The Data Integrity & AI Forum: No More Deferring: AI is making data quality a board-level issue
Welcome to The Data Integrity & AI Forum: Where data leaders connect to address emerging priorities, advance Agentic-Ready Data strategies, and translate AI ambition into real ROI.
Data quality remains the single most cited data integrity priority in the 2026 State of Data Integrity and AI Readiness report, named by 51% of data and analytics leaders as their top concern for 2026. Yet years of deferred investment have left many organizations with data they cannot fully measure, govern, or trust.
That risk grows as AI, specifically agentic AI, takes on a larger role in business decisions and processes. Autonomous agents act on data without a human in the loop. Data quality debt is no longer just an IT challenge.
Closing that gap also creates new opportunities. Once data is accurate, governed, and trusted, organizations can package it as reusable data products that are easier to discover, understand, and use. Data product marketplaces can then help teams share those assets across the business and extend their value to external partners Join this roundtable that brings together thought leaders from Precisely and industry partners to examine how organizations can address data quality debt, build greater confidence in AI, and create trusted data products at scale. The conversation will explore the foundations required to improve data quality and the data product marketplace model that can turn trusted data into broader business value.
The discussion will focus on:
- Why data quality debt has compounded for decades, and why AI has finally made it a board-level issue
- The measurement gap: why 29% of leaders say they can’t effectively measure data quality, and what that means for building any trustworthy AI or data product on top of it
- What separates organizations that are executing on data quality improvements (55%) from those still stuck in discovery or planning
- What it takes to package trusted, well-governed data for consumption via a data product marketplace, internally and externally
