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Andrew White

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Andrew is a distinguished Analyst and VP and has a primary research focus on the chief data and analytics officer (CDAO) role and responsibility. This includes data and analytics strategy, governance, organization and roles, business value of data and analytics and so on. He also focuses on terms of tools and technology on those related to data and analytics governance.

He is currently Chief of Research, Markets and oversees and coordinates our efforts related to how we cover markets in our research. Before that he was Chief of Research for China where he helped set up and organize our go-to-market Research storyline and team. Before that he was lucky enough to be the lead the overall research content development plan for our new CDAO product offering. And before this he was the Chief of Research for Data and Analytics. He has also been Vendor Lead for Oracle Corp. He was the Research leader behind the first ever Master Data Management Summit (MDM) in 2007 and was conference chair of that event through 2015 as it expanded focus to enterprise information and MDM, before merging with Analytics and BI to form Data and Analytics.

Andrew has been the recipient of the Gartner Thought Leadership Award. The first was in 2006 for his efforts in pioneering Gartner's research in Enterprise Information Management that led to MDM. His second was in 2021 as part of a team developing our Data Fabric research. His previous coverage areas focused on business apps and best practices in supply chain management (SCM), enterprise resource planning (ERP) and multienterprise B2B collaboration environments.

Bio from: gartner-data-analytics-apac-2025

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Organizations are charged with being more productive, and while AI is an answer to many such opportunities, organization and program structure can be far more impactful on productivity than using AI. This session will weave together data and analytics governance, MDM, and data quality into one organized initiative that will simplify complexity. Join this session to learn more.

Data governance has traditionally encompassed analytics governance, managing most risks and value in traditional analytics. However, AI introduces new risks and considerations that D&A governance may not be equipped for. Should D&A governance evolve to govern AI or is it time for a separate discipline with a fresh mandate? This session explores conflicting accountabilities, leadership and operating models between these disciplines.