Foreword for Smarter Data Science
Chapter 1 Climbing the AI Ladder
Taking the Ladder Rung by Rung
Constantly Adapt to Retain Organizational Relevance
Data-Based Reasoning Is Part and Parcel in the Modern Business
Toward the AI-Centric Organization
Chapter 2 Framing Part I: Considerations for Organizations Using AI
Data-Driven Decision-Making
Using Interrogatives to Gain Insight
The Importance of Metrics and Human Insight
Democratizing Data and Data Science
Aye, a Prerequisite: Organizing Data Must Be a Forethought
Preventing Design Pitfalls
Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time
Quae Quaestio (Question Everything)
Chapter 3 Framing Part II: Considerations for Working with Data and AI
Personalizing the Data Experience for Every User
Context Counts: Choosing the Right Way to Display Data
Ethnography: Improving Understanding Through Specialized Data
Data Governance and Data Quality
The Value of Decomposing Data
Providing Structure Through Data Governance
Curating Data for Training
Additional Considerations for Creating Value
Ontologies: A Means for Encapsulating Knowledge
Fairness, Trust, and Transparency in AI Outcomes
Accessible, Accurate, Curated, and Organized
Chapter 4 A Look Back on Analytics: More Than One Hammer
Been Here Before: Reviewing the Enterprise Data Warehouse
Drawbacks of the Traditional Data Warehouse
Modern Analytical Environments: The Data Lake
Elements of the Data Lake
The New Normal: Big Data Is Now Normal Data
Liberation from the Rigidity of a Single Data Model
Suitable Tools for the Task
Data Management and Data Governance for AI
Schema-on-Read vs. Schema-on-Write
Chapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail
The Discovery and Exploration Zone
Expanding, Adding, Moving, and Removing Zones
Data Storage and Retention
Management and Monitoring
Chapter 6 Addressing Operational Disciplines on the AI Ladder
