Introduction

Behind every successful AI system is a strong data governance policy. Without clean, accessible, and compliant data, AI efforts often fail before they begin.

Key Areas of Data Governance

• Data Quality: Incomplete or inconsistent data skews model accuracy.

• Access Control: Who can see and use what data — essential for privacy and compliance.

• Lifecycle Management: Ensuring data is archived or deleted as per policy and regulation.

• Metadata & Lineage: Understanding where data came from and how it's been modified.

Real-World Tip

Start by implementing small-scale governance around your key datasets — especially customer and transactional data.
Use tools like Collibra, Talend, or open-source Apache Atlas.