Index

  • A
    • A-B testing, 137
    • abstraction, in OSAPI acronym, 205
    • accelerated processing unit (APU), 237
    • accessibility, of data, 79, 183–184, 257
    • accuracy, of data, 134
    • Ackoff, Russell, 148
    • action, 154–156, 245–247
    • active learning, 11, 12, 13
    • Adams, Douglas (author), 31, 244
    • adapting, to retain organizational relevance, 8–10
    • adaptive development, 128, 136
    • agile development, 7, 128
    • agile processes, 125–126
    • AI Ladder
      • about, 121–122
      • adapting to retain organizational relevance, 8–10
      • AI-centric organization, 14–15
      • AIOps, 136–137, 142–144
      • Create, in agile processing, 128–130
      • data-based reasoning, 10–14
      • DataOps, 136–137, 139–142
      • development of, 1–2
      • DevOps/MLOps, 136–139
      • Execute, in agile processing, 130–133
      • Operate, in agile processing, 133–136
      • rungs of, 4–7
      • technology focus areas, 3
      • time and, 122–128
    • AI-centric organization, 14–15
    • AIOps, 121, 136–137, 142–144
    • Airflow, 110
    • aligned zone, 93–98
    • AlphaGo (DeepMind), xxi
    • AlphaZero (DeepMind), xxi
    • American National Standards Institute (ANSI), 62
    • analysis paralysis, 65
    • analytic intensity, 237–238
    • analytics
      • about, 1, 57, 87
      • adding zones, 107–108
      • as an essential element of AI, 228–244
      • big data as normal data, 77–81
      • community of interest in, 89
      • data lakes, 69–74
      • data topologies, 100–106
      • elements of data lakes, 75–77
      • enabling zones, 108–118
      • enterprise data warehouse (EDW), 57–64
      • expanding zones, 107–108
      • leaf zones, 108
      • modern environments, 69–74
      • moving zones, 107–108
      • need for organization, 87–100
      • paradigm shift, 68
      • removing zones, 107–108
      • schema-on-read approach vs. schema-on-write approach, 81–84
    • Analyze rung, of AI Ladder, 5
    • anti-pattern, 200
    • Apache Kafka, 110, 115, 117, 252
    • Apache Nifi, 252
    • Apache Spark, 78–79, 115, 209, 252
    • API Connect, 117
    • Apollo 13 mission, 15
    • ApplicationMasters, 75–76
    • “Applied Organization Change in Industry” (Leavitt), 245
    • architecture, design and, 29
    • artificial general intelligence (AGI), 46
    • artificial intelligence (AI). See also specific topics
      • about, 1–2, 35–36, 223–224
      • accessibility of data, 52–54
      • accuracy of data, 52–54
      • advances in, xxi
      • considerations for organizations using, 17–33
      • context, 38–42
      • curated data, 52–54
      • data governance, 43–46
      • data quality, 43–46
      • data-driven decision-making, 18–23
      • democratizing data/data science, 23–25
      • development efforts for, 224–228
      • displaying data, 38–42
      • driving action, 245–247
      • encapsulating knowledge, 46–49
      • essential elements of, 228–244
      • ethnography, 42–43
      • facilitating reaction time, 29–30
      • fairness in outcomes, 49–52
      • keeping it simple, 248–250
      • ontologies, 46–49
      • open platform capabilities, 256–259
      • organizing data, 26–29, 52–54
      • personalizing user experience (UX), 36–38
      • preparing data for, 2–3
      • questioning everything, 30–32
      • silos, 250–252
      • specialized data, 42–43
      • taxonomies, 252–256
      • transparency in outcomes, 49–52
      • trust in outcomes, 49–52
      • uses of, 17–18, 188–189
    • Artificial Intelligence: A Modern Approach, 3rd edition (Russell and Norvig), 12
    • assertions, expressed by ontologies, 47
    • Assessment Methods in Statistical Education: An International Perspective (Schields), 216
    • asset, managing data as an, 175–183
    • assure, in data governance, 44
    • asterisks, 96
    • atomic piece of data, 60
    • Atomicity, 60
    • atomicity, consistency, isolation, and durability (ACID), 243
    • attributes of difference, 73–74
    • automation, 144
    • averaging, 240
  • B
    • barriers, in agile processing, 129
    • Basel Committee on Banking Supervision (BCBS) 239 standard, 158
    • batch processing, 250
    • best practices, 67
    • bias, erasing from insight, 50
    • bidirectional communication flows, 239–240
    • big data, xxvi, 42, 77–81
    • Big Data in Practice (Marr), 207
    • big picture, 7
    • Biggs, John (psychologist), 215
    • BigIntegrate, 110
    • binary large objects (BLOBs), 62
    • Bitcoin, 202
    • black box, 51, 118, 184
    • body language, electronic, 36
    • bottom-up approach, for data governance, 187
    • boundaries, 189
    • brain processing unit (BPU), 237
    • Brooks, Frederick, 122
    • Brunel, 116
    • business analyst, 170
    • business intelligence (BI), xxv
    • business metadata, 167
    • business skills, in data science, 218
    • butterfly effect, 200
  • C
    • Caesars Entertainment, 206–208
    • capacity to innovate, increasing, 137
    • cataloging, 166–167
    • Chapters from My Autobiography (Twain), 197
    • character large objects (CLOBs), 62
    • citizen data scientist, 23, 165
    • classification, 12
    • cleansing, 46
    • cloud computing, 105, 228–244
    • cloud enablement, 228
    • cloud object storage, 69
    • Cloudant, 114
    • cloud-fog-mist paradigm, 105
    • clustering, 12, 107
    • CockroachDB database, 58
    • Codd, Ted, 58
    • Cognos Analytics, 116
    • Collect rung, of AI Ladder, 4
    • Collis, Kevin (psychologist), 215
    • communication skills, in data science, 218
    • community of interest, 157
    • completeness, of data, 134
    • complexity, in agile processing, 129
    • compliance, in agile processing, 132–133
    • comprehensive data sublanguage rule, 59
    • compute capacity, 234–236
    • concept drift, 138
    • conceptual data model, in data warehouses, 64
    • conformity, of data, 134
    • consistency, of data, 134
    • content, interactions within organizations and, 246
    • context, 38–42, 219–221, 245
    • continuous integration and continuous delivery (CI/CD), 133–134
    • contrast, data lakes by, 71–72
    • controlled redundancy, 164
    • controls, adding, 186–187
    • core skills, in data science, 217
    • correlation, value chain through, 152–153
    • costs, reducing, 79
    • Create, in agile processing, 126–127, 128–130, 143
    • cross-validation, 197, 226
    • cryptocurrency, 202
    • culture, as a fundamental practice, 126
    • curated zone, 100
    • curation, 45, 156–159
    • current endpoint, 152
    • customer experience, enhancing, 137
  • D
    • dark data, 80, 115, 176–177
    • dashboard, 116
    • data. See also specific types
      • accessibility of, 52–54, 183–184, 257
      • accuracy of, 52–54
      • as an essential element of AI, 228–244
      • atomic piece of, 60
      • average creation rate of, xxvi
      • curated, 45, 52–54
      • decomposition of, 43, 204
      • democratizing, 23–25
      • displaying, 38–42
      • drilling down for, 43
      • evolution of, 257
      • extending value of, 206–208
      • managing as an asset, 175–183
      • maximizing use of, 147–173
      • measurability of, 257
      • organizing, 26–29, 52–54
      • ownership of, 204
      • preparing for artificial intelligence, 2–3
      • providing self-service to, 184–185
      • quality of, xxvii
      • resiliency of, 257
      • specialized, 42–43
      • streaming, 78
      • text-based representations of, 38–40
      • training, 81
      • trustworthiness of, 257
      • use of by industries, 188–189
      • value of decomposing, 43
      • valuing, 175–197
      • virtualized, 178
    • data, information, knowledge, and wisdom (DIKW) pyramid, 148, 156, 162–163, 175
    • data access, 116–117
    • data catalog, 184–185
    • data cleansing, unified data governance rules and, 112
    • data decay, 135
    • data drift, 227
    • data dumping, 167
    • data estate, 4, 180, 209, 211
    • data flow, 102, 103, 104, 251
    • data governance
      • about, 43–46, 111–112, 117
      • bottom-up approach for, 187
      • data management and, 80–81
      • value-driven data and, 159–162
    • data lakes. see also data zones
      • about, xxvi, 67, 69–71
      • attributes of difference, 73–74
      • benefits of, 70
      • by contrast, 71–72
      • data management for, 243–244
      • Dixon on, 68
      • elements of, 75–77
      • failure rate for, xxvii
      • indigenous data, 72–73
      • ingestion process and, 109
    • data leakage, 186
    • data lifecycle management, as a consideration in data governance, 159
    • data lineage, 92, 112
    • data literacy, benefits of, 213–221
    • data management
      • data governance and, 80–81
      • for data lakes, 243–244
      • for data ponds, 243–244
      • for data puddles, 243–244
    • data mart, 70–71
    • data ponds, 105–106, 243–244
    • data population, 194
    • data preparation, integrated data management and, 168–169
    • data processing, 79, 114–115
    • data provenance, 90, 92, 112
    • data puddles, 105–106, 243–244
    • data quality, 43–46, 187
    • data retention, 112–114
    • data rich, information poor (DRIP), 100, 218–219
    • data sample, 194
    • data science, 3, 23–25
    • data scientist, 169
    • data storage, 112–114
    • data swamp, 68
    • data topography, 102, 103, 105–106
    • data topologies
      • about, 100–103
      • core elements of, 102
      • data flow, 104
      • data topography, 105–106
      • zone map, 103–104
    • data virtualization, 74
    • data visualization, 38–42
    • data zones
      • about, 88, 158
      • adding, 107–108
      • aligned, 93–98
      • curated, 100
      • discovery and exploration, 92–93
      • enabling, 108–118
      • expanding, 107–108
      • harmonized, 98–99
      • leaf, 108
      • moving, 107–108
      • organizing, 252–256
      • raw, 91–92
      • removing, 107–108
      • silos, 250–252
      • staging, 90–91
    • database administrator (DBA) groups, 63
    • data-based reasoning, 10–14
    • data-driven decision-making, 18–23
    • DataOps, 121, 136–137, 139–142, 226
    • datasets, 49, 187
    • datasets, tidy, 173
    • The Data Model Resource Book, Volume 1: A Library of Universal Data Models for All Enterprises (Silverston), 65
    • Db2 Big SQL, 113
    • Db2 database, 83
    • Db2 Warehouse, 113
    • debugging AI models, 226
    • decision-makers, interactions within organizations and, 246
    • Decisions dial, in data governance, 160–161
    • decomposition, 60, 204
    • deductive inference, 11, 12
    • Deep Blue (IBM), xxi
    • deep learning processing unit (DPU), 238
    • DeepMind's AlphaGo, xxi
    • DeepMind's AlphaZero, xxi
    • delta lakes, 243
    • deployments, elements of, 258–259
    • descriptive statistics, 42
    • design, 27–29, 68, 204–205
    • design thinking, 127
    • DevOps, 121, 136–139
    • difference, attributes of, 73–74
    • directives, 44
    • Directives dial, in data governance, 160–161
    • discovery, as a fundamental practice, 127
    • discovery and exploration zone, 92–93
    • discriminator, 226
    • disruption, 8–9
    • distributed storage, 179
    • distribution independence rule, 60
    • Dixon, James, 68
    • “Does Your DBMS Run by the Rules?” (Codd), 58
    • downstream system, 170
    • Dresner, 72
    • Drill, 115
    • drilling across, 152
    • drilling down, for data, 43, 152
    • drilling up, 152
    • Drucker, Peter, 148
    • dynamic binding, 201
    • dynamic online catalog based on the relational model rule, 59
  • E
    • Earley, Seth, xxiii
    • Edgent, 209
    • ElasticSearch, 114
    • electrocardiogram (EKG), 40–41
    • electronic body language, 36
    • Eliot, T. S., 149, 150
    • eminent domain, 30
    • emotion processing unit (EPU), 238
    • encryption, unified data governance rules and, 111
    • ends and means model, 8
    • ensemble models, 11, 12, 239–240
    • ensure, in data governance, 44
    • enterprise data warehouse (EDW), xxvi, 57–67, 79. See also analytics
    • entity types, expressed by ontologies, 47
    • envision, as a fundamental practice, 127
    • epic, 7
    • ethics, 52
    • ethnography, 42–43
    • evolution, of data, 257
    • Execute, in agile processing, 126–127, 130–133, 144
    • Experiences dial, in data governance, 160–161
    • explicit value, 98
    • Extended abstract task, in SOLO model, 215
    • Extended Binary Coded Decimal Interchange Code (EBCDIC), 87
    • “Extending and Formalizing the Framework for Information Systems Architecture” (Zachman), 19
    • eXtensible Markup Language (XML), 63
    • extract, transform, and load (ETL), 65
    • extrapolation, 172
  • F
    • fairness, in outcomes, 49–52
    • fallability, human, 219
    • fat fingering, 182–183
    • feature engineering, 171–172
    • fifth-generation (5G) network, 239
    • figurative color, 214
    • fit for purpose, 220
    • 5G (fifth generation) network, 239
    • flexibility, as advantage of data lakes, 72
    • Flume, 110, 115
    • fog computing, 209, 230–232
    • Forbes, 72
    • foresight, 103
    • Forrester, 72
    • foundation rule, 58
    • “A Framework for Information Systems Architecture” (Zachman), 19
    • Franklin, Benjamin, 150
    • The Future of Management (Hamel), 128
  • G
    • garbage data, xxvii
    • Gartner, xxvi, 72, 208
    • Gaussian distribution, 193–196
    • gender, 28
    • General Data Protection Regulation (GDPR), 29, 165, 181
    • generational loss, 177
    • generative adversarial network (GAN) model, 226
    • generator model, 226
    • Gerbert, Philipp (author), 18
    • gig economy, 189
    • GitHub, 133–134
    • Glass-Steagall Act (1933), 27
    • Goldman Sachs, 27
    • good enough state, 77
    • Gorovitz, Samuel (author), 219
    • governance and integration, as a technology focus area, 3
    • granularity, 152
    • GraphDB, 107
    • gray box, 118
    • Grotius, Hugo, 30
    • guaranteed access rule, 59
    • gut feeling, 15
    • gut-brain axis, 37
  • H
    • Hadoop, 68, 75, 79
    • Hadoop Distributed File System (HDFS), 69, 109, 114
    • Hamel, Gary (author), 128
    • happy day use case, 233–234
    • hard-coding, avoiding, 200–206
    • harmonized zone, 98–99
    • hasa relationship, 84
    • HBase, 114
    • Health Insurance Portability and Accountability Act (HIPAA), 113
    • hierarchies between entity types, expressed by ontologies, 47
    • hindsight, 103
    • The Hitchhiker's Guide to the Galaxy (Adams), 31, 244
    • Hive, 78–79, 114, 115
    • Hollerith, Herman, xxv
    • holonym, 84
    • “how” metamodel, 153
    • hues, 214
    • human fallability, 219
    • human insight, importance of, 22
    • Hunter, M. Gordon (author), 66
    • hybrid cloud, 230
    • hybrid data management, as a technology focus area, 3
    • Hybrid Transactional/Analytical Processing (HTAP), 71, 208–212
    • hypernym, 84
    • hyponym, 84
    • hypothesis data, 81
  • I
    • IBM, xxi, 110, 115, 127, 232, 247, 252. See also AI Ladder
    • IBM 360, 77, 122
    • immutable object, 30
    • implicit value, 98
    • indigenous data, 72–73
    • inductive learning, 11, 12
    • industries, use of data and AI by, 188–189
    • inflight, 103, 242–243
    • information architecture (IA)
      • about, 223–224
      • development efforts for AI, 224–228
      • driving action, 245–247
      • essential elements of AI, 228–244
      • keeping it simple, 248–250
      • open platform capabilities, 256–259
      • silos, 250–252
      • taxonomies, 252–256
    • information rule, 58
    • infrastructure-as-a-service (IaaS), 79, 230
    • Infuse rung, of AI Ladder, 5–6
    • ingestion, in agile processing, 131–132
    • ingestion process, 108–111
    • Initiate Systems, 63
    • Inmon, Bill, 147
    • insight, 103
    • insure, in data governance, 44
    • integrated data management
      • about, 162–163
      • cataloging, 166–167
      • data preparation, 168–169
      • feature engineering, 171–172
      • metadata, 167–168
      • multi-tenancy, 170–171
      • onboarding, 163–164
      • organizing, 164–166
      • provisioning, 169–170
    • integrity independence rule, 60, 134
    • intelligence processing unit (IPU), 238
    • interface, data literacy and, 219–221
    • interfaced, in OSAPI acronym, 206
    • interlocking, 67
    • International Organization for Standardization (ISO), 201
    • International Space Station, 38–40
    • interoperability, 238–241
    • interpolation, 172
    • interquartile range, 42
    • interrogatives, 19, 22–23, 32, 151, 219
    • “Is Your DBMS Really Relational?” (Codd), 58
    • isa relationship, 84
    • “it depends,” 149–150
    • IT skills, in data science, 218
    • iterative aspect, 151
  • J
    • Java Database Connectivity (JDBC), 117
    • JavaScript Object Notation (JSON), 112, 209–210
    • Just So Stories (Kipling), 19
  • K
    • Kipling, Rudyard, 19, 150
    • Kiron, David (author), 18
    • K-means, 12
    • knowing-your-customer (KYC), 157
    • knowledge, encapsulating, 46–49
    • Kranz, Gene, 15
    • Kruchten, Philippe, 29
    • kurtosis, 189–193
  • L
    • late binding, 201
    • leaf level, 102–103
    • leaf zones, 108
    • learning, 11, 13–14, 127
    • Leavitt, Harold, 245
    • leptokurtic curves, 191
    • lightness, 214
    • Locally Interpretable Model-Agnostic Explanation (LIME), 136, 247
    • logical data independence rule, 59
    • logical data model, in data warehouses, 64
    • logical entities definitions, expressed by ontologies, 47
    • logical relationships definitions, expressed by ontologies, 47
    • longitudinal data, 180
    • Long-Term Evolution (LTE) network, 238
    • long-term viability, 199–213
    • low-hanging fruit, 26
  • M
    • MACEM acronym, 247
    • machine learning, activities in, 256–257
    • MacIntyre, Alasdair (author), 219
    • Mad Men, 22
    • mailing address, 60
    • MapReduce, 78–79, 115
    • Marr, Bernard (author), 207
    • mart area, in data warehouses, 64
    • Master Data Management, 63
    • Math Men, 22
    • math skills, in data science, 217
    • mean, 190, 195
    • measurability, of data, 257
    • Media Men, 22
    • median, 190, 195–196
    • Medicare Conditions of Participation (COP), 113
    • meronym, 84
    • mesokurtic curves, 191–192
    • metadata, 112, 118, 156, 167–168, 1185, 218–219
    • metamodel, 151
    • metrics, importance of, 22
    • microservices, models deployed as, 212–213
    • minimum viable product (MVP), 126, 130–131, 133
    • Mission Control Center, 38–40, 219–220
    • mist computing, 209, 230–232
    • Mizuho Securities, 182–183
    • MLOps, 121, 136–139, 226
    • mode, 190
    • Model Agnostic Contrastive Explanations Method (ACEM), 136
    • models, deployed as microservices, 212–213
    • Modernize rung, of AI Ladder, 6–7
    • MongoDB, 107, 114
    • Morgan Stanley, 27
    • multi-instance learning, 11
    • multiple versions of the truth, 155
    • Multistructural task, in SOLO model, 215
    • multitask learning, 11, 12–13
    • multi-tenancy, integrated data management and, 170–171
    • mutable object, 30
    • The Mythical Man-Month (Brooks), 122
  • N
    • Netflix, 29
    • neural processing unit (NPU), 238
    • new-collar worker, 2, 22
    • NFS Gateway, 110–111
    • NiFi, 110
    • NodeManagers, 75–76
    • nonleaf level, 102–103
    • nonsubversion rule, 60
    • normality test, 189
    • normalization, 60
    • normalized area, in data warehouses, 64
    • Norvig, Peter (author), 12
  • O
    • obfuscation, 165
    • Occupational Safety and Health Administration (OSHA), 113
    • onboarding, 163–164
    • online learning, 11, 12, 13
    • on-prem computing, 232
    • ontologies, 46–49
    • ontology, 35
    • open platform, capabilities for an, 256–259
    • OpenScale, 247
    • operate, in agile processing, 126–127, 133–136, 144
    • operational data store (ODS), 71
    • operational disciplines, 121–122. See also AI Ladder
    • operational metadata, 168
    • optimized design, 68
    • organizational relevance, adapting to retain, 8–10
    • organizations, considerations for ones using AI, 17–33
    • Organize rung, of AI Ladder, 4–5
    • overloading, 201–202
    • oversight, 44
    • Oversight dial, in data governance, 160–161
    • ownership, of data, 204
    • ownership, stability, abstraction, performance, and interfaced (OSAPI), 205–206
  • P
    • P wave, 40–41
    • paradigm shift, 68
    • peer-to-peer communication flows, 239–240
    • people, interactions within organizations and, 245
    • performance, in OSAPI acronym, 206
    • personas, 169–170
    • physical data independence rule, 59
    • physical data model, in data warehouses, 64
    • Pig, 78–79, 115
    • planning and measuring, 139
    • platform-as-a-service (PaaS), 79, 230
    • platykurtic curves, 192
    • policies and standards, as a consideration in data governance, 159
    • polyglot persistence, 208–212
    • Poor Richard, 150
    • possible for high-level insert, update, and delete rule, 59
    • post-flight, 103
    • postflight, 242–243
    • PostgreSQL, 114
    • preflight, 103, 242–243
    • Prestructural task, in SOLO model, 215
    • private cloud provider, 230
    • proactive, 44
    • properties of entity types, expressed by ontologies, 47
    • provisioning, integrated data management and, 169–170
    • public cloud provider, 229
    • punched card-processing technology. see IBM
    • PyTorch, 225
  • Q
    • QRS complex, 40–41
    • qualitative benefit, 176
    • quality, of data, 43–46, 77, 134–135, 159
    • quantified measure, 176
    • questioning everything, 30–32
  • R
    • R, 116
    • ranking datasets, 187
    • Ransbotham, Sam (author), 18
    • raw data, 74, 168–169, 181, 182
    • raw zone, 91–92
    • reaction time, facilitating, 29–30
    • reactive, 44
    • reasoning, 10–14, 127
    • reassurance, in data governance, 44
    • recursive aspect, 151
    • reduction, 129–130
    • Reeves, Martin (author), 18
    • reference architecture, 89, 100
    • regression, 12
    • reinforcement learning, 11, 12
    • Relational task, in SOLO model, 215
    • relationships, 47, 63–64, 84
    • release and deploy, 139
    • relevance, 8
    • reliance, of data in agile processing, 135
    • reorganizing the swamp, 68
    • resampling procedure, 197
    • “Reshaping Business with Artificial Intelligence: Closing the Gap Between Ambition and Action” (Ransbotham, Kiron, Gerbert and Reeves), 18
    • resiliency, 105, 233–234, 257
    • RESTful APIs, 116
    • results, 81
    • retention, data, 112–114
    • retraining, 228
    • return on assets (ROA), 176
    • return on data assets (RDA), 176
    • return on investment (ROI), 175–176
    • reusability of data in agile processing, 135
    • root level, 102
    • rules, Codd's, 58–60
    • Russell, Stuart (author), 12
  • S
    • Sanctions dial, in data governance, 160–161
    • “sandbox” environment, 92
    • saturation, 214
    • Savage, Sam (professor), 197
    • scalability, 79–80
    • schema-less-write approach, 63, 204
    • schema-on-read approach, 63, 81–84
    • schema-on-write approach, 63, 81–84
    • Schield, Milo (author), 216
    • Scikit-Learn, 225
    • security, privacy, and compliance, as a consideration in data governance, 159
    • security necessities, 184
    • self-provided, 230
    • self-service models, providing to data, 184–185
    • self-supervised learning, 11
    • semi-supervised learning, 11
    • senior manager, 169
    • sensemaking, 123
    • service level agreements (SLAs), 164
    • shift left, 138
    • silos, 250–252
    • Silverston, Len, 65
    • single data model, 78
    • single version of truth (SVOT), 147–148
    • skewness, 189–193
    • skillsets, data literacy and, 216–218
    • Society of Automotive Engineers (SAE), 202–203
    • specialized data, 42–43
    • sprint, 122
    • Sqoop, 110
    • stability, 128–129, 205
    • Stafford, William, 220
    • staging area, in data warehouses, 64
    • staging zone, 90–91
    • standard deviation, 196
    • standardization, 46
    • starter set, 89, 100
    • “Starting a New Era at Goldman and Morgan” (White), 27
    • statistics, 42, 189–197
    • storage, data, 112–114
    • storage capacity, 234–236
    • Storm, 110, 115
    • Strategic Information Systems: Concepts, Methodologies, Tools, and Applications (Hunter), 66
    • streaming data, 78
    • strikethrough, 97
    • structure, 43–44, 245
    • Structure of Observed Learning Outcomes (SOLO) taxonomy, 215–216
    • sufficient insight, 24–25
    • sufficient oversight, 24–25
    • supervised learning, 11
    • supervised learning algorithms, 12
    • sustainment, 236
    • system of engagement, 123
    • system of record, 18, 123
    • System R, 83
    • systematic treatment of null values rule, 59
  • T
    • T wave, 40–41
    • tasks, for interactions within organizations, 245
    • taxonomies, 252–256
    • Tay chatbot, 50–51
    • technical metadata, 168
    • technology, 3, 26, 110–111, 115–117, 245
    • temperature, 214
    • TensorFlow, 225
    • test data, 81
    • text-based representations of data, 38–40
    • themes (AI Ladder), 4–7
    • “There is no AI without IA” (Earley), xxiii
    • thick data, 42
    • thing-relationship-thing metamodel, 84
    • thinking skills, in data science, 218
    • third normal form (3NF), 70
    • tidy datasets, 173
    • time, 179, 208
    • timestamp, 43, 62, 122–128
    • tinker toys, 63
    • Toffler, Alvin (futurist), 28, 78
    • tokenization, 165
    • “Toward a Theory of Medical Fallability” (Gorovitz and MacIntyre), 219
    • traditional data warehouse, drawbacks of, 64–67
    • training, curating data for, 45
    • training data, 81
    • transduction, 11, 12
    • transfer learning, 11, 12
    • transformation, 8–9
    • transparency, in outcomes, 49–52
    • trust, 49–52, 225, 257
    • trust matrix, 20–22
    • Twain, Mark (author), 197
  • U
    • Unicode, 87
    • unicorn, 23
    • unified data governance, 117
    • Unistructural task, in SOLO model, 215
    • unsupervised learning, 11
    • unsupervised learning algorithm, 12
    • user experience (UX), personalizing, 36–38
  • V
    • validation data, 81
    • value
      • big data and, 80
      • creating, 45–46
      • of data, 175–197
      • of decomposing data, 43
      • explicit, 98
      • extending for data, 206–208
      • implicit, 98
    • value chain, 148–156
    • value-driven data, 148–173
    • variance, 196
    • variety, of data lakes, 69
    • vehicle area network (VAN), 239
    • velocity, of data lakes, 70
    • veracity, 70, 80
    • view-updating rule, 59
    • VINs, 202–203, 204
    • virtualized data, 178
    • visibility, in agile processing, 132
    • visual impairment intracranial pressure (VIIP), 223
    • visualizing data, 116
    • volume, of data lakes, 69
  • W
    • waivers, 161–162
    • The War of the Worlds (Wells), xxv
    • wastage, 112
    • Watson, xxi
    • Weick, Karl, 123
    • Weiner, Ed (author), 27
    • Wells, H. G., xxv
    • What, How, Where, Who, When, Why sequence, 19
    • What Goes Up: The Uncensored History of Modern Wall Street as Told by the Bankers, brokers, CEOs, and Scoundrels Who Made It Happen (Weiner), 27
    • “when” metamodel, 153
    • “where” metamodel, 153
    • White, Ben (author), 27
    • white box, 118
    • “who” metamodel, 153
    • “why” metamodel, 153
  • X
    • XML (eXtensible Markup Language), 63
    • xOps, 121–122. see also AI Ladder
    • xPU acceleration, 237–238
  • Y
    • Y2K, 203
    • Yet Another Resource Negotiator (YARN), 75
  • Z
    • Zachman, John (author), 19, 22–23, 150–151
    • Zachman Framework, 22–23, 150–151
    • Zappa, Frank, 149
    • Zeleny, Milan, 149, 153, 162
    • zone map, 102, 103–104
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset
3.133.158.36