As a rapidly rising number of organizations across markets position data squarely at the heart of their business, data governance becomes a top priority amid regulations designed to ensure data privacy and security.
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Summary
Efforts to implement strong data governance and data quality are more difficult than ever as AI initiatives lean heavily on data to meet expectations for drastically improved processes and competitive advantages.
Research Report
As a rapidly rising number of organizations across markets position data squarely at the heart of their business, data governance becomes a top priority amid regulations designed to ensure data privacy and security. Efforts to implement strong data governance and data quality are more difficult than ever as AI initiatives lean heavily on data to meet expectations for drastically improved processes and competitive advantages. Data from across organizational frameworks is pouring into AI tools and supportive components, including large language models (LLMs) and foundational models (FMs), spawning even more scrutiny driven by regulatory, compliance, and corporate governance requirements.
To address these challenges, organizations are turning to data governance tools to help them carefully inspect, monitor, and manage data, especially in the face of widespread AI strategies and initiatives. To gain further insight into these trends, we surveyed 318 data and IT professionals at organizations in North America (US and Canada) involved with or responsible for evaluating, purchasing, and managing data governance solutions and services.
Research Report: Navigating Data Governance in the Age of AI
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Further reading
Author
Stephen Catanzano, Senior Analyst, Data Management and Analytics
Mike Leone, Principal Analyst, AI Software and Services
