The three pillars of trustworthy data

Data management, data quality, and data governance are often discussed as separate initiatives, but they are deeply interconnected. Without quality rules, governance is aspirational. Without governance, quality checks lack enforcement. Without management, neither has a foundation.

Data management

The operational discipline of cataloging, organizing, and maintaining data assets across their lifecycle:

  • Metadata management and schema tracking
  • Data lineage and dependency mapping
  • Lifecycle management and retention policies

Data quality

Measuring and maintaining the fitness of data for its intended purpose across key dimensions:

  • Accuracy — does the data reflect reality?
  • Completeness — are required fields populated?
  • Consistency — do values agree across systems?
  • Timeliness — is the data fresh enough for its use case?

Data governance

The policies, processes, and organizational structures that ensure data is managed responsibly:

  • Ownership and stewardship assignments
  • Access controls and approval workflows
  • Regulatory compliance mapping (GDPR, CCPA, HIPAA)
  • Audit trails and accountability

Operationalizing all three

The key insight is that these three disciplines reinforce each other. A well-cataloged data estate makes quality checks more effective, and governance policies ensure both management and quality practices are consistently applied.

Read the full article on Medium.