Abbasi, Sohaib, 234
Ab Initio Software Corporation, 233
A/B testing, 242
AC Milan, 42
ACNielsen, 131
action, designing decision making for, 163
activity-based costing (ABC), 92
actuarial and risk management 103–105
advertising, digital, 133, 136–137
AIG, 202
alerts, 253
algorithmic trading, 85
Amazon
analytical decision making at, 33–34
analytical leadership at, 193–194
analytics-based competition at, 24–25
Analytics Maturity Assessment of, 47
Echo/Alexa, 255
experiments at, 73
pricing optimization at, 141
senior management commitment at, 55, 56
supply chain management at, 149–151
Ambrose, John, 147
American Express, 103, 123–124
analysts
data scientists vs. quantitative, 5, 204–205
in DELTA model, 17, 46–47, 178, 179–180
DELTA model on, 47
software as replacement for, 187–190
analytical capabilities
key elements in, 161
managing for outcomes and, 181–185
maturity model on, 61
analytical companies, 63–64, 174–176
analytical companies in, 174–176
analytical decision making in, 33–36
assessing the degree of, 61–67
choosing strategic focus, 161–162
data and IT capabilities in, 220–221
definition of, 26
four pillars of, 60
full-steam-ahead vs. prove-it path to, 164–170
the leading edge in, 36
prove-it detour toward, 165–170
senior executives and, 190–200
analytical competitors, 176
definition of, 45
enterprise-level approach by, 51–54
external processes in, 130
four pillars of, 60
large-scale ambition in, 58–61
senior management commitment at, 54–58, 60–61
strategic, distinctive capability at, 48–51
analytical eras. See four eras of business analytics
analytical maturity model. See DELTA model
analytically impaired organizations, 65–67
analytical techniques
additional technical capabilities, 180–181, 183
for customer relationship management, 134–135
leaders’ knowledge of, 192–193
for supply chain management, 148–149
Analytics 1.0–4.0. See four eras of business analytics
analytics
definitions of terms in, 26
DELTA model for capability building in, 17, 46–47
enterprise-level approach to, 51–54
in government, 81–83 (see also government)
localized, 65
Analytics at Work (Davenport, Harris, and Morison), 17, 46
analytics experts
analytics hubs, 54
Analytics Maturity Assessment, 46–47
Apex Systems, 124
Apple, 255
of analytical technology, 217–224
analytical tools/applications and, 236–245
data repositories, 223, 234–236
deployment processes and, 246
artificial intelligence, 8–11, 118
artisanal analytics, 10, 11–12
Ascential Software, 233
competitive, 65
assumptions in quantitative analysis, 34
attribute analysis, 23
autonomous analytics, 8–12, 25, 26, 251
autonomous decision-making applications, 213–214
Bain & Company, 74
Barton, Richard N., 24
baseball, 37–40, 122, 141, 208
Beech, Roland, 42
behaviors, managing, 181
Bell, David, 145
Betterment, 172
Beyene, Saba, 128
Bezos, Jeff, 33–34, 55, 56, 73, 193–194, 229
Bierhoff, Oliver, 42
definition of, 31
origins of, 31
big data analytics, 32
Big Data @ Work (Davenport), 47
“black box” problem in machine learning, 10–11
Blink (Gladwell), 33
Blockbuster, 21
Bloomberg Technology, 151
Boger, Joshua, 118
Boston Celtics, 42
Boston Red Sox, 38–39, 208–209
Bridgewater Associates, 85
Brougher, Bill, 109
business environments, changing, 99–100
business intelligence, 2–3, 25, 31
business intelligence and advanced analytics, 32
business models
digital industrial, 7
obsolete, 88
Caesars Entertainment, 24
analytical leadership at, 194
analytical professionals at, 201
analytical strategy at, 161–162
distinctive capabilities at, 29
enterprise-level approach at, 51
external processes analytics at, 132–133
market share at, 59
pricing optimization at, 141
senior management commitment at, 55, 56
strategic, distinctive capability of, 48
unique strategy of, 79
Cafarella, Mike, 4
campaign management software, 131–132
Canadian Tire Corporation, 230
capability maturity model, 61
capacity planning, 148
amateur analysts in, 208
ambition at, 58
analytical decision making at, 34
analytical leadership at, 194
data issues at, 225
duplication of strategy at, 79
information-based strategy at, 29
outperformance of competitors by, 80
results at, 59
senior management commitment at, 55
subprime customers at, 139
talent management at, 125, 127–128
Capital One Health Care, 143–144
CareSage, 85
Catalina, 86
CHAID (chi-square automatic interaction detection), 134
change management, 115
Chico, 177
chief analytics officers, 53–54
chief data and analytics officers (CDAOs), 53–54, 198–199
chief data officers (CDOs), 53–54
chief financial officers (CFOs), 94–95, 194–196
chief information officers (CIOs), 196–198
Cinematch, 22
city planning, 83
Clark, Ben, 73
Climate Pro, 7
clinical trial design, 119
closed loops of analytics, 142–143
Coalition Against Insurance Fraud, 103–104
cognitive technologies, 8–11, 236–237, 240
collaborative filtering analytics, 150–151
Collins, Jim, 73
combinatorial optimization, 92–93
combinatorics, 148
company value, 99
competitive advantage
complacency, 176
CompStat, 81
computer-aided design (CAD), 108–109, 241
configuration problem, 110–111
conjoint analysis, 134
constraint analysis, 93
content personalization, 146–147
credit card industry, 69–70, 103–104
Credit Suisse, 98
crime statistics analysis, 81–82, 83, 163
cross-selling, 137
Cuban, Mark, 43
culture
in Analytics 1.0, 3
in Analytics 2.0, 6
executive commitment to, 55–56
executives in changing, 190–192, 196–198
customer experience life cycle model, 79
customer loyalty, 59
customer relationship management, 99, 129–147
analytical techniques in, 134–135
attracting/retaining customers, 133–139
connecting suppliers with customers and, 147–151
content personalization and, 146–147
life cycle management and, 145
pricing optimization and, 135, 140–141
Cutting, Doug, 4
dashboards, 99
data
acquisition of, 232
capabilities by analytical competition stage, 220–221
cleansing, 232
in DELTA model, 17, 46–47, 177, 178
getting, cleaning, and loading, 3
labeled training, 255
management, 27
organization and storage, 232
structuring, 13
value extraction from, 1–2, 230–231
data analytics
organizational implications of, 12–15
pace of change in, 1
data cubes, 235
data literacy, 211
data management, 33, 222, 224–233
data products, 5
data repositories, 223, 234–236
DataRobot, 9
data scientists, 5
culture and attitude of, 204–205
education of, 6
experimentation skills for, 13
job requirements for, 204
“Data Scientist: The Sexiest Job of the 21st Century” (Davenport and Patil), 5
data warehousing, 31, 229–230, 234–235
Davenport, Thomas H., 5, 17, 46, 259
decision making
in analytical competition, 28
automating without monitoring, 186
designing into processes, 163
enterprise systems and, 75
fact-based, 33
decision support, 91
in Analytics 1.0, 3
in Analytics 2.0, 5
external reporting/scorecards and, 96–98
decision support systems (DSS), 30–31
Deere & Company, 59
Deloitte, 49, 85, 105, 195, 208, 261
DELTA model, 17, 46–47, 177–183
DELTTA model, 47
demand-supply matching, 148
de Molina, Al, 196
Department of Veterans Affairs, 83
deployment processes, 223, 246
descriptive analytics, 25
definition of, 26
external reporting/scorecards and, 96–98
localized analytics and, 65
tools, 243
See also business intelligence
Dhore, Prasanna, 244
diapers and beer urban legend, 187–188
Dibble, Bill, 104
digital analytics, 242
digital industrial business model, 7
Disney’s Parks and Resorts, 142–143
Disraeli, Benjamin, 186
distinctive capabilities
of analytical competitors, 45, 48–51
competitive advantage and, 78–80
DnB NOR, 137
Dodd-Frank Act, 223
Domain Awareness System, 81–82
Dow Chemical, 159
dynamic pricing, 140
Earthgrains, 192
eBay, 122
econometric analysis, 133, 136–137
econometric modeling, 134, 142
edge analytics, 228
Einstein, 9
employee experience life cycle model, 79
employee learning and innovation, 99
employees
human resource analytics and, 122–128
energy cost management, 101–103
Entelos, Inc., 120
enterprise-level approach, 17, 46–47, 51–54, 177, 178
enterprise performance management, 98–100
enterprise resource planning (ERP) systems, 30, 31
enterprise systems
analytical tools in, 237
as data source, 227
decision making and, 75
eras, analytics. See four eras of business analytics
Eskew, Mike, 152
ESPN, 40
Evalueserve, 208
event streaming, 241
executives
analytical, characteristics of, 192–193
commitment of, 54–58, 60–61, 176, 199–200
in culture change, 14–15, 55–56
fact-based decision making and, 160
in full-steam-ahead approach, 164–165
managing analytical people and, 190–200
passion for analytics in, 55–56
sponsorship by, 36
executive support systems, 31
experimental design, 93
experimentation, 73
external processes and, 130
at Google, 109
in marketing, 134
skills in, 13
worker skills in, 210
explanatory analytics, 253
extract, transform, and load (ETL), 3, 233–234
eye tracking, 109
fashion industry, 29
Fast Company, 73
FedEx, 152
Field of Dreams approach, 185
enterprise performance management/scorecards, 98–100
financial services
analytical products/services in, 85
Analytics Maturity Assessment of, 47
senior management commitment in, 56–57
Fincher, David, 23
fivethirtyeight.com, 39, 40–41
Fleet Bank, 106
focus, choosing strategic, 162
four eras of business analytics, 1–15
four pillars of analytical competition, 60
Foxwoods Resort Casino, 79
Franks, Bill, 6
fraud
welfare, 82
Friedman, Frank, 195
full-steam-ahead approach, 164–165
funding of analytical groups, 206
future-value analysis, 93
Garmin TruSwing, 87
data management at, 225
differentiation at, 29
GE Capital, 208
genetic algorithms, 93
Genpact, 208
Gibson, William, 249
Gladwell, Malcolm, 33
Good to Great (Collins), 73
Google, 5
analytical leadership at, 194
analytics-based competition at, 24–25
artificial intelligence software, 262
digital advertising at, 133, 136–137
experimentation at, 109
experiments at, 73
human resource management at, 43
human resources analytics, 122
machine cognition at, 9
PageRank algorithm, 5
People and Innovation Lab (PiLab), 126
Project Oxygen, 126
talent management at, 125–127, 202, 210
government
as data source, 228
Internal Revenue Service (IRS) programs, 82
New York City’s CompStat program, 81
Singapore’s “Smart Nation” program, 83
UK police analytics insights, 163
graphics processing units (GPUs), 9
Green Bay Packers, 41
Griffiths, Hugh, 146
Grove, Jonathan, 103
H2O, 13
Harrah’s Entertainment. See Caesars
Hastings, Reed, 21, 22, 24, 55, 56
cost management, 101
insurance and credit scores in, 50
research and development analytics, 120–121
Healthways, 121
Henry, John, 38
Hewlett-Packard, 97
Hive, 4
Holland, Chuck, 57
HOLT, 98
Houghton Mifflin Harcourt, 144
House of Cards, 23
human element in analytics, 36, 43–44
See also data scientists; quantitative analysts
human resource information systems (HRIS), 122
human resources analytics, 122–128
human resources management, 187–216
of analytical amateurs, 208–216
of analytical professionals and data scientists, 200–208
IBM, 208
analytical tools, 35
Cognos, 239
Global Services, 85
merger and acquisition algorithms, 34, 106
on performance and analytics use, 77
SPSS, 31
Watson, 9
Infinity Property & Casualty, 104
Informatica Corporation, 233, 234, 236
information distribution, 221
information management, 27, 206
information orientation, 198
Information Resources Inc., 86–87, 131
information technology
in architecture of analytical technology, 218–224
industry, 97
signposts of effective, 222–223
vision for, 171
information workers, core skills for, 210–211
initiatives, evaluating, 184
in-memory processing, 252
in silico research, 120
Intel, 95
Intermountain Healthcare, 121
analytical techniques for, 92–94
human resources analytics, 122–128
operational analytics, 106–117
mergers and acquisition analytics, 105
research and development analytics, 117–121
See also processes
Internal Revenue Service (IRS), 82
National Research Program, 82
Taxpayer Compliance Measurement Program, 82
International Data Corporation (IDC), 74, 217, 237
International Institute for Analytics, 6, 46–47, 63
Internet of Things, 15, 34–35, 228, 250–251
Intuit, 73
intuitive decision making, 29–30
evidence on, 33
executive commitment and, 199–200
on pricing, 140
inventory optimization, 59
Irish Tax and Customs Authority, 83
Ittner, Chris, 99
Jagex Games Studio, 146
James, Brent, 121
JetBlue, 88
Johnson & Johnson, 120
kaizen, 131
Keen, Peter, 30
Kirby, Julia, 259
Kizer, Kenneth W., 83
Klein, Gary, 33
knowledge discovery, 31
Komatsu, Shigeru, 112
Korn Ferry, 49
Lane, Katrina, 201
Larcker, David, 99
law enforcement analytics. See government
analytical, characteristics of, 192–193
in building analytics capability, 17, 46–47
in DELTA model, 17, 46–47, 166, 178–179
emergence of analytical, 193–194
See also executives
Lever Brothers, 136
Leverhulme, Lord, 136
Lewis, Peter, 229
Liberson, Dennis, 127
lifetime value analysis, 134
LinkedIn, 5
Little, Grady, 39
localized analytics, 65
location analysis, 148
Lofgren, Chris, 56
Loop AI Labs, 9
Loveman, Gary, 51, 55, 56, 141, 194
loyalty programs, 72, 86, 138–139
machine learning, 8–11, 14, 240
management
alerts, 253
in analytical competition development, 36
by analytical competitors, 45
of analytical people, 187–216, 258–259
enterprise-level approach to analytics and, 51–54
fact-based, 25
managing for outcomes and, 181–185
skills across analytics eras, 13–15
management consulting, 49
manufacturing analytics, 106–111
market experiments, 134
marketing
analytical techniques in, 134–135
price optimization and, 135, 140–141
market share, 59
Marriott, J. Willard, 71
Marriott International, 71–72, 197
data sharing at, 260
distinctive capability at, 51
strategic, distinctive capability at, 48
Mars, 25
Massachusetts Institute of Technology (MIT), 77, 78
McDonald, Robert, 83, 194, 197, 211
McKinsey Solutions, 49
McNamara, Robert, 81
MediSpend, 97
mergers and acquisitions, 34
meritocracy, 193
metadata repositories, 235
metrics, 261
on analytical activity results, 58–59
exploitation and exploration of, 49–51
at Marriott, 71
monitoring strategic, 163
staffing, 125
Microsoft, 5
analytical capabilities in software by, 35, 251
energy cost management, 101–103
machine cognition at, 9
Milan Lab, 42
mining, 35
MIT Sloan Management Review, 77
MIT Sloan Sports Analytics Conference, 41, 42
MMIS, Inc., 35
modeling
uplift, 135
Modular Category Assortment Planning System, 148
Moneyball (Lewis), 37, 80, 122
Monsanto, 7
Monte Carlo simulation, 93
Moore’s Law, 8
multiple regression analysis, 93–94, 135
natural disaster preparedness, 83
natural language processing, 14, 241
needs assessment processes, 52
analytical leadership at, 194
experiments at, 73
senior management commitment at, 55, 56
strategic, distinctive capability of, 48, 51
New England Patriots, 41, 61, 122–123, 194
NewVantage Partners, 14, 262–263
New York Police Department, 81–82
Nucleus Research, 237
O2, 146
Oberhelman, Doug, 116
oil exploration, 35
OLAP (online analytical processing), 31, 238–239
On Assignment, 125
open-source technologies
analyst skills in, 13
distributed data frameworks, 235–236
machine learning, 9
statistical programming, 26–27
operational analytics, 6, 106–117
operations, 99
Optum, 7
Oracle, 35
ORION. See UPS ORION project
Ormanidou, Elpida, 177
outcomes, managing for, 181–185
Owens & Minor, 51
Partners HealthCare, 215
Passerini, Filippo, 197
passion, 192
Perez, William, 191
analytics as competitive advantage and, 78–80
commitment to analytics and, 75–77
evidence assessment and, 73–78
external reporting/scorecards and, 96–98
managing for outcomes and, 181–185
market for analytical products/services and, 84–87
monitoring, 31
See also metrics
performance reporting. See descriptive analytics
pharmaceutical industry, 80, 118–120
Philips, 85
pickstreams, 35
Pig, 4
Pioli, Scott, 123
Planck, Max, 15
Podium Data, 236
point-of-sale (POS) systems, 30, 31
politics, 207
in Analytics 1.0, 2
in customer life cycle management, 145
definition of, 26
performance and embedded, 77, 260
predictive lead scoring systems, 143–144
prescriptive analytics, 25, 254
in Analytics 1.0, 2
definition of, 26
Price, Mark, 108
Price, Paul, 106
price management and profit optimization (PMPO) solutions, 140
price optimization, 135, 140–141
pricing trend analysis, 130
technology investments and, 219
processes
analytics integration in, 6
managing for outcomes, 183
analytical leadership at, 194
data scientists at, 202
experiments at, 73
external processes analytics at, 131
mergers and acquisitions, 34, 106
metrics at, 59
outsourcing at, 258
reporting relationships at, 197
productivity, 122
products
constraint analysis for, 93
extensions of existing, 117–118
incorporating analytics in, 87
managing for outcomes, 183–184
research and development analytics, 117–121
programs, managing for outcomes, 183
Progressive Insurance, 50–51, 72, 80, 139, 219
Snapshot, 229
Python, 4
quality analytics, 24, 106–107, 111–112
quantitative algorithms, 239
quantitative analysis, 33
assumptions in, 34
at Netflix, 24
quantitative analysts, 5, 13–14
Quill, 98
R (programming language), 4, 237
radio frequency identification (RFID) sensors, 34–35
RapidMiner, 237
RBC Financial Group, 52
Recorded Future, 261
regulated industries, 80, 221–222
Renaissance Technologies, 85
renewable advantage, 80
Renwick, Glenn, 219
research and development (R&D) analytics, 117–121
resources
allocation of, 163
commitment of, 165
nonfinancial/intangible, 99
retention agents, 145
return on investment (ROI), 74
revenue opportunity, 71
revenue optimization, 82
Rocky Mountain Steel Mills, 107–108, 110
Roland Rating, 42
routing, 149
Royal Bank of Canada, 52
rule engines, 239
RuneScape, 146
Ruthven, Graham, 42
sales, converting customer interactions into, 143–145
Salesforce.com, 9, 35, 143, 251, 254
sales trend analysis, 130
San Diego Padres, 38
San Francisco Giants, 141
Sara Lee Bakery Group, 55
Sarbanes-Oxley Act of 2002, 221–222
SAS Institute, 9, 31, 35, 242–243, 245
Enterprise Miner, 245
High-Performance Analytics, 244
scheduling, 149
Schneider National, 54, 56, 113, 197
scientific retailing, 140
search engine optimization, 135
services, managing for outcomes, 183–184
shareholders, external reporting to, 96–98
Simchi-Levi, David, 77
supply chain, 149
tools for, 241
Singapore “Smart Nation” initiative, 83
situational awareness systems, 261
Skillsoft, 147
skimming the cream off the garbage, 139
Smart Inventory Management System (SIMS), 110–111
soccer, 42
social media analytics, 242
software, 223
in Analytics 1.0, 3
in Analytics 4.0, 8
capability maturity model for, 61
decision support systems, 30–31
democratization of analytics, 251–252
Sorrell, Martin, 136
Southwest, 88
Spacey, Kevin, 23
sponsors, 36
in analytical aspirations stage, 170–174
in analytical companies, 174
in full-steam-ahead approach, 164–165
in prove-it detour, 166
sports, analytics in, 37–43, 122–123
pricing optimization, 141
strategic, distinctive capability and, 48
error rates in, 53
SPSS, 31
Stabell, Charles, 30
staffing metrics, 125
Stanley, Tim, 132
statistical algorithms, 239
statistical analysis, 33
Strata + Hadoop World conference, 146
strategy, 7
analytical competitors and, 45
choosing focus or target in, 162
distinctive capabilities in, 48–51
suppliers, sharing data with, 132–133
supply chain management, 129–133, 147–153
analytical techniques in, 148–149
connecting customers and suppliers in, 147–151
logistics analytics in, 116–117
logistics management in, 151–153
optimization in, 6
support vector machine, 135
sustainable pipelines, 205–206
systems, analytics integration in, 6
Talend, 233
Tamr, 234
choosing strategic, 162
Taylor, David, 211
Tesla, 118
“test and learn” approach, 23–24
text categorization, 241
textual analysis, 94
Thomas, Charles, 54
Thompson, Mike, 112
time series experiments, 135
for analytical amateurs, 212–213
for analytical competition, 32, 34–36
in DELTTA model, 180
performance and spending on, 77
returns from investments in, 74–75
for self-service analytics, 2–3
in sports data, 43
visual analytics, 243
Toshiba Semiconductor Company, 111–112
Total Quality Management, 106
transaction data, 29
transformation tools, 222
transportation safety analytics, 112–113
Trifacta, 234
Trillium, 233
Troutwine Athletic Profile, 123
TrueCar, Inc., 236
Two Sigma, 85
uniqueness, 79
United Healthcare, 7
University of Utah Healthcare, 101
uplift modeling, 135
UPS ORION project, 7
change management in, 13
logistics analytics in, 114–116, 151–152
real-time analytics in, 252
savings from, 59
senior management commitment in, 57–58
Value Driven Outcomes (VDO), 101
value drivers, 99
Vardi, Nathan, 85
Varian, Hal, 202
Verizon Wireless, 85
Vertex Pharmaceuticals, 65, 118–120, 243
Veterans Affairs (VA) hospitals, 83
vision, 171
visual analytics approach, 112, 223, 243
Walmart, 51
human resources analytics, 122
pricing, 140
strategic, distinctive capability of, 48
supply chain management at, 147–149, 260
talent management at, 125, 128
Wayman, Bob, 97
Wealthfront, 172
web analytics, 242
welfare fraud detection, 82
Werner, Tom, 38
WPP plc, 136
Wynn, Steve, 79
Yankee Group, 140
YouTube, 255
Zillow, 5
Zimmer Biomet, 97