Successful Business Intelligence Second Edition Unlock the Value of BI & Big Data About the Author Cindi Howson is the founder of BI Scorecard (www.biscorecard.com), a resource for in-depth BI product reviews, and has more than 20 years of BI and management reporting experience She advises clients on BI strategy, best practices, and tool selections; writes and blogs for Information Week; and is an instructor for The Data Warehousing Institute (TDWI) Prior to founding BI Scorecard, Cindi was a manager at Deloitte & Touche and a BI standards leader for a Fortune 500 company She has an MBA from Rice University Contact Cindi at cindihowson@biscorecard.com About the Technical Editor Mark Hammond is a technology writer working in the IT field since 1998 with a focus on business intelligence and data integration An award-winning journalist, Hammond serves as a contributing analyst to The Data Warehousing Institute and provides services to leading enterprise software companies He can be reached at mfhammond@ comcast.net Successful Business Intelligence Second Edition Unlock the Value of BI & Big Data Cindi Howson New York Chicago San Francisco Athens London Madrid Mexico City Milan New Delhi Singapore Sydney Toronto Copyright © 2014 by McGraw-Hill Education All rights reserved Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher, with the exception that the program listings may be entered, stored, and executed in a computer system, but they may not be reproduced for publication ISBN: 978-0-07-180919-1 MHID: 0-07-180919-8 The material in this eBook also appears in the print version of this title: ISBN: 978-0-07-180918-4, MHID: 0-07-180918-X E-book conversion by codeMantra Version 1.0 All trademarks are trademarks of their respective owners Rather than put a trademark 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in contract, tort or otherwise For Keith This page intentionally left blank Contents Preface xi Acknowledgments xv BI and Big Data from the Business Side Business Intelligence by Other Names How Business Intelligence Provides Value The Business Intelligence Market Battle Scars The Research Best Practices for Successful Business Intelligence 11 18 19 24 Technobabble: Components of a Business Intelligence Architecture 27 Operational and Source Systems Data Transfer: From Operational to Data Warehouse The Data Warehouse Data Warehouse Tables The Data Warehouse Technology Platform Big Data Technologies Best Practices for Successful Business Intelligence 28 31 34 37 40 45 49 The Business Intelligence Front-End: More Than a Pretty Face 51 Utopia: Self-Service BI 52 Business Query and Reporting 53 Visual Data Discovery 56 Dashboards 60 Scorecards 62 Production Reporting 63 Mobile BI 64 Online Analytical Processing (OLAP) 65 Microsoft Office 68 Performance Management 69 Analytic Applications 71 Advanced and Predictive Analytics 72 vii viii Contents Big Data Analytics Best Practices for Successful Business Intelligence Measures of Success 73 73 75 Success and Business Impact How to Measure Success Measures of Success at Netflix Measures of Success at Learning Circle Measures of Success at Constant Contact Return on Investment Anecdotes of Hard Business Benefits Number of Users Other Measures of Success Best Practices for Successful Business Intelligence 75 79 80 82 83 85 87 87 92 93 Catalysts for Success: The LOFT Effect 95 The Role of Luck 95 Opportunity 100 Frustration 109 Threat 113 The Role of Time 116 If There Is No LOFT Effect, Is Successful BI Still Possible? 117 Best Practices for Successful Business Intelligence 119 Executive Support and Culture 121 Executive Support and Success 121 Which Executive Is the Best Sponsor? 123 The Role of an Executive Sponsor 126 Getting and Keeping Executive Buy-In 128 Culture 131 Best Practices for Successful Business Intelligence 138 The Business–IT Partnership 139 Voices of Frustration and Hope 139 The Business–IT Yin-Yang 141 Meet the Hybrid Business–IT Person 144 How to Be a Better Partner 146 Partnership and BI Success 149 Partnership at Netflix 150 Alignment 152 Best Practices for Successful Business Intelligence 155 D Is for Data 157 Data Breadth Data Quality 158 159 Contents ix Successful Data Architectures Master Data Management Right-Time Data Data Quality’s Chicken and Egg Best Practices for Successful Business Intelligence 169 171 175 178 180 Relevance 181 Relevance Brings Clearer Vision 182 Relevance Improves Patient Care 183 Relevance for Teachers 188 The Role of Incentives 189 Personalization 190 Requirements-Driven BI 191 What to Do with Big Data 191 Best Practices for Successful Business Intelligence 193 10 Agile Development Waterfall Development Process Agile Development Techniques Basic Concepts of Scrum Agile Culture at Netflix Medtronic: Agile for the Right Projects Sharper BI at 1-800 CONTACTS Best Practices for Successful Business Intelligence 11 Organizing for Success Enterprise vs Departmental BI The BI Steering Committee Business Intelligence Competency Centers (BICC) The Best People BI Team Leaders as Level Leaders Best Practices for Successful Business Intelligence 12 The Right BI Tool for the Right User The Importance of BI Tools The Role of BI Standardization The Right Tool for the Right User Characteristics for Defining User Segments The Most Successful BI Module Best Practices for Successful Business Intelligence 13 Other Secrets to Success 195 195 201 204 210 211 213 213 215 215 223 224 229 230 231 233 236 237 243 244 252 255 257 Innovation 257 Evangelizing and Promoting Your BI Capabilities 261 Training 268 306 Notes Robb, Drew, “EMA: Measuring the Emergency Room’s Pulse,” Computerworld, September 18, 2006 Learning Circle, user interview, April 2013 10 Levitt, Steven, and Dubner, Stephen, Freakonomics: A Rogue Economist Explores the Hidden Side of Everything, HarperCollins Publishers, 2005, p 11 Welsh, Patrick, “Don’t Judge Teachers’ Test Scores,” USA Today, June 5, 2013 12 Raden, Neil, “Toppling the BI Pyramid,” DM Review, January 2007 13 Netflix company website, Long-Term View, April 25, 2013 http:// ir.netflix.com/long-term-view.cfm 14 Kaye, Kate, “At Starbucks, Data Pours In But What to Do With It?” Ad Age, March 22, 2013 15 Constant Contact, interview notes, July 2013 16 Lundin, Barbara, “UK Smart Meter Plan Could Be Too Ambitious,” FierceEnergy, July 3, 2013 17 http://stopsmartmeters.org.uk/ Chapter 10 Heldman, Kim, Project Management Professional Study Guide, Sybex: 2013 http://www.agilemanifesto.org/history.html http://www.ambysoft.com/surveys/agileMarch2007.html Ambler, Scott, “Results from Scott Ambler’s July 2010 State of the IT Union Survey, ” posted at www.agilemodeling.com/surveys/ Schwaber, Ken and Sutherland, Jeff, The Scrum Guide, October 2011 Hughes, Ralph, Agile Data Warehousing, iUniverse, 2008 Hughes, Ralph, interview notes, June 2013 http://www.infoq.com/articles/hiranabe-lean-agile-kanban http://agile.techwell.com/articles/weekly/what-best-scrum-or-kanban 10 http://en.wikipedia.org/wiki/Kanban_(development) 11 Watson, Hugh, “Are Data Warehouses Prone to Failure?” TDWI Journal, Fall 2005, 454 respondents 12 Netflix presentation on culture, http://jobs.netflix.com/jobs.html 13 http://www.evancarmichael.com/Famous-Entrepreneurs/5338/ReedHastings-Quotes.html 14 Netflix press release, October 10, 2011 15 Netflix, Interview notes, February 2013 16 Tseitlin, Ariel, et al, “Announcing Ice Cloud Spend and Usage Analytics,” Netflix Technical Blog, June 2013 17 Medtronic, interview notes, May 2013 18 1-800 CONTACTS, interview notes, May 2007 Notes 307 Chapter 11 www.modelt.org/tquotes.html Wikipedia Eckerson, Wayne, and Howson, Cindi, “Enterprise Business Intelligence,” TDWI Report Series, August 2005 1-800 CONTACTS, interview notes and e-mails, April 2007 and September 2007 Miller et al., Business Intelligence Competency Centers, Wiley, 2006 Netflix, interview notes, February 2013 Rudin, Ken, Facebook, TDWI Keynote, May 2013 www.ni.com/case-studies National Instruments, interview notes, April 2012 10 Cognos Analyst Summit, customer presentation, September 2007 11 Kanter, Rosabeth, e-volve! Succeeding in the Digital Culture of Tomorrow, Harvard Business School Press, 2001, p 205 12 Collins, Jim, Good to Great, p 21 13 1-800 CONTACTS, interview notes, April 2007 Chapter 12 Microsoft User Conference, May 2007 Taylor, James, and Raden, Neil, Smart Enough Systems, Prentice Hall: 2007, p 15 Jim Jelter, “Kodak Restates, Adds $9 Million to Loss,” MarketWatch, Nov 9, 2005 “RedEnvelope Cuts Outlook, Shares Fall, CFO Eric Wong Resigns Amid Budget Errors,” Associated Press, March 29, 2005 Schenker, Lisa, “Utah Education Officials Make $25m School Funding Mistake,” The Salt Lake Tribune, April 11, 2012 Coy, Peter, “FAQ: Reinhart, Rogoff, and the Excel Error That Changed History,” Bloomberg BusinessWeek, April 18, 2013 Chapter 13 King, Julia, “No Place to Work in IT: USAA,” Computerworld, June 21, 2010 Burks, Ricky, and Thomas, Charles, “USAA: The Analytics Advantage: Converting Big Data to Business Growth,” Computerworld presentation, September 2012 Kaplan, Rachel, “CIO Michael Hedges: Medtronic,” Minneapolis St Paul Business Journal, September 2011 Boicey, Charles, interview notes, June 2013 308 Notes McKinley, Scott, “Nielsen,” IBM Business Analytics presentation, June 2013 Innovate1st.com, “Conversations on the Cutting Edge, Interview with Scott McKinley,” February 2013 Emergency Medical Associates, interview notes, April 2007 Chapter 14 Byrne, Tony, CMSWatch, interview notes, August 2007 BlueCross BlueShield of Tennessee, interview notes, April 4, 2007 BlueCross BlueShield BCBS of Tennessee, 2006 Annual Report Morris, Henry, “Bridging the Structured/Unstructured Data Gap at BCBS of TN,” IDC Opinion, December 2006 BlueCross BlueShield of Tennessee, interview notes, April 4, 2007 Medtronic, interview notes, May 2013 Winter, Richard, Gilbert, Rick, and Davis, Judith, R “Big Data: What Does It Really Cost? A WinterCorp Special Report,” August 2013 Isson, Jean-Paul, and Harris, Jesse, Win with Advanced Business Analytics, Wiley & SAS Press, 2013, p 88 Index 1-800 CONTACTS agile development and, 213 call center dashboard, 246 data warehouses, 175 frustration and, 110–111 overview, 20, 110 role of relevance, 182–183 A Ab Initio, 239 accounting systems, 28 acronyms, 147 ad hoc queries, 53, 263 ad hoc query tools, 53 ad hoc reports, 53, 54, 92 advanced analytics, 72–73 Affordable Care Act, 113 aggregate data, 41 aggregate tables, 37, 263 aggregation, 66 agile development, 195–214 1-800 CONTACTS, 213 best practices, 213 BI and, 203–204 considerations, 195, 204 Kanban development, 206 Medtronic, 211–212 need for, 204 Netflix, 210–211 project management and, 207–210 scrum concepts, 204–206 state of, 203–204 techniques, 201–203 waterfall process, 195–201, 210 Agile Manifesto, 201, 202 air travel data, 174–175 alignment, 152–155 Amazon, 150–152 Amazon Storage Center (S3), 44 Amazon Web Services, 44 American Airlines, 100, 101 analysis, analysis latency, 176 analytic appliances, 40–43 analytic applications, 71–72 analytic ecosystems, 48–49, 50 analytic job content, 248–249 analytics advanced, 72–73 big data, 73 business, 1, 2, complex, 226 predictive, 72–73 text, 281–284 appliances, 40–43 AsterData, 16 Austin, Texas fire department, 87 awareness, 261–262 B Bachenheimer, Eric, 161, 229, 268 Balanced Scorecard Collaborative, 62 bar charts, 271–272 Baseball Prospectus, 10 BCBS (BlueCross BlueShield), 283–284 Beane, Billy, best practices agile development, 213–214 BI components, 49–50 BI front-end, 73–74 BI tools, 254–255 data architecture, 180 evaluating measures of success, 93 executive support, 138 309 310 Index best practices (cont.) LOFT effect, 119 organizational issues, 231–232 partnerships, 155–156 relevance, 193 secrets to success, 273–274 sharing, 231 for successful BI, 24–25 BI (business intelligence) agile development and, 203–204 altruistic solutions, 8–9 benefits of, 263–264 best practices See best practices case studies, 20–23, 240, 268–270 challenges, 18–19 cloud-based, 16, 43–45 collaboration in, 284–285 components, 27–50 considerations, 4, 13 control, 4–5 customer service, defined, 1, departmental, 215–223 embedded, 29 enterprise, 215–223 evangelizing, 261–262 executive support and See executive support future of, 275–292 innovation, 257–261, 276–281 key messages, 264 management, 4–5 measures of success, 75–93, 228, 277 mobile, 17–18, 64–65, 263, 276 new business opportunities, 10 operational, 6–7 performance, 5–6 personalizing, 190–191 in politics, 10 process improvement, 7–8, 276–278 promoting, 261–268 real-time, 174–177 requirements-driven, 191 research, 19–24 right-time, 175 secrets to success, 257–274 self-service, 52–53, 276 in sports, 9–10 technology changes, 13–14 terminology, 1–4, 200 types of decisions supported by, 246–247 value provided, 4–10 web-based, 16 BI applications, 92 BI directors, 230, 231 BI environments architectures, 27–50 centralized vs decentralized, 225–226 failed deployments, 241 physical vs virtual, 225 BI experts, 24, 229 BI industry consolidation, 16 BI initiatives, 127, 231 BI labs, 258–260 BI life cycle, 238–239, 241 BI market, 11–18 BI maturity model, 289–290 BI modules, 239, 244, 252–254 BI organizational model, 224, 225 BI partnerships See partnerships BI platforms, 16, 255–256 BI portal, 267 BI program manager, 223 BI projects vs BICCs, 224 considerations, 122, 231 managing, 207–210 quality and, 209–210 resources, 209, 210 ROI for, 85 scope, 209, 210 sponsorship See executive support timeliness, 209, 210 BI Search, 281–283 BI solutions, 264–265 BI standardization, 237–243 BI steering committee, 223–224, 231 BI survey, 19 BI team leaders, 230–231 BI teams, 24, 227, 228, 229–234 BI technology evaluation, 278–281 BI tool portfolio, 237, 239–242, 256 BI tool selection team, 233–234 BI tools, 233–256 best practices, 254–255 BI standardization and, 237–243 business query/reporting, 53–56, 58, 63, 64 changes to, 278 Index 311 choosing the right tool, 24, 243–244 vs data, 269 front-end, 51–74, 236, 238–239, 252 importance of, 236–237 internal vs third-party, 269 managing, 239–243 overview, 233–236 planning tools, 68–71 single vs multivendor, 239–242 standardization, 237–243 statistical tools, 226 strategies, 235–236 user segments, 243–245 visual discovery See visual discovery tools BI training, 228 BI users See also employees active users, 91–92 analytic job content, 248–249 choosing the right BI tool, 24, 243–244 data literacy, 249, 251 defined users, 91 deployment rates, 90–91 empowering, 249 ERP/source system use, 250 internal vs external, 251 job functions, 248 job levels, 247–248 number of, 87–88 percentage of employees, 88–90 range of, 243–244 source system, 249, 250 spreadsheet usage, 251 technical literacy, 250–251 travel required, 251 BI vendors, 239–242, 282 BICC guiding principles, 227–228 BICC personnel, 224 BICCs (Business Intelligence Competency Centers), 224–228 big data analytics, 73 overview, 3–4 privacy issues, 179 quality issues, 179 relevance issues, 191–193 technologies, 45–49 biosurveillance, 183 BIScorecard.com, 244 BlueCross BlueShield (BCBS), 283–284 Boicey, Charles, 260 Bolder Technology, 175–176 Botello, Drake, 226 Boyd, Barb, 83, 188 BP oil spill, 137 BPM (business performance management), 70 BPM Standards Group, 70 brick-and-mortar stores, 107 bring your own device (BYOD), 65, 181 browser cookies, 288 business analysts, 226 business analytics, 1, 2, business client services, 226 business definitions, 169 business impact, 75–79 business intelligence See BI Business Intelligence Competency Centers See BICCs business performance management (BPM), 70 business query/reporting tools, 53–56, 58, 63, 64 business units, 148 business views, 55–56 business–IT partnerships, 139–156 See also partnerships alignment and, 152–155 challenges, 215–216 communication issues, 139–140 frustration and, 139–140 hope and, 140 hybrid business–IT people, 144–146 perceptions, 149–150 yin and yang, 141–144 BusinessObjects, 16, 234, 235, 239 BYOD (bring your own device), 65, 181 Byrne, Tony, 282 C calculations, 66 call center dashboard, 246 call centers, 182–183 CAO (chief analytics officer), 128 CAPT (Center for Applications of Psychological Type), 142 capture latency, 176 case studies, 20–23, 240, 268–270 CeBIT, 233 312 Index Center for Applications of Psychological Type (CAPT), 142 CEO (chief executive officer), 123–126 CFO (chief financial officer), 123–124 charts, 263, 273 Chelsea Football club, 10 chief analytics officer (CAO), 128 chief executive officer (CEO), 123–126 chief financial officer (CFO), 123–124 chief information officer (CIO), 123–125, 127, 128 chief operating officer (COO), 123–124 CIO (chief information officer), 123–125, 127, 128 cloud computing, 277 cloud-based BI, 16, 43–45 Cloudera, 48 Cognos, 16, 283 Cognos PowerPlay, 17, 234, 235 collaboration, 284–285 Collins, Jim, 135, 230 color, 271–272, 273 commons, tragedy of, 220–221 companies culture, 131–137 employees See employees hiring the right people, 135–137 newsletters, 266, 267 company portal, 267 complex analytics, 226 conferences, 267 Constant Contact, 21, 83–85, 126, 192 consumers, 12–13 Continental Airlines, 23 COO (chief operating officer), 123–124 cookies, browser, 288 Coon, Chris, 213 corporate culture, 131–137 Corporate Express, 23 corporate information factory, 170 corporate performance management (CPM), 70 cost avoidance, 85 Costa, Mike, 154, 163, 173 coupons, 288 CPM (corporate performance management), 70 cross-dimensional calculations, 66 Crystal Reports, 239 culture, 131–137 customer segmentation, 243 customer service, 8, 93 Cutting, Doug, 46 D dashboards, 60–62, 246, 248, 276 data, 157–180 See also metadata access to, 5–6, 248, 252 aggregate, 41 air travel, 174–175 analyzing, 248, 249 architecture, 157–158, 169–171, 180 bad, 160–161, 178–179 best practices, 180 vs BI tools, 269 big See big data breadth, 158–159 business view of, 55–56 collected by companies, 192–193 common business definitions, 169 decision-making and, 14–15 deluge, 14–15 errors, 29, 228 extracting, 32 lost/stolen, 288–289 master, 29, 34 MDM, 34, 171–175 metadata, 33 multidimensional, 65–66 from multiple sources, 158, 166–169 new sources of, 159–160 overview, 157–158 privacy concerns, 192–193, 287–289 problems, 162–166 quality, 158, 159–166, 178–179 real-time, 174–177 relevant, 269 right-time, 175–177 social, 160 structured, 281 variety, velocity, visual, 17, 56–60 volume, data explosion, 14–15 data governance, 161–162 data literacy, 249, 251 data management, 226 data marts, 34, 35–37 Index 313 data mining, 72, 73 data modelers, 230 data overload, 14–15 data redundancy, 39 data scientists, 72, 248 data scrubbing, 112 data sources, 28–31 data transfer, 31–32 data warehouse tables, 37–40 data warehouses, 34–43 appliances, 40–43 central, 159 considerations, 4, 35 data transfer, 31–32 efect of Hadoop on, 286–287 need for, 34–35 philosophies, 169–170 scrum development and, 205 technology platform, 40–45 updating of, 175–177 Data Warehousing Institute See TDWI databases in-database analytics, 73 NoSQL, 45–48 RDBMS, 235 relational, 41–43 reporting, 34 Davenport, Tom, 72, 126 decision support, decision support system (DSS), 98 decision-making, 14–15 decisions fact-based, 246–247 latency, 176 types of, 246 demographics, 12–13, 19, 293–296 Dempsey, Andrew, 211 Department of Transportation (DOT), 100 departmental BI, 215–223 dimension tables, 37 dimensions, 39 DOLAP (dynamic OLAP), 67 DOT (Department of Transportation), 100 Dow AgroSciences, 233–234 Dow Chemical Company about, 21, 97 BI tool strategies, 235–236 ERP system, 163–165 globalization, 98–100 information management strategy, 95–96 MDM and, 172–173 role of luck, 95–98 tool strategy, 235–236 Union Carbide merger, 106–107 Dow Diamond Systems, 96 Dow Elanco, 233–234 Dow Workstation, 96, 98 drilling, 66 DSS (decision support system), 98 Dyche, Jill, 161 dynamic OLAP (DOLAP), 67 E Eckerson, Wayne, 60 e-commerce, 107 EIM (enterprise information management), 33–34 EIS (Executive Information Systems), 62 elevator speech, 263–264 ELT (extract, load, transform), 31 ELT scripts, 198–201 EMA (Emergency Medical Associates), 104–106, 183–187 e-mail, 285–286 embedded BI, 29 EMBI (Emergency Medicine BI), 21, 186 EMC Corporation, 16, 40, 48 Emergency Medical Associates (EMA), 104–106, 183–187 Emergency Medicine BI (EMBI), 21, 186 employees See also BI teams; BI users analytic job content, 248–249 data literacy, 249, 251 hiring the right people, 135–137 job function, 248 job levels, 247–248 job protection issues, 232 job satisfaction, 93 mastery of skills, 229 personal agendas, 232 sense of membership, 229 value of work, 229 women, 148 ENECO Energie, 129 Energizer Holdings, 246 314 Index English, Larry, 160–161 Enron, 137 enterprise BI, 215–223 enterprise information management (EIM), 33–34 enterprise performance management (EPM), 70 enterprise resource planning See ERP EPM (enterprise performance management), 70 ERP (enterprise resource planning), 62, 71, 190, 250 ERP systems considerations, 28, 29 Dow Chemical Company, 163–165 MDM and, 171–172 errors, data, 29, 228 Essbase, 17 ETL (extract, transform, and load), 31–32, 162–163 ETL scripts, 198–201 events, 266–267 Excel, 68, 69, 254, 285–286 exception-based reporting, 263 Executive Information Systems (EIS), 62 executive support, 121–138 best practices, 138 BI success and, 121–125 choosing best sponsor, 123–125 considerations, 23, 24 corporate culture and, 131–137 described, 121 earning trust, 129–130 ensuring ongoing support, 131 executive buy-in, 128–129 exploiting frustration, 130–131 importance of, 121–123 managing expectations, 130 role of sponsor, 126 types of executives, 123–125 external users, 251 extract, transform, and load See ETL F Facebook, 132, 226, 288 fact tables, 37 fact-based decisions, 246–247 Few, Stephen, 60, 271–272 financial consolidation tools, 71 Financial Genes, 59 FiveThirtyEight blog, 10 fixed reports, 54 FleetRisk Advisors, 87 FlightCaster, 103 FlightStats about, 21 alignment to business goals, 154–155 considerations, 48 data collection, 176–177 data quality, 169 LOFT effect and, 100–104 multiple data sources, 168–169 real-time data and, 176–177 taglines, 264 FOCEXEC reports, 234 formatting, 54–55 Forrester Consulting, 85–86 fraud detection, 221–222 front-end tools, 51–74, 236, 238–239, 252 frustration, 109–112 business/IT, 139–140 exploiting, 130–131 overview, 109–111 public education and, 111–112 FTEs (full-time equivalents), 107 full-time equivalents (FTEs), 107 G GCH (Global Complaint Handling) project, 165–166, 212 GDP (gross domestic product), 255 Gladwell, Malcolm, 137 Global Complaint Handling (GCH) project, 165–166, 212 global financial crisis, 255 global positioning system (GPS), 288 Global Reporting Project, 96–98, 154, 233, 235 globalization, 98–100 Goodman, Neal, 126 GPS (global positioning system), 288 graphs, 263, 270 Green, Brian, 131 Greenplum, 16 Greenplum Pivotal HD, 48 gross domestic product (GDP), 255 Index 315 H Hackathorn, Richard (Dr.), 175–176 Hadoop Distributed File System (HDFS), 32, 47, 170–171 Hadoop-based solutions big data and, 45–49, 73 effect on data warehousing, 286–287 medical records and, 260 Harriott, Jesse, 128 Harris, Jesse, 292 Hastings, Reed, 114, 210–211 HBase, 48 HDFS (Hadoop Distributed File System), 32, 47, 170–171 healthcare importance of relevance, 183–187 improving via BI, 184–187 rising costs, 113–114 Hedges, Mike, 259–260, 292 hierarchies, 39 Hill, Jim, 147, 162, 213, 231 Hive, 48 HiveQL, 48 HP, 16 “hub and spoke” approach, 170 Hughes, Ralph, 205, 208 hybrid business–IT people, 144–146 Hyperion, 16 I IaaS (Infrastructure-as-a-Service), 43 Iannaconne, Ray, 291 IBM, 16 IBM OS/s, 233 IDC (International Data Corporation), 71–72 Impala, 48 INCA (Infrastructure for Code Administration), 172–173 incentive compensation, 148 incentives, 189–190 in-database analytics, 73 independent spreadsheets, 92 industry journals, 266–267 Informatica, 239 Information Builders WebFOCUS, 44–45 information delivery, 226 information overload, 14–15 information requirements, 247 Infrastructure for Code Administration (INCA), 172–173 Infrastructure-as-a-Service (IaaS), 43 in-memory OLAP, 67 in-memory technology, 277 Inmon, Bill, 96, 169–170 innovation, 257–261, 276–281 Innovation Lab, 260–261 interactive fixed reports, 252 interactivity, 66 internal users, 251 International Data Corporation (IDC), 71–72 iPads, 10, 17–18 iPod armband, 10 IRI Express, 235 IT manager, 123 IT-business partnerships, 139–156 J Jackson, Danny, 150–152 Janis, Norman (Dr.), 153 Jansen, Brenda, 246 jargon, 147 job function, 248 job levels, 247–248 job protection issues, 232 Joint Commission, 113 Jurgensen, Jerry, 111 K Kanban development, 206 Kanter, Rosabeth, 229 Kennedy, Jeff, 100, 177, 291 Kepler, Dave, 99, 106, 135 key messages, 264 key performance indicators (KPIs), 62, 182, 254, 285–286 Kimball, Ralph, 169–170 King.com, 171, 257–258 Kodak, 255 KPIs (key performance indicators), 62, 182, 254, 285–286 316 Index L Laney, Doug, Larson, Karen, 237 latency, 176 Learning Circle components, 44–45 measures of success, 82–83 opportunity/frustration issues, 111–112 overview, 21, 78–79 social data and, 188 Successful BI survey, 78–79 level leaders, 230–231 Liebtag, Jonathan, 133–134 LOFT (Luck, Opportunity, Frustration, and Threat), 24, 119 LOFT effect, 95–119 best practices, 119 considerations, 117–119 frustration, 109–112 opportunities, 100–109 role of luck, 95–98 role of time, 116–117 threats, 113–116 lookup tables, 37 Lucene search engine, 282 Luck, Opportunity, Frustration, and Threat See LOFT luck, role of, 95–98 “lunch and learns,” 267, 269 Lutchen, Mark, 127 M Macy’s, 22, 107–108, 171 manufacturing systems, 28 MapReduce framework, 47–48 Masciandaro, Mike, 291 massively parallel processing (MPP), 41 master data, 29, 34 master data management See MDM MBTI (Myers-Briggs Type Indicator), 141–143 McClellan, Ed, 83, 268–269 McDonald, Bob, 132 McKinley, Scott, 260 MDM (master data management), 34, 171–175 MDM market, 173 MDM systems, 172–175 MDX (Multidimensional Expressions), 68 “me” generation, 181 measures, 37 medical records, 260 Medtronic about, 22, 114 agile development and, 211–212 innovation, 259–260 LOFT effect, 114–116 tracking complaints, 165–166 metadata, 33 Microsoft, 233 Microsoft Analysis Services., 284 Microsoft Excel, 68, 69, 254, 285–286 Microsoft Office, 68–69, 254–255, 285–286 Microsoft PowerPoint, 244, 286 Microsoft Reporting Services, 239 Microsoft Windows, 233 MicroStrategy, 239 MicroStrategy Cloud, 44 Millenials, 181 mobile BI, 17–18, 64–65, 263, 276 mobile phone apps, 12 MOLAP (multidimensional OLAP), 67, 234 Moneyball, MongoDB, 48 Morris, Henry, 71 MPP (massively parallel processing), 41 Multidimensional Expressions (MDX), 68 multidimensional OLAP (MOLAP), 67, 234 Musunuru, Kiran, 212 Myers-Briggs Type Indicator (MBTI), 141–143 N NASA, 226 National Instruments, 226–227 National Security Agency (NSA), 288 Nationwide, 111, 112 Netezza, 16 Netflix about, 22, 81–82 agile culture at, 210–211 BI organizations, 225–226 culture, 133–134 Index 317 innovation, 257 measures of success, 80–82 partnerships at, 150–152 personalization, 191 threats, 114 newsletters, 266, 267 Nielsen, 260, 261 Nieters, Sarah, 116, 212 Nike, 10 No Child Left Behind Act, 82 normalized tables, 38–39 Norway Post, 22, 166–168 NoSQL databases, 45–48 Novation, 87 NSA (National Security Agency), 288 O Obama, Barack, 10 ObamaCare, 113 ODS (operational data store), 39 Office See Microsoft Office OHECN (Ohio Education Computer Network), 45 Ohio Education Computer Network (OHECN), 45 OLAP (online analytical processing), 16, 17, 65–68 OLAP cube, 213 OLAP platforms, 66–68 OLAP viewers, 68 Olsen, Dag Vidar, 291 Olson, Ken, 90 online analytical processing See OLAP Open Government Initiative, 10 open-source software, 18 operational BI, 6–7 operational data store (ODS), 39 operational decisions, 246 operational systems, 28–34 See also source systems opportunities, 100–109 Oracle, 16, 234, 235 organizational issues, 215–232 organizational structures, 148 P PaaS (Platform-as-a-Service), 43–44 Palladium Group, 62 Panorama Sotware, 284–285 partnerships See also business–IT partnerships alignment and, 152–155 best practices, 155–156 BI success and, 149–150 guidelines, 146–148 Netflix example, 150–152 PDF files, 88 Pekala, Mike, 177 performance calculating, 76–77 operational BI, 5–6 underperformance consequences, 215–216 performance management See PM personal agendas, 232 personalization, 190–191 pivoting, 66 planning tools, 68–71 Platform-as-a-Service (PaaS), 43–44 PM (performance management), 69, 70 PM tools, 69–71 POCs (proofs of concept), 258–259 podcasts, 266 politics, 10 portal, BI, 267 PowerPlay, 17, 234, 235 PowerPoint, 244, 286 PowerPoint modules, 244 predictive analytics, 72–73, 254 predictive models, 226 PRISM project, 288 privacy issues, 192–193, 287–289 product IDs, 34 product names, 264–265 production reporting tools, 63–64 projects See BI projects promotion, 261–268 promotional media, 266–268 proofs of concept (POCs), 258–259 Q QlikTech, 17 QlikView, 59, 61, 62 QlikView dashboard, 61 queries ad hoc, 53, 263 business query tools, 53–56, 58, 63, 64 318 Index queries (cont.) formatting, 54–55 vs reporting, 53 SQL See SQL Qwikster, 210–211 R Raden, Neil, 246, 247 RDBMS (relational database management system), 235 Real Story Group, 282 real-time BI, 174–177 real-time data, 174–177 RedEnvelope, 255 redundancy, 39 reference tables, 37 relational database management system (RDBMS), 235 relational databases, 41–43 relational OLAP (ROLAP), 67 relational storage, 41 relevance, 181–193 relevant data, 269 reporting business query/reporting tools, 53–56, 58, 63, 64 described, 1, 53 excption-based, 263 production, 63–64 vs queries, 53 reporting database, 34 reports ad hoc, 53, 54, 92 Crystal Reports, 239 fixed, 54 FOCEXEC, 234 formatting, 54–55 interactive, 252 sample, 54 standard, 92 templates, 55 requirements-driven BI, 191 research, 19–24 resources centralized, 221, 222 considerations, 209, 210 dedicated, 224 described, 209 enterprise, 221, 222 listed, 297–298 sharing, 231 return on investment See ROI revenue per call (RPC), 182 right use case, 243 right-time BI, 175 right-time data, 175–177 road shows, 266 Rohm and Haas, 99–100, 106–107, 165 ROI (return on investment) BI and, 85–86 calculating, 86–87 considerations, 75 overview, 85–86 ROLAP (relational OLAP), 67 Rothman, Jonathan, 104, 136, 184–185 Rottunda, Sara, 212 RPC (revenue per call), 182 Rudin, Ken, 226 S SaaS (Software as a Service) solutions, 43, 44 Sadara, 165 sales systems, 28 San Diego Unified School District, 87 San Francisco 49ers, 10 SAP, 16, 162, 234, 235 SAP BW Accelerator, 235 SAP Data Services, 239 SAP ERPs, 162–165 SAP Hana, 239, 283 Sarbanes-Oxley Act of 2002, 12 SAS BI, 285–286 SAS JMP, 235 “scale out,” 41 “scale up,” 41 Schiff, Craig, 69–70 scope, 209, 210 scorecards, 62–63, 253–254 scrum development, 204–206 Scrum.org, 205 Search, BI, 281–283 segmentation, 243–245 self-assessment worksheet, 245 self-service BI, 52–53, 276 Shepherd, Denise, 113–114 Silicon Graphics, Silver, Nate, 10 Six Sigma, 163, 164 slicing, 66 smart meters, 192–193 Index 319 smartphones, 65 SMP (symmetric multi processing), 41 Snowden, Edward, 288 snowflake design, 37–38 social data, 160 social media, 260 social networking, 12–13, 18, 284–285 Software as a Service (SaaS) solutions, 43, 44 source system users, 249, 250 source systems, 28–34 sponsorship See executive support sports, 9–10 spreadsheet integration, 248 spreadsheet usage, 251 spreadsheets, 68–69, 92, 285 SQL (Structured Query Language) NoSQL databases, 45–48 overview, 46 self-service BI and, 52–53 staff meetings, 267 stakeholders, 23 standardization, 237–243 Staples, 23 star schemas, 37, 38, 41, 170 Starbucks, 192, 270–271 statistical analysis, 72 statistical tools, 226 statisticians, 72–73, 248 Storage Center (S3), 44 strategic decisions, 246 strategy maps, 254 structured data, 281 Structured Query Language See SQL success, measures of, 75–93, 228, 277 SUCCESS solution, 234 SUCCESS team, 98 Successful BI survey, 19, 75–92, 293–296 summary tables, 37, 263 supply chain systems, 28 “sweet spot,” 244 symmetric multi processing (SMP), 41 T Tableau Software, 17, 49, 56, 59, 60 tables aggregate, 37, 263 data warehouse, 37–40 dimension, 37 fact, 37 lookup, 37 normalized, 38–39 reference, 37 summary, 37, 263 tablet computers, 17–18, 65 See also iPads tactical decisions, 246 taglines, 264, 265 Taylor, James, 246, 247 TDWI (The Data Warehousing Institute), 19, 34, 293 TDWI Best Practices awards, 19–24 teachers, 188 team building, 147–148 technical literacy, 250–251 technobabble, 147, 264 technology adoption of, 228 in-memory, 277 innovation, 257–261, 276–281 maturity of, 278, 280 templates, 55 Teradata, 16, 239 terminology, 1–4, 200 text analytics, 281–284 The Data Warehousing Institute See TDWI threats, 113–116 TIBCO Spotfire, 59 timeliness, 209, 210 Tomak, Kerem, 107–108 tools See BI tools tragedy of the commons, 220–221 training methods, 269–270 transaction systems, 39 Transportation Security Administration (TSA), 175 travel, amount of, 251 TSA (Transportation Security Administration), 175 T-shirt days, 267 Tufte, Edward, 270 U Under Armour, 10 Union Carbide, 106–107 United Airlines, 23 universities, 260 U.S Postal Service (USPS), 150–152 use case, 243 user conferences, 267 320 Index user segments, 244–245 users See also employees active users, 91–92 analytic job content, 248–249 choosing the right BI tool, 24, 243–244 data literacy, 249, 251 defined users, 91 deployment rates, 90–91 empowering, 249 ERP/source system use, 250 internal vs external, 251 job functions, 248 job levels, 247–248 number of, 87–88 percentage of employees, 88–90 range of, 243–244 source system, 249, 250 spreadsheet usage, 251 technical literacy, 250–251 travel required, 251 USPS (U.S Postal Service), 150–152 Utah Department of Education, 255 V value add, 82 value-added assessment, 82 Van De Vanter, Kay, 126 van den Dungen, Ton, 129 variety, velocity, vendors, 239–242, 282 Vertica, 16 video clips, 266 visual data discovery, 253 visual discovery tools vs business query tools, 58, 64 considerations, 17, 277 guidelines, 271–272 overview, 56–60 visualizations, 270–273 volume, VP of marketing, 123 W Walker, Dave, 147, 213, 230–231, 292 Walker, Scott, 111 Wal-Mart, 162, 260 waterfall development, 195–201, 210 Watson, Hugh, 207–208 Watson, Thomas, 90 web-based BI, 16 WEBeMARS, 113–114, 268 WebFOCUS, 44–45 WellPoint, 20, 162 What’s in it for me (WIFM)?, 250 White, Colin, 175 Whiting, David, 83–84, 126 Whittmer, Leon, 174 WIFM (What’s in it for me)?, 250 Winter, Richard, 287 Winter Corporation, 287 women, hiring, 148 workforce demographics, 12–13 Y yin and yang, 141–144 Z Zuckerberg, Mark, 132 .. .Successful Business Intelligence Second Edition Unlock the Value of BI & Big Data About the Author Cindi Howson is the founder of BI Scorecard (www.biscorecard.com), a resource for in-depth BI. .. out the 3Vs of big data in the late 1990s (then at Meta Group) that are now part of the big data lexicon.2 With these characteristics in mind, it’s not surprising that some of the initial big data. .. see a big disparity in companies who are exploiting BI and big data, and others who are floundering While some of the same challenges remain, in 2013, the influences of big data, cloud, mobile,