NEAL FISHMAN is a Distinguished Engineer and CTO of Data-Based Pathology at IBM. He is an IBM-certified Senior IT Architect and Open Group Distinguished Chief Architect. COLE STRYKER is a journalist based in Los Angeles. He is the author of Epic Win for Anonymous and Hacking the Future.

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• Foreword for Smarter Data Science xixEpigraph xxiPreamble xxiiiChapter 1 Climbing the AI Ladder 1Readying Data for AI 2Technology Focus Areas 3Taking the Ladder Rung by Rung 4Constantly Adapt to Retain Organizational Relevance 8Data-Based Reasoning is Part and Parcel in the Modern Business 10Toward the AI-Centric Organization 14Summary 16Chapter 2 Framing Part I: Considerations for Organizations Using AI 17Data-Driven Decision-Making 18Using Interrogatives to Gain Insight 19The Trust Matrix 20The Importance of Metrics and Human Insight 22Democratizing Data and Data Science 23Aye, a Prerequisite: Organizing Data Must Be a Forethought 26Preventing Design Pitfalls 27Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time 29Quae Quaestio (Question Everything) 30Summary 32Chapter 3 Framing Part II: Considerations for Working with Data and AI 35Personalizing the Data Experience for Every User 36Context Counts: Choosing the Right Way to Display Data 38Ethnography: Improving Understanding Through Specialized Data 42Data Governance and Data Quality 43The Value of Decomposing Data 43Providing Structure Through Data Governance 43Curating Data for Training 45Additional Considerations for Creating Value 45Ontologies: A Means for Encapsulating Knowledge 46Fairness, Trust, and Transparency in AI Outcomes 49Accessible, Accurate, Curated, and Organized 52Summary 54Chapter 4 A Look Back on Analytics: More Than One Hammer 57Been Here Before: Reviewing the Enterprise Data Warehouse 57Drawbacks of the Traditional Data Warehouse 64Paradigm Shift 68Modern Analytical Environments: The Data Lake 69By Contrast 71Indigenous Data 72Attributes of Difference 73Elements of the Data Lake 75The New Normal: Big Data is Now Normal Data 77Liberation from the Rigidity of a Single Data Model 78Streaming Data 78Suitable Tools for the Task 78Easier Accessibility 79Reducing Costs 79Scalability 79Data Management and Data Governance for AI 80Schema-on-Read vs. Schema-on-Write 81Summary 84Chapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail 87A Need for Organization 87The Staging Zone 90The Raw Zone 91The Discovery and Exploration Zone 92The Aligned Zone 93The Harmonized Zone 98The Curated Zone 100Data Topologies 100Zone Map 103Data Pipelines 104Data Topography 105Expanding, Adding, Moving, and Removing Zones 107Enabling the Zones 108Ingestion 108Data Governance 111Data Storage and Retention 112Data Processing 114Data Access 116Management and Monitoring 117Metadata 118Summary 119Chapter 6 Addressing Operational Disciplines on the AI Ladder 121A Passage of Time 122Create 128Stability 128Barriers 129Complexity 129Execute 130Ingestion 131Visibility 132Compliance 132Operate 133Quality 134Reliance 135Reusability 135The xOps Trifecta: DevOps/MLOps, DataOps, and AIOps 136DevOps/MLOps 137DataOps 139AIOps 142Summary 144Chapter 7 Maximizing the Use of Your Data: Being Value Driven 147Toward a Value Chain 148Chaining Through Correlation 152Enabling Action 154Expanding the Means to Act 155Curation 156Data Governance 159Integrated Data Management 162Onboarding 163Organizing 164Cataloging 166Metadata 167Preparing 168Provisioning 169Multi-Tenancy 170Summary 173Chapter 8 Valuing Data with Statistical Analysis and Enabling Meaningful Access 175Deriving Value: Managing Data as an Asset 175An Inexact Science 180Accessibility to Data: Not All Users are Equal 183Providing Self-Service to Data 184Access: The Importance of Adding Controls 186Ranking Datasets Using a Bottom-Up Approach for Data Governance 187How Various Industries Use Data and AI 188Benefi ting from Statistics 189Summary 198Chapter 9 Constructing for the Long-Term 199The Need to Change Habits: Avoiding Hard-Coding 200Overloading 201Locked In 202Ownership and Decomposition 204Design to Avoid Change 204Extending the Value of Data Through AI 206Polyglot Persistence 208Benefi ting from Data Literacy 213Understanding a Topic 215Skillsets 216It’s All Metadata 218The Right Data, in the Right Context, with the Right Interface 219Summary 221Chapter 10 A Journey’s End: An IA for AI 223Development Efforts for AI 224Essential Elements: Cloud-Based Computing, Data, and Analytics 228Intersections: Compute Capacity and Storage Capacity 234Analytic Intensity 237Interoperability Across the Elements 238Data Pipeline Flight Paths: Preflight, Inflight, Postflight 242Data Management for the Data Puddle, Data Pond, and Data Lake 243Driving Action: Context, Content, and Decision-Makers 245Keep It Simple 248The Silo is Dead; Long Live the Silo 250Taxonomy: Organizing Data Zones 252Capabilities for an Open Platform 256Summary 260Appendix Glossary of Terms 263Index 269

NEAL FISHMAN is a Distinguished Engineer and CTO of Data-Based Pathology at IBM. He is an IBM-certified Senior IT Architect and Open Group Distinguished Chief Architect. COLE STRYKER is a journalist based in Los Angeles. He is the author of Epic Win for Anonymous and Hacking the Future.

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• Foreword for Smarter Data Science xixEpigraph xxiPreamble xxiiiChapter 1 Climbing the AI Ladder 1Readying Data for AI 2Technology Focus Areas 3Taking the Ladder Rung by Rung 4Constantly Adapt to Retain Organizational Relevance 8Data-Based Reasoning is Part and Parcel in the Modern Business 10Toward the AI-Centric Organization 14Summary 16Chapter 2 Framing Part I: Considerations for Organizations Using AI 17Data-Driven Decision-Making 18Using Interrogatives to Gain Insight 19The Trust Matrix 20The Importance of Metrics and Human Insight 22Democratizing Data and Data Science 23Aye, a Prerequisite: Organizing Data Must Be a Forethought 26Preventing Design Pitfalls 27Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time 29Quae Quaestio (Question Everything) 30Summary 32Chapter 3 Framing Part II: Considerations for Working with Data and AI 35Personalizing the Data Experience for Every User 36Context Counts: Choosing the Right Way to Display Data 38Ethnography: Improving Understanding Through Specialized Data 42Data Governance and Data Quality 43The Value of Decomposing Data 43Providing Structure Through Data Governance 43Curating Data for Training 45Additional Considerations for Creating Value 45Ontologies: A Means for Encapsulating Knowledge 46Fairness, Trust, and Transparency in AI Outcomes 49Accessible, Accurate, Curated, and Organized 52Summary 54Chapter 4 A Look Back on Analytics: More Than One Hammer 57Been Here Before: Reviewing the Enterprise Data Warehouse 57Drawbacks of the Traditional Data Warehouse 64Paradigm Shift 68Modern Analytical Environments: The Data Lake 69By Contrast 71Indigenous Data 72Attributes of Difference 73Elements of the Data Lake 75The New Normal: Big Data is Now Normal Data 77Liberation from the Rigidity of a Single Data Model 78Streaming Data 78Suitable Tools for the Task 78Easier Accessibility 79Reducing Costs 79Scalability 79Data Management and Data Governance for AI 80Schema-on-Read vs. Schema-on-Write 81Summary 84Chapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail 87A Need for Organization 87The Staging Zone 90The Raw Zone 91The Discovery and Exploration Zone 92The Aligned Zone 93The Harmonized Zone 98The Curated Zone 100Data Topologies 100Zone Map 103Data Pipelines 104Data Topography 105Expanding, Adding, Moving, and Removing Zones 107Enabling the Zones 108Ingestion 108Data Governance 111Data Storage and Retention 112Data Processing 114Data Access 116Management and Monitoring 117Metadata 118Summary 119Chapter 6 Addressing Operational Disciplines on the AI Ladder 121A Passage of Time 122Create 128Stability 128Barriers 129Complexity 129Execute 130Ingestion 131Visibility 132Compliance 132Operate 133Quality 134Reliance 135Reusability 135The xOps Trifecta: DevOps/MLOps, DataOps, and AIOps 136DevOps/MLOps 137DataOps 139AIOps 142Summary 144Chapter 7 Maximizing the Use of Your Data: Being Value Driven 147Toward a Value Chain 148Chaining Through Correlation 152Enabling Action 154Expanding the Means to Act 155Curation 156Data Governance 159Integrated Data Management 162Onboarding 163Organizing 164Cataloging 166Metadata 167Preparing 168Provisioning 169Multi-Tenancy 170Summary 173Chapter 8 Valuing Data with Statistical Analysis and Enabling Meaningful Access 175Deriving Value: Managing Data as an Asset 175An Inexact Science 180Accessibility to Data: Not All Users are Equal 183Providing Self-Service to Data 184Access: The Importance of Adding Controls 186Ranking Datasets Using a Bottom-Up Approach for Data Governance 187How Various Industries Use Data and AI 188Benefi ting from Statistics 189Summary 198Chapter 9 Constructing for the Long-Term 199The Need to Change Habits: Avoiding Hard-Coding 200Overloading 201Locked In 202Ownership and Decomposition 204Design to Avoid Change 204Extending the Value of Data Through AI 206Polyglot Persistence 208Benefi ting from Data Literacy 213Understanding a Topic 215Skillsets 216It’s All Metadata 218The Right Data, in the Right Context, with the Right Interface 219Summary 221Chapter 10 A Journey’s End: An IA for AI 223Development Efforts for AI 224Essential Elements: Cloud-Based Computing, Data, and Analytics 228Intersections: Compute Capacity and Storage Capacity 234Analytic Intensity 237Interoperability Across the Elements 238Data Pipeline Flight Paths: Preflight, Inflight, Postflight 242Data Management for the Data Puddle, Data Pond, and Data Lake 243Driving Action: Context, Content, and Decision-Makers 245Keep It Simple 248The Silo is Dead; Long Live the Silo 250Taxonomy: Organizing Data Zones 252Capabilities for an Open Platform 256Summary 260Appendix Glossary of Terms 263Index 269

NEAL FISHMAN is a Distinguished Engineer and CTO of Data-Based Pathology at IBM. He is an IBM-certified Senior IT Architect and Open Group Distinguished Chief Architect. COLE STRYKER is a journalist based in Los Angeles. He is the author of Epic Win for Anonymous and Hacking the Future.

Select Guide Rating

• Foreword for Smarter Data Science xixEpigraph xxiPreamble xxiiiChapter 1 Climbing the AI Ladder 1Readying Data for AI 2Technology Focus Areas 3Taking the Ladder Rung by Rung 4Constantly Adapt to Retain Organizational Relevance 8Data-Based Reasoning is Part and Parcel in the Modern Business 10Toward the AI-Centric Organization 14Summary 16Chapter 2 Framing Part I: Considerations for Organizations Using AI 17Data-Driven Decision-Making 18Using Interrogatives to Gain Insight 19The Trust Matrix 20The Importance of Metrics and Human Insight 22Democratizing Data and Data Science 23Aye, a Prerequisite: Organizing Data Must Be a Forethought 26Preventing Design Pitfalls 27Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time 29Quae Quaestio (Question Everything) 30Summary 32Chapter 3 Framing Part II: Considerations for Working with Data and AI 35Personalizing the Data Experience for Every User 36Context Counts: Choosing the Right Way to Display Data 38Ethnography: Improving Understanding Through Specialized Data 42Data Governance and Data Quality 43The Value of Decomposing Data 43Providing Structure Through Data Governance 43Curating Data for Training 45Additional Considerations for Creating Value 45Ontologies: A Means for Encapsulating Knowledge 46Fairness, Trust, and Transparency in AI Outcomes 49Accessible, Accurate, Curated, and Organized 52Summary 54Chapter 4 A Look Back on Analytics: More Than One Hammer 57Been Here Before: Reviewing the Enterprise Data Warehouse 57Drawbacks of the Traditional Data Warehouse 64Paradigm Shift 68Modern Analytical Environments: The Data Lake 69By Contrast 71Indigenous Data 72Attributes of Difference 73Elements of the Data Lake 75The New Normal: Big Data is Now Normal Data 77Liberation from the Rigidity of a Single Data Model 78Streaming Data 78Suitable Tools for the Task 78Easier Accessibility 79Reducing Costs 79Scalability 79Data Management and Data Governance for AI 80Schema-on-Read vs. Schema-on-Write 81Summary 84Chapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail 87A Need for Organization 87The Staging Zone 90The Raw Zone 91The Discovery and Exploration Zone 92The Aligned Zone 93The Harmonized Zone 98The Curated Zone 100Data Topologies 100Zone Map 103Data Pipelines 104Data Topography 105Expanding, Adding, Moving, and Removing Zones 107Enabling the Zones 108Ingestion 108Data Governance 111Data Storage and Retention 112Data Processing 114Data Access 116Management and Monitoring 117Metadata 118Summary 119Chapter 6 Addressing Operational Disciplines on the AI Ladder 121A Passage of Time 122Create 128Stability 128Barriers 129Complexity 129Execute 130Ingestion 131Visibility 132Compliance 132Operate 133Quality 134Reliance 135Reusability 135The xOps Trifecta: DevOps/MLOps, DataOps, and AIOps 136DevOps/MLOps 137DataOps 139AIOps 142Summary 144Chapter 7 Maximizing the Use of Your Data: Being Value Driven 147Toward a Value Chain 148Chaining Through Correlation 152Enabling Action 154Expanding the Means to Act 155Curation 156Data Governance 159Integrated Data Management 162Onboarding 163Organizing 164Cataloging 166Metadata 167Preparing 168Provisioning 169Multi-Tenancy 170Summary 173Chapter 8 Valuing Data with Statistical Analysis and Enabling Meaningful Access 175Deriving Value: Managing Data as an Asset 175An Inexact Science 180Accessibility to Data: Not All Users are Equal 183Providing Self-Service to Data 184Access: The Importance of Adding Controls 186Ranking Datasets Using a Bottom-Up Approach for Data Governance 187How Various Industries Use Data and AI 188Benefi ting from Statistics 189Summary 198Chapter 9 Constructing for the Long-Term 199The Need to Change Habits: Avoiding Hard-Coding 200Overloading 201Locked In 202Ownership and Decomposition 204Design to Avoid Change 204Extending the Value of Data Through AI 206Polyglot Persistence 208Benefi ting from Data Literacy 213Understanding a Topic 215Skillsets 216It’s All Metadata 218The Right Data, in the Right Context, with the Right Interface 219Summary 221Chapter 10 A Journey’s End: An IA for AI 223Development Efforts for AI 224Essential Elements: Cloud-Based Computing, Data, and Analytics 228Intersections: Compute Capacity and Storage Capacity 234Analytic Intensity 237Interoperability Across the Elements 238Data Pipeline Flight Paths: Preflight, Inflight, Postflight 242Data Management for the Data Puddle, Data Pond, and Data Lake 243Driving Action: Context, Content, and Decision-Makers 245Keep It Simple 248The Silo is Dead; Long Live the Silo 250Taxonomy: Organizing Data Zones 252Capabilities for an Open Platform 256Summary 260Appendix Glossary of Terms 263Index 269