts focus on identifying trends in existing data and deriving insights from it, data scientists are concerned with using that data to generate future predictions and in automating or scaling out the generation of insights.

Seven Crashes: The Economic Crises That Shaped Globalization
by Harold James
Published 15 Jan 2023

And nothing can be of the least avail which does not come from concerted action at the centre. We have a poignant example today of the helplessness of the individual, however powerful and however great his genius, in the tragic death of Mr Ivar Kreuger. Here was a man of perhaps the greatest constructive business intelligence of his age, a man whose far-flung activities have been in the widest sense in the public interest, who had conceived it his mission in the chaos of the post-war world to furnish a channel between the countries where resources were in surplus and those where they were desperately required, one who built on solid foundations and surrounded himself with such safeguards as could be humanly devised in the circumstances,—suffering what the ignorant might mistake for the fate of the common gambler, but in truth crushed between the ice-bergs of a frozen world which no individual man could thaw and restore to the warmth of normal life.

Connectography: Mapping the Future of Global Civilization
by Parag Khanna
Published 18 Apr 2016

ESRI MAPPING CENTER http://mappingcenter.​esri.​com/​index.​cfm?​fa=​resources.​cartoFavorites Esri’s Mapping Center provides access to various resources that are used regularly by professional mapmakers and cartographers, enabling its users to create maps using ArcGIS. FIRST MILE GEO https://www.​firstmilegeo.​com/ First Mile Geo is a business intelligence software that enables users to collect, visualize, and monitor data collected online or off-line through mobile, SMS, surveys, or manual sources. Maps, dashboards, indices, and alerts can be generated in multiple languages. FLEETMON http://www.​fleetmon.​com/​live_tracking/​fleetmon_explorer FleetMon is an open database of ships and ports worldwide that uses real-time AIS positioning data to visualize the location and movement of nearly 500,000 vessels, allowing for the analysis of shipping and trade patterns.

After Steve: How Apple Became a Trillion-Dollar Company and Lost Its Soul
by Tripp Mickle
Published 2 May 2022

As much as anything else, he saw a troubled business in need of a fix, a challenge that he’d have the authority to address. So he took the job based in Denver, his first home outside the Southeast. He and Coffey, who joined around the same time, went to work identifying cost reductions to turn around a distressed business. Intelligent Electronics had more warehouses than it needed and different software systems for customer orders. It had grown fast by acquiring rivals but had not integrated those businesses well. At the time, some customers wanted Intelligent Electronics to fulfill a single order with networking equipment from Cisco and computers from IBM, but the equipment was often spread across four warehouses in Denver.

Essential Scrum: A Practical Guide to the Most Popular Agile Process
by Kenneth S. Rubin
Published 19 Jul 2012

The extent of that responsibility would depend on the organization, the specific product, and the skills of the person selected to be the product owner. For example, an organization that is producing a simple unit-conversion application for sale in a mobile device app store will not require as many activities as an organization creating the next major release of its enterprise Business Intelligence product. Therefore, it isn’t practical to universally define the extent of the product owner’s responsibility relative to the Pragmatic Marketing framework. As I will discuss shortly, there are times when the scope of the product owner activities may be too large for any one person to adequately perform.

Natural language processing with Python
by Steven Bird , Ewan Klein and Edward Loper
Published 15 Dec 2009

Rather than trying to use text like (1) to answer the question directly, we first convert the unstructured data of natural language sentences into the structured data of Table 7-1. Then we reap the benefits of powerful query tools such as SQL. This method of getting meaning from text is called Information Extraction. Information Extraction has many applications, including business intelligence, resume harvesting, media analysis, sentiment detection, patent search, and email scanning. A particularly important area of current research involves the attempt to extract 262 | Chapter 7: Extracting Information from Text structured data out of electronically available scientific literature, especially in the domain of biology and medicine.

Addiction by Design: Machine Gambling in Las Vegas
by Natasha Dow Schüll
Published 15 Jan 2012

Graphical data visualization system for casinos (by Mariposa, partnered with IGT). Gamblers are mapped as icons on a casino floor; casinofloor managers can click onthe icons to access their corresponding player preference profiles. Images accessed from Mariposa website, June 2007. A company called Compudigm, now partnered with Bally, created a business intelligence tool called seePOWER that specializes in analyzing data from multiple gamblers to reveal group “tendencies and preferences,” as one press release put it. The technology works by transforming massive amounts of player tracking information into colorful heat maps that represent the collective behavior of patrons in and over time (see fig. 5.3).

Beautiful Architecture: Leading Thinkers Reveal the Hidden Beauty in Software Design
by Diomidis Spinellis and Georgios Gousios
Published 30 Dec 2008

* * * [23] http://restlet.org Data-Driven Applications Once an organization has gone to the trouble of making its data addressable, there are additional benefits beyond enabling the backend systems to cache results and migrate to new technologies in unobtrusive ways. Specifically, we can introduce entirely new classes of data-driven applications and integration strategies. When we can name our data and ask for it in application-friendly ways, we facilitate a level of exploration, business intelligence, and knowledge management that will make most analysts drool when they see it. The Simile Project,[24] a joint effort between the W3C and the MIT CSAIL group, has produced a tremendous body of work demonstrating these ideas and how much drool can actually be produced. Consider the scenario of tracking the efficacy of various marketing strategies on website traffic and sales.

The Billionaire's Apprentice: The Rise of the Indian-American Elite and the Fall of the Galleon Hedge Fund
by Anita Raghavan
Published 4 Jun 2013

Kumar was comfortable transgressing the law, but he was far less at ease with the idea of crossing social norms. His pushback came at a trying time in his relationship with Rajaratnam. Ever since his big tip in 2006 on the AMD–ATI Technologies talks, Kumar’s hot streak had gone stone cold. In 2007, he told Rajaratnam that the industry of business intelligence, in which companies use software to mine mountains of information stored in widely available databases, was ripe for consolidation. The tip piqued Rajaratnam’s curiosity. “How do you know?” he asked. Kumar explained that he was providing consulting services to a company called Business Objects, a French-American firm that made intelligence software.

Addiction by Design: Machine Gambling in Las Vegas
by Natasha Dow Schüll
Published 19 Aug 2012

Graphical data visualization system for casinos (by Mariposa, partnered with IGT). Gamblers are mapped as icons on a casino floor; casinofloor managers can click onthe icons to access their corresponding player preference profiles. Images accessed from Mariposa website, June 2007. A company called Compudigm, now partnered with Bally, created a business intelligence tool called seePOWER that specializes in analyzing data from multiple gamblers to reveal group “tendencies and preferences,” as one press release put it. The technology works by transforming massive amounts of player tracking information into colorful heat maps that represent the collective behavior of patrons in and over time (see fig. 5.3).

Appetite for America: Fred Harvey and the Business of Civilizing the Wild West--One Meal at a Time
by Stephen Fried
Published 23 Mar 2010

A former president of the American Bankers Association and adviser to Presidents Taft and Hoover, Swinney had put together the deal for Kansas City Union Station, and he still sat on the boards of several major companies. The aging banker had been a friend and close colleague of Ford Harvey—who sat on his board and worked with him on the rebuilding of Kansas City’s streetcar system. He had also known Byron for years. So, when E. F. Swinney said that he held Kitty Harvey’s business intelligence in high esteem—and that, in fact, he thought she had a better business brain than her uncle—people listened. Of course, Swinney had his own civic reasons for keeping Fred Harvey in Kansas City. But he also had spent a lot of time with Kitty as she and Freddie worked at investing the money from their father’s estate.

Site Reliability Engineering: How Google Runs Production Systems
by Betsy Beyer , Chris Jones , Jennifer Petoff and Niall Richard Murphy
Published 15 Apr 2016

Golab et al., “Eventually Consistent: Not What You Were Expecting?”, in ACM Queue, vol. 12, no. 1, 2014. [Gra09] P. Graham, “Maker’s Schedule, Manager’s Schedule”, blog post, July 2009. [Gup15] A. Gupta and J. Shute, “High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads”, in Workshop on Business Intelligence for the Real Time Enterprise, 2015. [Ham07] J. Hamilton, “On Designing and Deploying Internet-Scale Services”, in Proceedings of the 21st Large Installation System Administration Conference, November 2007. [Han94] S. Hanks, T. Li, D. Farinacci, and P. Traina, “Generic Routing Encapsulation over IPv4 networks”, IETF Informational RFC, 1994.

Architects of Intelligence
by Martin Ford
Published 16 Nov 2018

That demonstration gave them the ability to position themselves well, both internally and externally. With regard to the businesses, I think IBM is in a unique place regarding the way they can capitalize on this kind of AI. It’s very different than the consumer space. IBM can approach the market broadly through business intelligence, data analytics, and optimization. And they can deliver targeted value, for example in healthcare applications. It’s tough to measure how successful they’ve been because it depends on what you count as AI and where you are in the business strategy. We will see how it plays out. As far as the consumer mindshare these days it seems to me like Siri and Amazon’s Alexa are in the limelight.

Piracy : The Intellectual Property Wars from Gutenberg to Gates
by Adrian Johns
Published 5 Jan 2010

They took this initiative at the very moment when private detective agencies such as Pinkerton’s were coming into their own in America and Britain as entrepreneurial counterparts (and sometimes more) to the professionalizing police forces. Like them, Preston and Abbott had hit upon an opportunity that was not to go away. Their initiative marked the beginning of an alliance between business, intelligence, policing, and intellectual property that would endure long after their victory. Today, private antipiracy policing is a growth industry. It recruits expolicemen, as Preston and Abbott did, and it too has been known to pursue its quarry not just as pirates but as criminal conspirators. The modern pirate hunters propel policy and legislation too.

Facebook: The Inside Story
by Steven Levy
Published 25 Feb 2020

“We hope to play a critical role in reaching one of Internet.org’s most significant goals—using data more efficiently, so that more people around the world can connect and share,” wrote Rosen. But Facebook’s motivation wasn’t really providing an app to improve phone performance in developing countries. It maintained Onavo’s business model, which was gathering data from deceptively “free” apps to inform its money-making business intelligence operations. When the mobile performance tool no longer served its purpose, Facebook created a different honey trap for user data, Onavo Protect, which delivered what seemed like a bargain: a free “Virtual Private Network” (VPN) that provided more security than public Wi-Fi networks. It takes a certain amount of chutzpah to present people with a privacy tool whose purpose was to gain their data.

Hadoop: The Definitive Guide
by Tom White
Published 29 May 2009

Today, Hive is a successful Apache project used by many organizations as a general-purpose, scalable data processing platform. Of course, SQL isn’t ideal for every big data problem—it’s not a good fit for building complex machine learning algorithms, for example—but it’s great for many analyses, and it has the huge advantage of being very well known in the industry. What’s more, SQL is the lingua franca in business intelligence tools (ODBC is a common bridge, for example), so Hive is well placed to integrate with these products. This chapter is an introduction to using Hive. It assumes that you have working knowledge of SQL and general database architecture; as we go through Hive’s features, we’ll often compare them to the equivalent in a traditional RDBMS.

Solr in Action
by Trey Grainger and Timothy Potter
Published 14 Sep 2014

Solr in Action, written by longtime Solr power users and community members, Trey and Timothy, covers these important recent Solr features and provides an excellent starting point for those new to Solr. Solr is now used in more places than I could ever have imagined—from integrated library systems to e-commerce platforms, analytics and business intelligence products, content-management systems, internet searches, and more. It’s been rewarding to see Solr grow from a few early adopters to a huge global community of helpful users and active volunteers cooperatively pushing development forward. Solr in Action gives you the knowledge and techniques you need to use Solr’s features that have been under development since 2004.

Empire of Guns
by Priya Satia
Published 10 Apr 2018

David Jr.’s second wife was the sister of Charles Lloyd, who married James Farmer’s daughter by Priscilla Plumsted, granddaughter of John Freame. The network of family connections is truly indescribable. The point is that this network married finance to industry—and allowed for exchange of business intelligence between them. Joseph Freame shared his worries about the bank after the Seven Years’ War with Farmer’s daughter “Polly” (Mary), who socialized with the Barclays and the Plumsteds. Agatha Barclay, who became a Gurney in 1773, wrote frequently to Polly, who became a Lloyd in 1774, their correspondence often referring to their parents’ exchanges and meetings.

The Singularity Is Near: When Humans Transcend Biology
by Ray Kurzweil
Published 14 Jul 2005

In our reporting on the KurzweilAI.net Web site, we feature one or more dramatic systems almost every day.180 A 2003 study by Business Communications Company projected a $21 billion market by 2007 for AI applications, with average annual growth of 12.2 percent from 2002 to 2007.181 Leading industries for AI applications include business intelligence, customer relations, finance, defense and domestic security, and education. Here is a small sample of narrow AI in action. Military and Intelligence. The U.S. military has been an avid user of AI systems. Pattern-recognition software systems guide autonomous weapons such as cruise missiles, which can fly thousands of miles to find a specific building or even a specific window.182 Although the relevant details of the terrain that the missile flies over are programmed ahead of time, variations in weather, ground cover, and other factors require a flexible level of real-time image recognition.

Masterminds of Programming: Conversations With the Creators of Major Programming Languages
by Federico Biancuzzi and Shane Warden
Published 21 Mar 2009

SQL manages persistent data, which has a long lifetime. Enterprises that have an investment in SQL databases are inclined to build on that investment rather than starting over with a different approach. SQL is robust enough to solve real problems. It spans a broad spectrum of usage from business intelligence to transaction processing. It is supported on many platforms and in many processing environments. Despite some criticism about its lack of elegance, SQL has been used successfully by many organizations to develop critical, real-world applications. I believe that this success reflects the origin of the language in an experimental prototype that was responsive to the needs of real users from the earliest days.

The Art of Scalability: Scalable Web Architecture, Processes, and Organizations for the Modern Enterprise
by Martin L. Abbott and Michael T. Fisher
Published 1 Dec 2009

We guarantee that scalability was on the mind of every bank during the consolidation that occurred after the collapse of the banking industry. The models and approaches that we present in our book are industry agnostic. They have been developed, tested, and proven successful in some of the fastest growing companies of our time and they work not only in front-end customer facing transaction processing systems but back-end business intelligence, enterprise resource planning, and customer relationship management systems. They don’t discriminate by activity, but rather help to guide the thought process on how to separate systems, organizations, and processes to meet the objective of becoming highly scalable and reaching a level of scale that allows your business to operate without concerns regarding customer or end-user demand.

The Art of SEO
by Eric Enge , Stephan Spencer , Jessie Stricchiola and Rand Fishkin
Published 7 Mar 2012

The list of situations where the brand can limit the strategy is quite long, and the opposite can happen too, where the nature of the brand makes a particular SEO strategy pretty compelling. Ultimately, your goal is to dovetail SEO efforts with branding as seamlessly as possible. Competition Your SEO strategy can also be influenced by your competitors’ strategies, so understanding what they are doing is a critical part of the process for both SEO and business intelligence objectives. There are several scenarios you might encounter: The competitor discovers a unique, highly converting set of keywords. The competitor discovers a targeted, high-value link. The competitor saturates a market segment, justifying your focus elsewhere. Weaknesses appear in the competitor’s strategy, which provide opportunities for exploitation.

Backup & Recovery
by W. Curtis Preston
Published 9 Feb 2009

SQL Server 2005 also offers a wealth of additional features such as services for analysis, data integration, notification, and reporting as well as the service broker. Combined, these elements make up a complete relational database system that can be used for simple tasks such as a database-driven web application or for more advanced needs such as data mining, complex business intelligence gathering, specialized reporting and notification, and a host of additional needs. Tip When referring to specific versions, this chapter often uses just the version numbers. For example, 2000 refers to SQL Server 2000, and 2005 refers to SQL Server 2005. When referring to both SQL Server 2000 and 2005, I use the term SQL Server.

The First American: The Life and Times of Benjamin Franklin
by H. W. Brands
Published 1 Jan 2000

Besides the benefit of finishing the job on schedule, Franklin appreciated the positive impression he was making on the sober and hardworking Quakers. “This industry visible to our neighbors began to give us character and credit,” he remembered. Many of the merchants, who gathered for refreshment and the exchange of business intelligence at the Every-Night Club, wondered at Franklin and Meredith’s boldness in beginning their business when Philadelphia already had two printers and was hardly clamoring for a third. Those without personal knowledge of Franklin asserted that the new enterprise must surely fail. Yet individuals who observed Franklin at work argued a contrary view.