15 CHAPTER 9: Constructing for the Long-Term The Need to Change Habits: Avoiding Hard-Coding Extending the Value of Data Through AI Polyglot Persistence Benefiting from Data Literacy Summary
16 CHAPTER 10: A Journey's End: An IA for AI Development Efforts for AI Essential Elements: Cloud-Based Computing, Data, and Analytics Driving Action: Context, Content, and Decision-Makers Keep It Simple The Silo Is Dead; Long Live the Silo Taxonomy: Organizing Data Zones Capabilities for an Open Platform Summary
17 Appendix: Glossary of Terms
18 Index
List of Illustrations
1 Chapter 1Figure 1-1: The AI Ladder to achieve a full complement of data and analytics...Figure 1-2: The ladder is part of a repetitive climb to continual improvemen...Figure 1-3: Current state ⇦ future state ⇦ current stateFigure 1-4: Ends and means model
2 Chapter 2Figure 2-1: Trust matrixFigure 2-2: Breadth and depth sliversFigure 2-3: GradingFigure 2-4: Data and AI democratizationFigure 2-5: Recognizing that the ability to skillfully ask questions is the ...
3 Chapter 3Figure 3-1: Monitors at Mission Control Center for the International Space S...Figure 3-2: A closer viewFigure 3-3: Monitors in a hospital emergency roomFigure 3-4: An electrocardiogram pattern showing normal and abnormal heartbe...Figure 3-5: Data governanceFigure 3-6: An ontological modelFigure 3-7: InferenceFigure 3-8: Blood test results showing normalcy, part AFigure 3-9: Blood test results showing normalcy, part BFigure 3-10: Recognizing preconditions
4 Chapter 4Figure 4-1: Reviewing atomic dataFigure 4-2: Simplified information architecture for an EDWFigure 4-3: YARN architecture
5 Chapter 5Figure 5-1: Starter set zonesFigure 5-2: Consistent to ActiveFigure 5-3: Consistent to InactiveFigure 5-4: Consistent to CISCO_INACTIVE_INDFigure 5-5: Consistent to StatusFigure 5-6: Consistent to WorksheetsFigure 5-7: Consistent to Implied MeaningFigure 5-8: Consistent to MetadataFigure 5-9: Misrepresenting the nature of data governanceFigure 5-10: Core elements of a data topologyFigure 5-11: Primitive zone typesFigure 5-12: Data lakes, data ponds, and data puddles
6 Chapter 6Figure 6-1: ChallengesFigure 6-2: Seven practicesFigure 6-3: Building to an MVPFigure 6-4: DevOps shift-left approachFigure 6-5: Core capabilities for DevOps and MLOpsFigure 6-6: Identifying DataOps stakeholdersFigure 6-7: Building blocks for AIOps
7 Chapter 7Figure 7-1: Data to wisdomFigure 7-2: Dialing data governance
8 Chapter 8Figure 8-1: Data value chainFigure 8-2: SkewnessFigure 8-3: KurtosisFigure 8-4: Identifying outliersFigure 8-5: Gaussian distributionFigure 8-6: Gaussian histogram plotFigure 8-7: Gaussian distribution with low and high variance
9 Chapter 9Figure 9-1: Caesar's Entertainment operating company asset valuesFigure 9-2: What color are clouds?Figure 9-3: Digitization misses more data than is actually collected.
10 Chapter 10Figure 10-1: Cloud topographyFigure 10-2: Compute and storage capabilitiesFigure 10-3: Analytic intensityFigure 10-4: Communication flowsFigure 10-5: Flight paths for model executionFigure 10-6: Driving prediction, automation, and optimizationFigure 10-7: Transitive closure and access privilegesFigure 10-8: A proliferation of lines serves to highlight the need for line ...Figure 10-9: Taxonomic representationFigure 10-10: Virtualized data zones
Guide
1 Cover
2 Table of Contents
Pages
1 i
2 ii
3 iii
4 iv
5 v
6 vi
7 vii
8 ix
9 xi
10 xix
11 xx
12 xxi
13 xxiii
14 xxiv
15 xxv
16 xxvi
17 1
18 2
19 3
20 4
21 5
22 6