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1、Preparing for AI-Driven InnovationWhite paperModern Data GDan has over 2 decades of experience in Energy,FinTech,Central Government,Logistics&other industries.He specializes in helping businesses understand the potential of their data,the power of Artificial Intelligence,and how it can be used to ch

2、ange users day-to-day experiences-both inside organizations and in the external consumer world.Daniel Minnick,Head of Data&AI at CiklumIndexIntroductionSECTION 0206Executive SummarySECTION 010407Strategic Framework for Modern Data GovernanceSECTION 0317ImplementationStrategySECTION 0424Industry-Spec

3、ific Regulatory ConsiderationsSECTION 0528Measuring SuccessSECTION 0632ReferencesSECTION 0930ConclusionSECTION 0731RecommendationSECTION 084SECTION 01Executive SummaryIn todays AI-driven business landscape,robust data governance has become a critical success factor for organizations looking to lever

4、age artificial intelligence and machine learning technologies.This paper presents a comprehensive framework for modern data governance that specifically addresses the challenges and requirements of AI readiness while establishing business as usual and sustainable data management practices.Poor data

5、quality costs organizations an average of 15-25%of their revenue,while research indicates that well-governed,high-quality data can increase data asset valuations by 50%or more.For AI initiatives,where data scientists typically spend 60-80%of their time on data preparation,effective governance become

6、s even more crucial for success.The framework outlined in this paper focuses on four key pillars:data observability,quality management,standardization,and knowledge organization systems.Data observability ensures end-to-end visibility of data pipelines and early detection of issues that could impact