CRITICAL FOUNDATIONS PRESENT
You’ve established important elements of data quality and governance, but opportunities exist to strengthen these capabilities. Prioritize implementing automated quality checks to flag duplicates, missing values, and inconsistencies — this typically reduces QA time by 60-70% while improving accuracy.
Close data completeness gaps, particularly in clinical data or SDoH, by establishing data-sharing agreements with key providers and participating in health information exchanges.
Enhance normalization capabilities to map all diagnosis codes, provider identifiers, and lab results to consistent taxonomies.
Implement comprehensive lineage tracking (increasingly required for AI/ML validation and regulatory compliance) and build transparency tools so end users can drill into aggregated data.
If governance is informal, establish a formal Data Governance Council with executive sponsorship, defined ownership roles, and documented policies.
Find a technology partner that supports these activities, like Cedar Gate’s platform with built-in quality monitoring, comprehensive lineage, normalized datasets, and transparency tool. This will allow your team to focus on governance policy rather than building infrastructure.
