Index

a

  • academic institutions, 322–323
  • accuracy, 25, 37, 84, 141, 190, 215, 236, 252, 295, 296, 301
  • activity-based costs, 53
  • ad-hoc reporting, 10
  • advertising, 13, 15
  • Aerospike, 97
  • AIC, 247
  • algorithmic evaluation, 235
  • algorithmic modeling, 231, 236
    • data acquisition, and cleaning, 236–237
    • feature engineering, 237–238
    • model fitting (training) and feature selection, 240–241
    • model implementation, 242
    • modeling overview, 238–239
    • model performance assessment, 242
    • model (algorithm) selection, 241–242
  • alternate solution methods, 200–206
  • Analysis of Variance (ANOVA), 124–126
  • analysis/query/drill-down, 10
  • analytical
    • capability, 1
    • ecosystem, building, 73–74
    • groups, goals of, 62
    • life cycles, 63
    • organization, 74
    • practice, 52
    • practitioners, 5
    • professionals, 57
    • skills, 56
    • specificity, 39
    • talent, 57
  • analytics applications, 313, 317, 320, 322, 324
    • Analytics 1.0, 32
    • Analytics 2.0, 32, 37
    • Analytics 3.0, 32
  • analytics-based system, 45
  • analytics categories, 6
    • descriptive, 7–10
      • data modeling, 7
      • reporting, 10
      • software, 10
      • visualization, 10
    • predictive, 10–14
      • data mining, 11
      • forecasting, 11
      • leveraging expertise, 12–14
      • pattern recognition, 11
      • predictive modeling, 11
      • simulation, 11
        • models, 13
    • prescriptive analytics, 14–16
  • Analytics Certification Board, 50
  • analytics-focused software developers, 317–319
    • predictive analytics, 318
    • prescriptive analytics, 318–319
    • reporting/descriptive analytics, 317–318
  • analytics industry analysts, and influencers, 321–322
  • analytics industry ecosystem, 312–325
    • business analytics professionals, 312
    • communicator, 312
    • mathematicians, 312
    • modeler, 312
    • programmer, 312
  • analytics initiative, 49
  • analytics methodology, for life cycle management, 276–303
  • analytics model, deployment, 298–300
  • analytics practitioner
    • macro-solution methodologies, 106
  • analytics problem framing, 278, 283–287
  • analytics problem statement, 284, 286–289, 292–293, 295
  • analytics process, 3, 78
  • analytics professional (AP), 1, 24, 275–284, 286–288, 291, 293–296, 298–300, 303–309
  • analytics project, 17, 142
    • desired outcome, 145
    • documentation, 148
    • finding the right answer, 148–149
    • lack of perceived success, 148–149
    • life cycle, 293
    • null hypothesis, 146
    • OR field, 145
    • real-world, 101
    • research-and-discovery-leaning, 109
    • software and tool selection, 142–143
    • systematic solution methodologies, 145
  • analytics solution, determine, problem amenable, 281
  • analytics space, 314
  • analytics/technical skills, 57
  • analytics to detect new fraudulent claims, 45
  • analytics user organizations, 323–325
  • analytics within organizations, 16
    • communicating analytics, 21–
    • organizational capability, 21–23
    • projects, 17–19
  • analytic teams, 5
  • analyzing data, 2
  • annual performance assessments, 58
  • ANOVA, 104, 124, 125
  • anti-discrimination laws, 24
  • AP. See analytics professional (AP)
  • application developers, industry-specific/general, 319–321
    • analytics software providers, 319
    • data aggregators, 319
    • data infrastructure, 319
    • data warehouse, 319
    • middleware, 319
  • ARENA, 319
  • Aristotle, 106
  • ARMA methods, 138, 140
  • ARPANET configuration, 136
  • artificial intelligence (AI) systems, 1, 52, 56, 231, 234
  • Asset ID, 93
  • assigned customers, 67
  • Associate CAP (aCAP), 49
  • audio, 39
    • files, 38
  • auditability, 25
  • audit cycle, 82
  • automated control protocol, 156
  • automated data collection, 82
  • automation, 85
  • automobile insurance, 45
  • automotive manufacturers, 15

b

  • backpropagation, 264
  • Bacon, Francis, 106
  • bad data, 39
  • balancing bias, 245–247
  • bank’s branch network, 53
  • Baker, S., 322
  • BIC, 247
  • bias-variance trade-off, 243
  • big data, 1, 3, 5, 9, 33, 52, 59, 74, 95, 120, 121
  • binary/binomial classification, 233
  • binning, 89, 90
  • box-and-one approach to teaching with cases, 343–344
  • black box, 11
  • breadth, 39
  • Brown, G., 214
  • bureaucracy, 62
  • business, 66
  • business knowledge, 50
    • and design skills, 50
  • business-oriented translators, 51

c

  • C, 207
  • C++, 95
  • call centers, 74
  • CAP Certification. See Certified Analytics Professional certification
  • CAP Job Task Analysis (JTA), 277
  • case study
    • alternate solution methods, 200–206
    • portfolio optimization, solved by a variety of methods, 178–181
    • traveling salesman problem, 200–206
  • Cassandra, 97
  • centralization, 61, 62
  • centralized analytics groups, 62, 69
  • CEP. See complex event processing
  • certification agencies, 322–323
  • certification programs, 49, 322
  • Certified Analytics Professional (CAP) certification, 49, 58, 277, 299, 301, 323
  • changing world of analytics, 25–28
  • Chief Analytics Officers (CAO), 23, 70
  • Chief Data Officer, 70
  • Chief Data Scientists, 70
  • Chief Executive Officer, 324
  • Chief Information Officer, 324
  • citizen data scientist, 311
  • Clark, Robert, 96, 312
  • classification problems, 232, 233
  • cleaning data, 28
  • cleansing, 36
  • C-level positions, 1
  • client, 160
  • cloud computing, 315
  • cluster documents, 234
  • clustering methods, 233
  • coaching, 51
    • capabilities, 52
  • Codd, E.F., 93
  • cognitive
    • burden, 10
    • computing, 1
    • technologies, 52
  • Cognos software, 10
  • cohesion, 58
  • COIN-OR, 143
  • collaboration, 35, 41
  • collecting data, 2
  • columnar databases, 96
  • combinations, 162
  • commercial analytics group, 74
  • communicating analytics, 21
  • communication, 10, 303, 305–306
  • communities, 1, 62, 67
  • competition, 15
  • competitive advantages, 32
  • complex event processing (CEP), 319
  • complex queueing system, 130
  • computational infrastructure, 39
  • computational power, 5
  • computer
  • conceptual framework, 3
    • data-centric analytics, 3
  • conditional probability, 163
  • conduct data-driven experiments, 54
  • confidentiality, 38
  • confusion matrix (truth table), 249, 250, 252
  • consulting firms, 32
  • consulting skills, 51
  • contextual knowledge, 5
  • continuous data, 79
  • coordination approaches, 65–66
  • coordination mechanisms, 66–67, 70
  • corporate litigation, 56
  • correction, 36
  • cosine similarity, 272
  • cost, 8
  • cost-effective, 37
  • credibility, 14, 53
  • credit score, 320
  • CRISP-DM. See cross-industry standard process for data mining
  • critical path method (CPM), 142, 176–178
    • Gantt chart (deterministic, descriptive), 177
  • cross-channel analytics, 74
  • cross-channel perspective, 74
  • cross-functional team, 2
  • cross-industry standard process for data mining (CRISP-DM), 112, 276
    • CRISP-DM diagram, 277
    • CRISP-DM methodology, 278
    • CRISP-DM Phase 1, 278
    • CRISP-DM Phase 2 and 3, 288–289
    • CRISP-DM Phase 4, 293–294
    • CRISP-DM Phase 5, 294–297
    • CRISP-DM Phase 6, 297–298
    • methodology, 106, 112–113, 137, 140
      • business understanding, 112–113
      • data preparation, 113
      • data understanding, 113
      • deployment, 113
      • evaluation, 113
      • modeling, 113
      • OR project method, 113
      • steps of, 112
  • cross-sectional data, 79
  • CRUD cycle, 98
  • customer data integration (CDI), 98

d

  • Dantzig, G., 181
  • data
    • about data, 98
    • access to, 40
    • acquisition, and cleaning, 236, 290–291
    • analysis, 2
    • analytics, 311–312
    • captured, 38, 98
    • cleaning, 17
    • collecting and applying analytics business, 45
    • collection, 42, 77, 108, 109, 314
    • culture, 42
    • discovery, 80–86, 81
    • driven decisions, 49
    • elements, 8
    • exhaust, 8
    • exploration, 8, 10
    • extrapolation, 217–218
    • generation, 314
      • infrastructure providers, 314–315
    • governance, 38
    • harmonization, rescaling, cleaning, and sharing, 291–292
    • integrity, 217
    • interpolation, 217–218
    • literate, 55
    • management, 50, 52, 97–98
      • infrastructure providers, 315
        • big data space, 315
        • cloud computing, 315
        • SQL server, 315
      • oriented employees, 57
      • skills, 50
    • master, 98
    • mining, 51
      • projects, 276
    • modeling, 8, 93
      • nonrelational databases, 95–97
      • relational databases, 93–95
    • need and sources, identifying and prioritizing, 290
    • numeric, 55
    • old, 45
    • potential sources, 8
    • process, 38
    • products, 52
    • profiling, 86, 87
    • quality control, 39
    • quantity, 37
    • reduction, 92
    • relationship identification, 292–293
    • reporting, 10
    • resources, 4
    • rules, for usage (See rules, for data usage)
    • science, 1, 2, 59, 61
      • skills, 51
      • teams, 62
    • scientists, 36, 38, 52, 54, 61, 62, 63
      • skillset, 40
    • security, 59, 323
    • semistructured, 37
    • sensor, 43
    • service providers, 316–317
    • sets
      • variability in size and information density, 9
    • small, 27
    • squashing, 93
    • stewards, 98
    • storage, 84
    • structure, 37, 291–292
    • thick, 5
    • transmission errors, 121
    • “typical” sets, 41
    • useful, 37
    • visualization, 10, 55, 317
    • warehouse providers, 316
  • database
    • computing, 315
    • key-value pair, 37, 97
  • data-centric approach, 3, 4, 5, 6
  • data-centric groups, 5
  • data preparation, 86–93
    • specialists, 51
  • data types, 77
    • binary data, 78
    • continuous data, 79
    • cross-sectional data, 79
    • nominal data, 77
    • ordinal data, 78
    • panel data, 79
    • qualitative data, 77
    • quantitative data, 79
    • spatial data, 79
    • time series data, 79
    • unstructured text data, 80
  • data understanding phases, 303
  • Davenport, T., 322
  • Davenport identifies Analytics 1.0, 2.0, and 3.0, 31
  • decentralization, 62, 63
    • approach, 63
    • direction, 62
  • decimal scaling, 91
  • decision-centric analytics, 4
  • decision-centric approaches, 4, 5, 6
  • decision-centric framing, 5
  • decision-centric organizations, 5
  • decision-makers, 5, 15, 53
  • Decision theory, 184–187
  • decisions, 5, 37
    • better, 2
    • levers, 15
    • making rules, 50
    • making space, 235
    • process, 41
    • theory, 184–187
      • skating competition, 186–187
    • tree, 129
    • variables, 132
  • deep learning, 27, 52
  • deliver project model, 299
  • demand vs. nonfill percentage, 123
  • density methods, 233
  • deployment
  • descriptive analytics, 10, 52
  • descriptive–predictive–prescriptive analytics paradigm, 105
  • deterministic models, 161–162
  • developing talent, 58–59
  • digital food pantry, 122
  • digital simulation, 173–174
    • coin toss simulation (stochastic, descriptive), 173
    • static vs. dynamic simulations, 174
  • dimension reduction, 233
  • disciplines, 1, 50, 105, 106, 312
    • scientific, 106
    • using analytics in research and practice, 144
  • discrete data, 79
  • discrete event simulation, 130
  • disease class, 233
  • distance education, 59
  • DIY software, 142
  • documentation, 39, 116, 142, 148, 166, 170, 206, 216, 290, 297, 303–305
  • document-oriented database, 97
  • domain
    • expert, 35
    • knowledge, 5
  • Drucker, P., 215
  • dual solution, 183
  • Duhigg, C., 322
  • dynamic programming, 195–196

e

  • Eckerson, W., 322
  • economic order quantity (EOQ), 162, 174
  • economic variables, 15
  • ecosystem exchange information, 314
  • education level, 24
  • effective data management programs, 98
  • Einstein, A., 208, 228
  • electric utilities, 85
  • electronic medical records (EMR), 319
  • e-mail, 38
  • e-mail addresses, 89
  • EMR. See electronic medical records
  • engineering, 15
  • Enterprise Data, 59
  • Enterprise Miner, 318
  • ethical implications, 23–25
  • ethics, 25, 26
    • guidelines on, 26
  • execution performance, 40
  • executive sponsor, 35
  • executives, 57, 160
  • experimentation skills, 52
  • explainability, 25
  • extract, transform, and load (ETL) procedures, 81

f

g

  • game theory, 15, 181–184
  • generic data models, 8
  • Gladwell, M., 322
  • Go Grandmaster, 235
  • Google, 55, 73, 314, 315, 317, 321
  • Google Earth, 221, 228
  • Google Maps, 158
  • Google’s recruiters, 55
  • government agencies, 309
  • Government Performance and Results Act, 309
  • granular (disaggregated) data, 38
  • granularity, 38, 45
  • graph-based models, 138
  • graph database, 37, 97
  • Gross Domestic Product (GDP), 14
  • Guidelines on ethics, INFORMS, 26

h

  • hackers, 23
  • Hamming, R., 227
  • hardware, 54
  • Harris, Jeanne, 55, 62, 64
  • high-demand resource, 61
  • high-quality analytical work, 49
  • high-velocity analytics, 43, 44
    • for quick response to customers, 44
    • to save maintenance costs, 44–45
    • to save operating costs, 43–44
  • Hughes’ Salvo Model of Combat, 192–193
    • equations, 192
  • human analytical resources, 49
  • human decision-making, 53
  • human resources (HR), 59
    • leadership, 59
    • organizations, 59

i

j

  • Jaccard Index, 271
  • Java, 95
  • Java Script Object Notation (JSON), 97
  • job task analysis (JTA), 277
    • JTA domain I task 1, 278–283
    • JTA domain I task 2, 279–280
    • JTA domain I task 3, 281
    • JTA domain I task 4, 281
    • JTA domain I task 5, 281–282
    • JTA domain I task 6, 282–283
    • JTA Domain II, Task 1, 283–285
    • JTA Domain II, Task 2, 285–286
    • JTA Domain II, Task 3, 286
    • JTA Domain II, Task 4, 287
    • JTA Domain II, Task 5, 287–288
    • JTA Domain III, Task 1, 290
    • JTA Domain III, Task 2, 290–291
    • JTA Domain III, Task 3, 291–292
    • JTA Domain III, Task 4, 292–293
    • JTA Domain III, Task 5, 293
    • JTA Domain III, Task 6, 293
  • JTA. See job task analysis (JTA)

k

l

  • Lanchester models of warfare, 189–192
    • Warfare, Lanchester Models, 189–192
    • aimed fire square law, 190
    • area fire linear law, 190–191
    • simulation, 191–192
  • Lanchester, F.W., 189
  • Lanchester's Square Law, 191–192
  • language system operator’s, 160
  • leadership, 62
    • sponsors, 17
  • Lean Six Sigma certifications, 325
  • life cycle management, 275–276, 303–309
  • life cycle of analytics projects, 18
  • linearized feasible region, 134
  • linear programming (LP), 133
    • classification models, 138, 139
    • clustering models, 138, 139
    • generalized linear models, 138
    • graph-based models, 138, 140
  • LinkedIn, 320
  • Loess Regression, 91
  • Loh, Wei-Yin, 259

m

  • machine age, 3
  • machine learning, 1, 11, 27, 57, 231, 232, 234, 235
    • goals and guiding principles in, 235–236
    • practitioners, 231
  • macro-methodology. See macro-solution methodologies
  • macro-solution, 103
  • macro-solution methodologies, 103, 106, 144, 146
    • analytics project, 114–116
    • cross-industry standard process for data mining (CRISP-DM) methodology, 112–113
    • operations research project methodology, 109–112
    • relationship, 115
    • scientific method, 145
    • scientific research methodology, 106–109
    • software engineering-related solution methodologies, 114
    • take-home message, 116
  • make to order (MTO), 85
  • make to stock (MTS), 85
  • management process, 17
  • management science/operations research (MS/OR) software, 318
  • management skills, 51
  • managers, 51
  • manufacturing, 15
  • marketing, 15, 32, 36, 50, 64, 69, 73, 304, 321, 322
  • market research, 4
  • master data, 98
  • master data management (MDM), 98
  • mathematical method, 2
  • mathematical model, 158
  • mathematical optimization, 127, 174–175
    • economic order quantity, 175
  • mathematical programming techniques, 131–133
    • discrete, combinatorial, and network optimization, 135
    • integer programming, 135
    • linear programming (LP), 133
    • mixed integer programming, 135
    • nonlinear programming (NLP), 133
  • mathematicians, 312
  • mathematics, 1
  • MATLAB, 143, 318
  • matrix, 67–69, 92, 172, 181, 183, 184, 195, 249
  • McDonald, Bob, 54
  • mean squared error (MSE), 248
  • mean absolute deviation (MAD, aka mean absolute error (MAE), 248
  • measurement units, 175–176
    • units in expressions, 176
  • medical images, 39
  • metadata, 98
  • methodology
  • metrics, 308–309
    • creation and usage, 301–303
    • Government Performance and Results Act 1993, 309
  • micro-methodology. See micro-solution methodologies
  • Microsoft EXCEL, 143
  • micro-solution methodologies, 103, 141, 144
    • description framework, 117–118
    • for exploration and discovery, 119–126
    • preliminaries, 116–117
    • pseudo- or quasi- forms, 135
    • techniques to find solutions
      • dependent on data, 137–141
      • independent of data, 127–137
  • middle managers, 54
  • middleware providers, 316
  • min–max, 91
  • missing at random (MAR), 88
  • Minitab, 143
  • missing completely at random (MCAR), 88
  • missing values, 39, 87
  • mixed-integer programming, 318
  • model, 155, 159
    • counting, 162
    • deterministic/stochastic, 161–162
    • development, 235
    • documentation (See model documentation)
    • error, 243
    • exponential, poisson, and memoryless, 171
    • failure, objective criteria, 160
    • fitting, 243, 245–247
      • in business, 68
        • home location, 68
        • work location, 68
    • formulation, 206–207
    • goals shifting, 160
    • map, 156
    • mathematical, 158
    • obsolescence, 226–227
    • physical, 157
    • prescriptive, 15
    • problem and improtance/solution, 159–160
    • queueing, 170
    • solutions, 224–226
    • success
      • objective criteria, 160
    • technique, 159–160
    • time series, 138
    • types of, 161
      • descriptive, 161
      • predictive, 161
      • prescriptive, 161
    • validation, 218–220
    • verification, 218–220
      • comparing models, 218–219
      • data diagnostics, 220
      • data provenance, 220
      • data vintage, 220
      • sample data, 220
  • model documentation, 206
    • with different methods, 211–212
    • with different variables, 212–213
    • extensibility, 214–215
    • formulation, 206–207
    • implementation language, choice of, 207
    • model fidelity, 208–210
    • reliability, 213
    • scalability, 213
    • sensitivity analysis, 210–211
    • stability, 213
    • supervised vs. automated models, 207–208
  • modeler, 24, 159, 312, 318
  • Modeling General Motors, 15
  • MongoDB, 97
  • Monte Carlo simulation, 15, 54, 130
  • Morison, Bob, 62, 64
  • MS/OR. See management science/operations research
  • multiclass/multinomial classification problems, 233
  • multi-variate analysis of variance (MANOVA), 104, 124, 126
  • Murray, Desmond, 59

n

  • National institute of Standards and Technology (NIST), 323
  • natural gas, 85
  • network models, 127
  • neural networks, 138, 140
    • deep learning, 264
    • recurrent neural networks, 264
    • software, 318
  • new data, 10, 38, 39, 45, 235, 237, 260
    • management, 52
  • new technical skills, 52
  • Newton's second law, 161
  • NIST. See National institute of Standards and Technology (NIST)
  • nominal data, 77
  • nondeterministic polynomial (NP) time, 133
  • nonlinear programming (NLP), 133
  • nonrelational database technologies, 80
  • nonstandard values, 88
  • normalization, 91
  • North American Automobile Market, 15
  • NoSQL database, 95
  • not missing at random (NMAR), 87
  • number in line, 131

o

  • obtain/receive problem statement and usability, 278–279
  • obtain stakeholder agreement on business statement, 282–283
  • Ohm’s law, 219–220
  • Oklahoma State University, 324
  • old and new data plus analytics
    • to decrease crime, 45
  • online channels, 74
  • online social media, 320
  • operational analytics, 52
  • operational data store (ODS), 81
  • operations data, 44
  • operations research, 1
  • operations research project methodology, 106
  • optimization, 7, 15, 39, 81, 104, 110, 127
  • ordinal data, 78
  • organization, 16, 23, 49, 53, 57, 58, 62, 63
    • capability, 21–23
    • commitment, 61
    • decision-making, 2
    • designs, variables for tuning, 68
    • IT functions, 5
    • requirements for analytical capabilities, 49
    • structures, 70, 293
      • rule, 63
  • organizing analytics, basic models for, 63
    • center of excellence model, 65
    • centralized model, 63
    • consulting model, 64
    • decentralized model, 65
    • functional or “best home” model, 64
  • ORION, 52
  • OR/MS textbook, 109
  • OR/MS Today, 228, 319
  • OR project methodology, 110, 111, 112
  • ORSA/TIMS conference, 132
  • overfit, 243

p

  • panel data (or longitudinal data or cross-sectional time series data), 79–80
  • payoff matrix, 181, 184
  • peer review, 100, 108
  • permutations, 162
  • pervasive data, 1, 5
  • physical model, 157
  • PI data historian, 85
  • planners, 160
  • point of sale (POS) systems, 85
  • portfolio optimization
    • assessing our progress, 179
    • heuristic, 179
    • relaxations and bounds, 179–180
    • simple optimization problem, 180
  • post-deployment activities, 301–303
  • powerful computation, 1
  • predictive analytics, 57, 58
  • predictive performance evaluation, 247–248
    • classification performance, 249–253
    • performance evaluation for time-dependent data, 253–254
    • regression performance, 248–249
  • prescriptive analytics, 15, 57
  • price, 8
  • primary key, 93
  • principal components analysis (PCA), 92, 234, 270
  • principle of optimality, 195–196
  • privacy, 23, 33, 38, 281, 290, 321, 323
  • private sector organizations, 309
  • probability, 163
    • Bayes theorem, 163–164
    • binomial model of coin tosses, 164
    • independence assumption, 163
    • models, 127
    • multiplication rule, 163
    • perspectives and subject matter experts, 165
    • synonyms for, 164
  • probability models, 127
  • problem
  • problem-solving, 99, 100, 102
    • research on operations, 137
    • visualization, 143–144
  • product design-to-market cycles, 32
  • product development, 52, 73
  • product information management (PIM) tools, 98
  • productionization, 17
  • professional analysts, 54
  • professional communities, 5
  • profitability, 15, 16, 22, 53
  • program evaluation and review technique (PERT), 142
  • Program Management Office (PMO), 65, 66, 67
  • programmers, 51
  • projects, 17–20
    • based funding, 69
    • manager, 35
    • planning, 142
  • public sector organizations, 295
  • purchasing decision, 41
  • Pyomo, 207
  • Pyramid of Analytic Knowledge, 60
  • Python, 143, 207, 228

q

  • qualitative data, 77–79
  • quality, 38
    • control, 39
  • quantitative analysts, 50
  • quantitative analytics professionals, 17
  • quantitative data, 79–80
  • quantitative methods, 105
  • quantitative skills, 50
  • queueing models, 127
  • query, 94
  • queueing theory, 128

r

  • R, 207, 318
  • RapidMiner, 318
  • rationalization, 70
  • real goal, 42
  • real-life, 15
  • real-time traffic information, 320
  • real-time transaction data, 27
  • Receiver operating characteristics (ROC) curves, 250–251
  • recidivism models, 24
  • refine problem statement, and delineate constraints, 281
  • regularization, 247
  • regression, linear least-squared error, 167–169
  • regression model error, components of, 243–245
  • regression problems, 232
  • regulators, and policy makers, 323
    • Federal Trade Commission (FTC), 323
    • information and communication technologies (ICT), 323
    • International Data Corporation (IDC), 311
    • International Telecommunication Union (ITU), 323
    • National Institute of Standards and Technology (NIST), 323
  • reinforcement learning, 233, 234
    • problems, 232
  • relational database, 37, 93–95
  • relational database management system (RDBMS), 93
  • relationship skills, 51
  • reporting, 10
    • ad-hoc, 10
    • structure, 69
  • resource utilization, 131
  • response functions, 233
  • responsibility, 17, 24, 25, 75, 276, 298–300
  • revolution analytics, 318
  • risk management, 62
  • Roberts, Greta, 56–57
  • rolling-horizons design, 253–254
  • root mean squared error (RMSE), 248
  • Rose, Robert, 2
  • rotation, 67
  • R package, 143
  • “R-squared” metric, 12
  • rules, for data usage
    • copyright, 216
    • data integrity, 217
    • Department of Defense, 216
    • Department of Energy, 216
    • Institutional Review Board (IRB), 216
    • law enforcement data, 216
    • licensed data, 215–216
    • model outputs, displays of, 217
    • multiple data evolutions, 217
    • paraphrased and plagiarized data, 217
    • personally identifiable information (PII), 216
    • proprietary data, 215
    • Protected Critical Infrastructure Information System (PCIIMS), 216
    • trademark, 216

s

  • sale systems, 314
  • Salford Systems, 318
  • SAP system, 110, 316
  • SAS, 112, 113, 143, 276, 316, 317, 318, 319, 322
  • saving operating costs, 43
  • Scheinberg, Katya, 264
  • Schramm, Harrison, 43, 80
  • scientific experimental design, 55
  • scientific method. See scientific research methodology
  • scientific research methodology, 106–109
  • scoring function, 234
  • search theory, 189
    • area search (stochastic, predictive), 189
  • security, 17, 19, 23, 25, 35, 38, 89, 95, 98, 268, 290, 313, 320
  • segment-by-segment decisions, 15
  • semistructured data, 37, 97
  • semisupervised learning, 232
  • SEMMA (Sample, Explore, Modify, Model, and Assess), 276
  • senior management team, 54
  • sensitivity (recall, true positive rate (TPR), or detection probability) vs. specificity (true negative rate), 250
  • sensor data, 43, 44
  • sentencing decisions, 24
  • shared services, 72–73
  • sharing, 41
  • shelf life, 37
  • SIGDSA. See Special Interest Group on Decision Support and Analytics (SIGDSA)
  • Silicon Valley, 52
  • simple random sample (SRS), 92
  • simple random sampling without replacement (SRSWOR), 92
  • simple random sampling with replacement (SRSWR), 92
  • simplex method, 132
  • simulation, 7, 11, 15, 55, 81, 82, 111, 127
  • single-use models, 193–195
    • compound interest and net present value, 193
    • cost to maintain safety stock, 194–195
  • skewness, 90
  • skills, 50, 55, 57
    • inventory, 57
  • small data, 9, 27
  • smart humans, 53
  • smart machines, 53
  • smartphones, 320
  • social media, 44, 80
    • data, 38
    • driven content, 95
  • social network analysis, 320
  • social security numbers, 89
  • softer questions, 5
  • software, 10, 41, 54, 57, 227–228
    • engineering methodology, 106
    • selection
      • analytics project, 142–143
    • skill, 50
    • tools, 227
  • software engineering-related solution methodologies, 114
    • design, 114
    • implementation, 114
    • maintenance, 114
    • requirements, 114
    • verification, 114
  • solution methodologies
    • analytics breakdown, 104
    • defined, 99, 100
    • implementation, 102
    • macro/microlevel analytics, 103
    • vs. products, 101–103
  • sophistication, 7, 39
  • spatial data, 79
  • Special Interest Group on Decision Support and Analytics (SIGDSA), 322
  • sports analytics, 319
  • Spotfire software, 10
  • SPSS, 112, 113, 143, 276
  • SQL Server BI tool kit, 317
  • stack-based enumeration, 197
    • combinations, 199–200
    • data structures, 197–198
    • generating permutations and combinations, 199–200
  • Stackelberg game, 182
  • staff development skills, 51
  • stakeholder agreement, obtainment, 287–288
  • stakeholders, 36, 42
  • standardization, 36
  • standard reporting, and dashboards, 10
  • standard systems design, 299
  • Starbucks network, 44
  • statistical learning, 231
  • “statistically significant” effect, 11
  • statistics, 1, 53
    • analysis of data, 166
    • descriptive statistics, 166
    • inferential, 169–170
    • method, 2
    • models, 50
    • parameter estimation with confidence interval, 166–167
    • random sample, 166
    • regression, 167–169, 233
  • Statsoft, 318
  • Stephens, Eric, 19–20, 325–326
  • stochastic gradient descent, 240
  • stochastic models, 127, 161–162
  • stochastic process, 128, 170–173
    • exponential, poisson, and memoryless models, 171
    • Markov chains (stochastic, descriptive), 171–172
    • M/M/1 queue (stochastic, descriptive), 172–173
    • queueing model, 170–171
  • stock keeping units (SKUs)
    • pairwise correlation coefficient, 123
  • stored procedures, 95
  • stress testing, 308
  • string testing, 308
  • structured data, 37, 38
  • structured formats, 63
  • structured query language (SQL), 93
    • SQL–a query language, 50, 97
    • SQL server, 315, 317
  • subject matter expert/expertise (SME), 4, 5, 15, 17, 81, 161, 165–166
  • sum of squared errors (SSE), 247
  • supervised learning algorithms, 254
    • artificial neural networks, 262–264
    • classification and regression trees, (CART), 257–259
    • ensemble methods, 265–267
    • extensions to regression, 256–257
    • KNN (k-Nearest Neighbors) algorithm, 255–256
    • overview, 254–255
    • support vector machines, (SVM), 261–262
    • time series forecasting, 259–261
  • supervised learning methods, 235
    • selection, and deployment, 235
  • supervised learning problems, 232
  • Supervisory Control and Data Acquisition (SCADA), 84
  • supply chain software, 101
  • susceptible, exposed, infected, recovered (SEIR) epidemiology, 187–189
    • deterministic/predictive, 188
  • system, 155
    • dynamics simulation model, 15
    • function, 156
    • operators, 156, 159
      • language, 160

t

  • Taber, Alan, 2, 280
  • Tableau software, 10, 316, 317, 322
  • talent map, 57
  • team budget, 41
  • technical factors, 39
    • selecting analytic tools, 39
  • technical skill, 50
  • telecommunications industries, 55
  • teleprompter, 11
  • temporal alignment, 39
  • Teradata, 276, 315, 316, 319, 322
  • testing, 307–308
    • string/stress, 308
  • text data, 80
  • thick data, 5, 9
  • time flow mechanism, 130
  • time series data, 79
  • time series models, 138, 140, 259–260
  • tool selection, analytics project, 142–143
  • trade-off, 37
  • traditional quantitative analysts, 63
  • traditional statistical methods, 27
  • training program, 58
  • transaction-oriented systems, 83
  • transformation, 36, 54
  • Transmission Control Protocol (TCP), 207
  • traveling salesman problem, (TSP), 200–206
  • trucking company, 43
  • trusting relationship, 53
  • type I errors (false positive errors), 169, 170, 196, 250
  • type II errors (false negative errors), 169, 170, 196, 250
  • Twitter, 44, 320

u

  • underfit, 243
  • units in expressions, 176
  • unstructured data, 37, 38, 39, 52
  • unsupervised learning algorithms, 267
    • association rule mining, 268–269
    • bag-of-words and vector space models, 271–272
    • clustering methods, 269–270
    • kernel density estimation, 267–268
    • principal components analysis (PCA), 270–271
  • unsupervised learning method, 233, 234
  • unsupervised learning problems, 232

v

w

  • waiting time, 131
  • Walker, Russell, 60, 306–307
  • warehouse, 10
  • web browsing data, 27
  • Web sites, 80
  • Weka, 318
  • Western capitalist culture, 102
  • Wide-Column stores, 37
  • Woolsey, G., 224
  • work ethic, 305
  • work orders table, 93
  • World Wide Web, 31

x

  • XML, 97
  • XPRESS toolbox, 143

z

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