AI offers powerful insights, but its value depends on the strength of the data foundations that support it. Without clear terminology, defined rules, and trustworthy data, even advanced AI produces inconsistent or misleading results. This tutorial helps participants move from fragmented, ad-hoc data and documentation practices toward a disciplined approach that ensures reliable, scalable value. 

Through interactive case studies and exercises, participants will see how different interpretations of the same dataset lead to conflicting answers and how aligning on shared definitions, rules, and metrics eliminates confusion. Unlike many data-focused sessions that concentrate primarily on structured datasets, this tutorial explicitly addresses the challenges of unstructured data and natural-language content.

The tutorial will also cover practical governance tactics, including validation, controlled vocabularies, and metric rules, that strengthen organizational trust in data. Attendees will leave with a repeatable framework to prepare their own ecosystems for AI adoption, enabling consistent practices, stronger collaboration, and the confidence to harness AI effectively.

LEARNING OBJECTIVES

Collect and define shared vocabulary and rules to ensure consistent data interpretation.

Apply practical techniques to improve the quality and consistency of unstructured data for reliable AI outcomes.

Understand how fostering a strong data culture enables teams to trust, govern, and effectively use data for AI.