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.

Many organizations are wondering how GenAI will impact their D&A governance and management tools, and by extension, their D&A governance operations. This research provides an overview of how key D&A markets are incorporating GenAI capabilities and how these will change the nature of D&A governance.

Trust in data, strong risk management, robust trust models, and agentic AI are reshaping how enterprises handle data, analytics, compliance, and business value. This session explores the impact of trust on data governance and agentic AI.

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.

We will discuss the latest issues that heads of data governance must address and provide recommendations to manage and capitalize on emerging opportunities. By understanding key drivers and trends, leaders will be equipped to optimize their data governance strategies.