Anurag Raj

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.

There are seven key reasons organizations struggle with effective data governance. This session examines what consistently goes wrong and offers practical solutions to help make data governance work for your organization.

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.

Gartner research shows that technical excellence alone rarely shifts mindsets or drives cultural change. If you want your business to truly think differently about data, you need to stop “selling” capabilities and start “marketing” value, using the same tactics as world-class consumer brands. This session unveils a new playbook for D&A leaders to build and execute a marketing plan that transforms culture and boosts adoption, with Gartner insights and real-world examples. Learn why most D&A strategies fail, how to apply proven marketing principles, and spark enthusiasm for data across your organization.

In a recent Gartner survey, 89% of CDAOs said effective D&A governance is crucial for business, technology and AI innovation, yet almost 70% find their current D&A and AI governance practices, skills and technology insufficient to drive innovation and business value. This multigroup discussion will focus on sharing experiences and addressing how CDAOs are evolving their D&A and AI governance approaches, building the business case for foundational investments, and designing effective operating models.

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.

Data and analytics governance has historically lagged in innovating. We are at a tipping point where D&A governance can be a single point of failure for AI. To succeed, D&A leaders must evolve governance for the future while reinforcing proven practices. This session provides a forward-looking perspective on D&A governance, including governance of AI, by AI and for AI.