1  Cover

      2  About the Authors

      3  Acknowledgments

      4  Foreword for Smarter Data Science

      5  Epigraph

      6  Preamble Why You Need This Book What You'll Learn

      7  CHAPTER 1: Climbing the AI Ladder Readying Data for AI Technology Focus Areas 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 Summary

      8  CHAPTER 2: Framing Part I: Considerations for Organizations Using AI Data-Driven Decision-Making Democratizing Data and Data Science Aye, a Prerequisite: Organizing Data Must Be a Forethought Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time Quae Quaestio (Question Everything) Summary

      9  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 Ontologies: A Means for Encapsulating Knowledge Fairness, Trust, and Transparency in AI Outcomes Accessible, Accurate, Curated, and Organized Summary

      10  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 Paradigm Shift Modern Analytical Environments: The Data Lake Elements of the Data Lake The New Normal: Big Data Is Now Normal Data Schema-on-Read vs. Schema-on-Write Summary

      11  CHAPTER 5: A Look Forward on Analytics: Not Everything Can Be a Nail A Need for Organization Data Topologies Expanding, Adding, Moving, and Removing Zones Enabling the Zones Summary

      12  CHAPTER 6: Addressing Operational Disciplines on the AI Ladder A Passage of Time Create Execute Operate The xOps Trifecta: DevOps/MLOps, DataOps, and AIOps Summary

      13  CHAPTER 7: Maximizing the Use of Your Data: Being Value Driven Toward a Value Chain Curation Data Governance Integrated Data Management Summary

      14  CHAPTER 8: Valuing Data with Statistical Analysis and Enabling Meaningful Access Deriving Value: Managing Data as an Asset Accessibility to Data: Not All Users Are Equal Providing Self-Service to Data Access: The Importance of Adding Controls Ranking Datasets Using a Bottom-Up Approach for Data Governance How Various Industries Use Data and AI Benefiting from Statistics