AI is only as smart as the data that feeds it. For information governance (IG) professionals, ensuring data quality and integrity isn’t just good practice — it’s essential for achieving trustworthy, compliant, and high-performing AI outcomes.
This AI Readiness & Data Quality Checklist from Gimmal outlines a practical, step-by-step approach to improving the credibility and utility of your organization’s data. It’s designed for IG and data governance leaders who want to strengthen control and reliability before scaling AI initiatives.
In this free checklist, you’ll learn how to:
Identify and mitigate common data quality risks across systems and spreadsheets.
Define validation rules that ensure data accuracy, completeness, and consistency.
Build governance practices that enhance both data integrity and AI readiness.
Spot and correct issues such as redundant records, inconsistent formatting, and bias.
Develop a repeatable data validation and improvement process within your organization.
Whether you’re preparing to implement machine learning or optimizing enterprise data management, this resource provides a ready-to-use framework to elevate the quality and confidence of your data assets.
Complete the form to download your copy and take the next step toward AI-ready information governance.
