Data Governance

Data governance is the set of policies, roles, and processes that ensure an organization's data is accurate, consistent, secure, and used responsibly across its entire lifecycle - from collection through storage, analysis, and eventual deletion.

Core Pillars of Data Governance

• Data quality - ensuring data is accurate, complete, and consistent
• Data security - protecting data from unauthorized access or breaches
• Data privacy and compliance - following regulations like GDPR that govern how personal data is handled
• Data ownership - defining who is responsible for maintaining and approving changes to specific datasets
• Metadata management - documenting where data comes from and how it's defined

Why Data Governance Matters for Data Science

Even the best model is only as reliable as the data behind it. Strong governance ensures data scientists work with trustworthy, well-documented data, and that sensitive information (like personal or financial data) is handled in compliance with legal and ethical standards.

Practical Examples

• Restricting access to columns containing personal data, like email or phone numbers
• Maintaining a data dictionary that documents what each column and table means
• Setting up audit logs to track who accessed or modified sensitive data