Reconciliation
Month-level metrics verify that enrollment and claims totals align across payer, plan, and data_source slices before stakeholders use the downstream outputs.
Warehouse-Native Product
Spotlight DQ
When the data contract drifts, trust erodes long before anyone catches it. Spotlight DQ gives healthcare teams a warehouse-native way to monitor reconciliation, completeness, and terminology validity with PHI-safe aggregate outputs that can be reviewed every run.
The package runs on any dbt-supported adapter and publishes scorecard-ready summaries instead of row-level extracts, so stakeholders can see whether the pipeline is holding up without widening data access.
Official-run history gives teams a repeatable Look step: compare production releases over time, explain movement, and decide whether the data is getting healthier or drifting.
Product brief
Recurring data quality review
Spotlight DQ makes production data quality visible over time, so teams can compare official runs, explain movement, and catch drift before stakeholders lose trust.
- Review aggregate outputs without widening PHI access
- Track reconciliation, completeness, and validity together
- Make release-to-release data health part of operations
Coverage
One package, three ways to prove the pipeline still deserves trust
Completeness
Field-level null and non-null analysis highlights whether key inputs are present, while applicability rules prevent false alarms on fields that are not relevant to every claim type.
Terminology validation
Reference-table checks and business rules expose invalid codes, malformed identifiers, and date problems before they silently degrade analytics products.
Operating Need
Built for recurring production review, not one-off QA
Healthcare data quality is not solved by a single pre-launch cleanup. Eligibility feeds change, terminology tables drift, and claims timing shifts. Spotlight DQ keeps those realities visible with outputs that are safe to circulate and easy to trend.
Because the outputs stay aggregated and PHI-safe, data, analytics, and operations teams can review the same scorecard without creating a parallel access pattern for raw records.
Review cadence
What teams monitor over time
Official runs persist history so release-to-release comparison becomes part of the operating cadence.
- Member month and paid amount movement by source
- Completeness deltas on high-value fields
- Terminology validity pass rates and threshold breaches
Delivery Model
Implemented inside the same warehouse workflow you already run
Adapter-aware dbt package
The same shared SQL pattern works across Snowflake, Databricks, BigQuery, Redshift, Spark, and Fabric with adapter-aware incremental strategies where needed.
Tuva-native downstream fit
The package expects a Tuva-style upstream contract, which keeps the semantics familiar for healthcare teams already standardizing around those core models.
Scorecard-ready outputs
Results land as aggregate summaries and history tables that can feed operational scorecards, release checklists, and stakeholder reporting.
Next step
Want to see how Spotlight DQ fits your warehouse and data contract?
We can talk through your current cloud platform, upstream source contract, and the operational workflow that needs to be supported before we recommend an implementation path.