Data Quality as the Foundation
Decisions depend on clean, accurate data. Duplicate, inconsistent, or outdated records create costly mistakes.
Regular audits and validation routines keep datasets trustworthy.
Ensuring Data Security
Security is not optional. Encryption, masking, and access control prevent breaches.
Beyond IT safeguards, employees also carry responsibility.
Defining Data Ownership
Each dataset must have a steward accountable for its accuracy and distribution.
Clear roles prevent confusion, duplication, and blame-shifting.
Governance Tools
Tools like Collibra, Alation, or Microsoft Purview simplify cataloging and lineage.
Accessible catalogs foster trust and transparency across teams.
Governance in the AI Era
AI models demand even stricter governance. Garbage data leads to garbage insights.
Future regulations will require explainability frameworks for AI analytics.