Aasman, Jans, 19
ABox, 316–18
Agile movement, 315
Air flight, 104–6
Anderson, MD, 207
Antibodies, corporate, 307–8
App store, enterprise, 28–31
Application change
cost of, 134–35
Application code, 211–13
Application security, model-driven, 251–54
Application software, 225–26
complexity of, 69–71
Application software code
volume needed, 217–20
Application source code, 227
Application-centric approach, 2
Architecture
data-centric, 24
new delivery, 54–56
SNL data, 128
Arthur, Brian, 4
The Nature of Technology, 3
AS/400. See IBM AS/400 system
Asset
application code as, 211–13
Berners-Lee, Tim, 180
Big Data, 257–59
integration of, 190
Blockchain, 274
Brand, Stuart, 23
How Buildings Learn, 23
Business Rule industry, 264
Case studies
summary of, 143–44, 208
CBox, 316–18
Center for Health Data Innovations, 201
Change
and executive sponsorship of, 312–13
and the herd phenomenon, 118–19
governance structure for managing, 313–18
non-linear, 112–13
Christiansen, Clayton, 97, 98
Chromebook OS, 217
CIA World Fact Book, 164
Cline, Marshall, 24
Cloud, the, 261–62
Code
declarative, 245
reduction of, 227–28, 237, 238–39
software, 213–16
to schema relationship, 220–21
Code base
reduction in, 209
Code Complete (McConnell), 59
Code reduction, 237
Code reuse, 232–34
Columbus, Christopher, 103
Complexity, 61–62
example of, 72–73
measure of, 14
of application software, 69–71
reduction of, 238
schema, 228–29
Complexity math, 76–78
Computer Aided Design (CAD) system, 241
Computer Aided Software Engineering (CASE) system, 242
Consultants
and the data-centric revolution, 114
Contingency, true, 305–6
Copernicus, Nicholas, 102
Core, small, 289–90
Corporate antibodies, 307–8
Corporate evolution, 130–35
Cowboy coding practices, 315
CRUD forms, 129
Curated data, 183
Current situation, assessment of, 278–89
Data, 172
curated, 183
expressing, 150–51
inventory of, 287–88
linked, 162–69
policing of, 187
RDF, 161
resolvable, 161
self-assembling, 158
self-describing, 177–78
stupid, 84–88
types of, 31–32
Data conversions
end of, 89–90
Data Definition Language (DDL), 173
Data lakes, 259–60
difficulties in using, 86–88
Data Manipulation Language (DML), 173
Data models, 187–88
changes and, 51–53
reduction in, 90–91
simpler, 91–94
Data set
exploration of, 162
protection of, 187
Data types, 31–32
Data warehouses, 259–60, 315
Database, graph, 162
Databases
proprietary graph, 162
Data-centric approach, 1, 2
complexity reduction, 238
disruptive nature of, 97–98
flexibility of, 94–95
maturity model of, 319–24
what if view of, 88–90
Data-centric architecture, 24
Data-centric maturity, 320–24
Data-centric paradigm, 97–98, 123
Data-centric position
and being ephemeral, 16–17
vs. Data-driven, 15–16
Data-centric self-assessment, 279–82
Data-centric transformation, 320
assistance with, 113–15
Data-centricity, 327
Data-driven
vs. Data centric position, 15–16
Datascape, 122, 203
flat, 234
DBpedia triple store, 164
DDL (Data Definition Language), 316
Debt, integration, 74, 91
Declarative code, 245–46
Default sharing
and data-centric position, 17–18
Development
federated, 308–9
Digital native companies, 4, 10
Digital transformation, 10–11, 115–17
DML (Data Management Language), 316
Domain name system, 160
Economic assessment, 282–83
Edmonton Transfer, 136
Emerging technology, 274–76
End game, economics of, 32–35
Enduring Business Themes, 24
Enterprise app store, 28–31
Enterprise knowledge graph, 309–10
Enterprise ontology, 297–300, 310, 322
establishing, 300–302
Enterprise Resource Planning systems (ERPs), 19
ESB (Enterprise Service Bus), 269, 270
ETL (extract, transform and load), 259
Executive sponsorship, 5–6
Existence proof, 11
Existential need, 11
Fad surf, 9, 49, 50
Federated development, 308–9
Fielding, Roy, 268
Fire hydrant system, 299
Formal definition, 174–76
FoxPro database, 126
Franz Inc., 20
Function Point Analysis, 70
Functional routines
shared, 33–34
Galilei, Galileo, 112
Garlik, 195–200
GeoNames, 164
Germ theory, 109–11
Girou, Mike, 24
Gist, 300–302
Global identifiers, 154–56
and self-assembling data, 158
Google Maps, 188
Gorman, John, 237
Governance, 311–25
structure, 313–18
summary of, 324–25
traditional, 319
Governance structures, 311–25
Graph databases, 151–53, 159, 162
GraphQL, 162
Harris, Steve, 195
Helicobacter Pylori, 111
Herd phenomenon, the
and change, 118–19
Historical analogy, 11
Home environment management system, 272
How Buildings Learn (Brand), 23
Human in the loop processing, 75
IBM AS/400 system, 137, 139
Ideas, new, 103
Identifiers, global, 154–56
Identity management, 250–51
Ilube, Tom, 197
Impact analysis, 133–34
Incentives, 120–23
perverse, 221–23
Inertial resistance, 38–39
InfoBoxes, 163
Information
follow your nose concept and, 89
separation of structure and meaning of, 149–50
Information systems
complexity of, 78
Information Technology transitions, 113
Initial projects
executing, 310
Initial self-assessment, 279
Inmon approach, 315
Integration, 63
elimination of, 237–38
reduction in cost of, 92–94
Integration costs, 76
Integration debt, 74, 91
Internal consortium, sharing and, 312
Internal IT
and the data-centric revolution, 114
Internet of Things (IoT), The, 271–73
Inventory, 283–86
IRI (International Resource Identifier), 160
Isolation and separation, 73–75
IT expenditure, self-funding of, 290–94
IT fads, 9–11
IT initiatives, think big/start small and, 295–97
JavaScript Object Notation (json), 81
json (JavaScript Object Notation), 81
Kafka, 269–71
Kimball approach, 315
Knowledge graph, enterprise, 309–10
Knowledge graphs, 10, 146
Kuhn, Thomas, 98
Lancaster, James, 107, 112
Langley, Stuart Pierpont, 105
Latent defects, 59
Learning process, sharing the, 319
Lee, Tim Berners-, 180
Legacy systems
and entrenchment, 64–66
Lind, James, 107–8
Linked data, 146, 162–69
Linked Enterprise Data, 163
Linked Open Data (LOD), 163
Linux operating system, 217
Logical proof, 11
Low-code, 244–45
Machine Learning (ML), 10, 266–67
Market Intelligence Data Architecture, 132
Marshall, Barry, 111
Maturity model
data-centric, 319–24
McConnell, Steve
Code Complete, 59
Metadata, 127–28, 172
Metadata repository, 287
Metaweb, 148
Microservices Architecture, 233–34
Microservices Architecture movement, 267–69
Microsoft chatbot, 267
Mirhaji, Parsa, 201–2
Model-driven constraints, 247–48
Model-driven development, 241–44, 255–56
and data-centric approach, 241
and low-code, 244–45
and no-code, 244–45
Model-driven validation, 247–48
Montefiore Medical Center, 200–208
Named Entity Recognition, 262
Namespaces, 160
Napoleon, 108
Napoleonic Wars, 108
Natural Language Processing (NLP), 191, 262
Natural Learning Program (NLP), 276
Nature of Technology, The (Arthur), 3
Negative network effect, 75
Neo4j, 152
Nest, 272, 273
Network structured data, 31
NLP (Natural Language Processing), 191, 262
NLP (Natural Learning Program), 276
No-code, 244–45
Non-linear change, 112–13
Object-oriented development, 235
Ontologies, 171, 184–85, 229
enterprise, 300–302, 322
modularity of, 185–87
OpenLink Software, 163
Opportunity, spotting, 289
PALM system, 206
Paradigm shift, 98–102
Parametric CAD, 242
ParaSQL System, 138
Patient Centered Outcomes Research Institute (PCORI), 202
Patient-centered Analytic Learning Machine (PALM), 201
Perverse incentives, 221–23
Pilot projects, 302–5
Platform migration
cost of, 131–33
Polo, Marco, 103
Procedural help, 11
Profile, data and, 287–88
Programmers, 223–25
Project budgets, 7–8
Projects, assessment of, 288
Proof of Concept (POC), 302
Proprietary graph databases, 162
Query language, 181
RDF data, 161
RDF resource descriptions framework, 153–54
RDF Triples, 317
Relational database, 150
Relational to RDF Mapping Language (R2RML), 189
Resistance
overt and covert, 40–42
Resolvable data, 161
RESTful architecture, 267–69
Round Earth, 103–4
Rule-based systems, 264–66
S&P Market Intelligence (MI), 125
history of, 126
S&P MI architecture, challenges of, 135–36
Salesforce, 244
Sarbanes Oxley Act, 286
Schema, 178–79
on read, 178–79
on write, 178–79
reduction in variety, 229–32
subset of, 234–37
Schema complexity, 228–29
Schema variety
reduction in, 229–32
Scurvy, 106–9
Security
application, 251–54
Self-assembling data, 158
Self-assessment
data-centric, 279–82
initial, 279
Semantic Arts, 301, 329
Semantic Circles, 316
Semantic query language, 181
Semantic system, 177
Semantic technology, 146
local constraints and, 181–83
Semantic Technology Conference, 197
Semantic Web, 180
Semantic Web community, 160
Semantic Web Layer Cake, 192
Semi-structured data, 31
Semmelweis, Ignaz, 109–11, 112
Separation and isolation, 73–75
SHACL, 182
Silos
application-centricity and, 62–63
Skunkworks, 8
Small core, 289–90
SNL data architecture, 128
SNOMED, 229
SOA (Service Oriented Architecture), 269, 271
Social proof
and change, 119–20
Software, 210–13, 216
Software code, 213–16
Software vendors
and the data-centric revolution, 113–14
Software, unnecessary, 220–25
Sokil Group, 136–43, 143
Sokil, John, 136
Sokil, Kim, 136–42
SPARQL, 162, 181
Specialize-ability, 23, 25–26
Standard & Poor’s case study, 311
Standish Chaos (2014), 69
Starting point, assessment of, 294
Structured data, 31
Structured databases, 150
Systems integration, 63
Systems integrators
and the data-centric revolution, 114
Task-centric approach, 81–84
Tasks
and inventory systems, 80
Taxonomy, 32
TBox, 316–18
Technological movements, 7
Technology
pace layering and, 23
Technology Readiness Level, 302
Telemedicine Advanced Research and Technology Center (TATRC), 201
Traditional governance, 319
Transformation, 319
True contingency, 305–6
Ulcers, 111
Uncurated data, 184
University of Berlin, 163
Unstructured data, 31
URIs
minting and detecting, 318
URIs (Uniform Resource Identifiers), 160
User interfaces, model-driven, 249–50
Variety, Big Data and, 258
Velocity, Big Data and, 258
Vienna General Hospital, 110
Vision, 11
Volume, Big Data and, 258
W3C standards, 153, 189
Warren, Robin, 111
Wikipedia, 163
Windows 10, 217
Workers Compensation Insurance company, 254
Workflows, 80
Wright brothers, 104–6