Metadata Matters:
Powering AI Data Discovery and Context

This program challenges the common assumption that data quality is a problem to be solved with tools alone. It argues that successful data quality is not a project, but a continuous program that must be engineered by design, ensuring trustworthy data across the enterprise. This approach is more critical than ever in the age of AI, where the principle of "Garbage In, Garbage Out" is a fundamental reality.  

To address these challenges, organizations need to move beyond reactive, fire-fighting solutions and adopt a proactive, programmatic approach rooted in systems thinking and the Deming Cycle (Plan-Do-Check-Act). The presentation will clarify that data quality challenges come in two forms:

Practice-related: Issues that are closer to the user and their processes.

Structure-related: Imperfections in how data is arranged and stored, which require architectural solutions.

Through real-world case studies, you will learn how to make the business case for data quality by focusing on tangible ROI and reframing technical conversations around business outcomes. You will be able to demonstrate how a dedicated, skilled team can develop the repeatable capabilities necessary to ensure reliable data inputs, allowing your organization to confidently deploy AI systems and achieve strategic success.

Program Learning Objectives

-   Help you understand foundational data quality concepts
    for improving data quality at your organization

-  Demonstrate how chronic business challenges for
    organizations are often rooted in poor data quality

-  Shared case studies illustrating the hallmarks and
    benefits of data quality success