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Data Quality

Emory's data quality program combines OHDSI community standards with DataOps-inspired pipeline testing to ensure OMOP data is accurate, complete, and fit for research.

At a Glance

  • 96.6% (v1.0.0) Pass Rate

    Overall OHDSI Data Quality Dashboard pass rate across all checks. Drill into failures and category breakdowns.

    DQD results Interactive dashboard

  • DBT Test Suites

    Column-level test definitions for every table in the ETL pipeline — referential integrity, nullability, uniqueness, and domain validation.

    DBT test definitions

  • Known Issues

    Table-by-table documentation of mapping gaps, data limitations, and recommended workarounds.

    Known issues

Our Approach

  • DataOps Design Philosophy

    Our quality process is built on the DataOps framework — combining DevOps practices with manufacturing-inspired process control to build quality into the pipeline, not bolt it on after.

    Design philosophy

  • OHDSI Data Quality Dashboard

    The community-standard DQD runs 2,000+ automated checks across completeness, conformance, and plausibility — providing a standardized quality assessment comparable across OHDSI sites.

    Explore the dashboard

Found a data quality issue?

Report it through our bug report form or reach out on Microsoft Teams.