- 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, –10
- adaptive development, 128, 136
- agile development, , 128
- agile processes, 125–126
- AI Ladder
- about, 121–122
- adapting to retain organizational relevance, –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, –2
- DevOps/MLOps, 136–139
- Execute, in agile processing, 130–133
- Operate, in agile processing, 133–136
- rungs of, –7
- technology focus areas,
- 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, , 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,
- 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, –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, –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,
- 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,
- 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, –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, , 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, , 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, –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,
- 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,
- 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,
- 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,
- 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, –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, –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, , 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, –10
- organizations, considerations for ones using AI, 17–33
- Organize rung, of AI Ladder, –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,
- 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, , 26, 110–111, 115–117, 245
- temperature, 214
- TensorFlow, 225
- test data, 81
- text-based representations of data, 38–40
- themes (AI Ladder), –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, –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
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