1、12ContentsFrom the Editors Desk _3Foreword _5Data and Analytics Value drivers _7Chapter 1:Analytics for The Win through the Data Lens _9Chapter 2:The 10 Crucial Elements of a Winning Data&Analytics Strategy _18Chapter 3:Managing the ML Project Lifecycle _24Chapter 4:Textual analytics and Textual dis

2、ambiguation _30Chapter 5:Hallmarks of a True Data-driven Organization Culture _40Chapter 6:Data Culture and Data ethics in Business _52Chapter 7:Business Analytics:A Blueprint for Unlocking Digital Value _61Chapter 8:Creating Business Value with Data,Analytics&Insights _67Chapter 9:Defining the Mode

3、rn Data Landscape with the Semantic Layer _75Chapter 10:Demystifying BI with AI _86Chapter 11:Translating analytics products into business value _94Chapter 12:A Product Management Approach to Data Monetization _98Chapter 13:Achieving Executive Engagement in AI Programs _109Chapter 14:Modern Data Man

4、agement and Engineering _123Chapter 15:Gung-Ho Analytics_130Chapter 16:Do you really want to be DAD?_136Chapter 17:The Need for Semantic Layer in Financial Services _141Chapter 18:Understanding and Mitigating AI Risk _147Chapter 19:Actionable Insights for Improved Business Results _152Chapter 20:The

5、 Semantics of the Semantic Layer _158Glossary _166Acronyms and Abbreviations _1703Data and Analytics(D&A)is considered the next frontier for innovation and productivity in business.A Mckinsey report says data-driven organizations provide EBITDA increases of up to 25 percent.According to Boston Consu

6、lting,the first 9 of the top 10 innovative companies are data firms.But achieving a sustainable competitive advantage from D&A is challenging.Many D&A projects are unsuccessful.Gartner says,only 20 percent of the Data and Analytic solutions deliver business outcomes.So,how can organizations get valu