Organizations struggle to make sense of numerous programs and projects that overlap or operate in silos. This research will weave together data and analytics governance, MDM and data quality into one organized initiative that every CDAO should be interested in.

Data architects are increasingly tasked with provisioning quality unstructured data to support AI models. However, little has been done to manage unstructured data beyond data security and privacy requirements. This session will look at what it takes to improve the quality of unstructured data and the emerging best practices in this space.

As AI evolves into more agentic forms, capable of autonomous decision-making and complex interactions, the readiness of your data becomes a mission-critical priority. This roundtable gathers data & analytics leaders to explore the unique challenges of preparing data ecosystems for agentic AI. Discussions will focus on overcoming barriers such as data quality gaps, governance complexities, and scalability issues, while highlighting the transformative role of technologies like generative AI, data fabrics, and metadata-driven governance

Traditional approaches and thinking around data quality are out of date and not sufficient in the era of AI. Data, analytics and AI leaders will need to reconsider their approach to data quality going beyond the traditional six data quality dimensions. This session will help data leaders learn to think about data quality in a holistic way that support making data AI-ready.

The case study will focus on the approach to drive an organization wide data quality uplift initiative with clearly defined objectives, to enable appropriate rule coverage, right DQ checks, mastering of DQ rules, automated analogy detection, automated DQ issue remediation, centralized monitoring and a collaborative accountability.

It's now easier than ever for less technical users to access, manage and analyze data without needing help from IT. But, self-service data management isn't always straightforward, and there are plenty of pitfalls, like data quality issues, skills gaps and governance concerns. This session will cover practical ways to make self-service data management work.