Structural validation
Track refreshes, schemas, required fields, data types, and source-level completeness.
Enterprise Application
Data Quality Intelligence
Know whether your healthcare data is complete, valid, and analytically trustworthy before the business depends on it.
Why Data Quality Intelligence
DQI turns hundreds of healthcare-specific checks into a clear operating view for engineering and analytics teams.
Data Quality Intelligence systematically profiles every source running through Tuva, from atomic field-level checks to the downstream analytical domains those issues can affect.
Teams can compare source health at a glance, drill into failing tests, and understand whether data is ready for use cases such as utilization, readmissions, demographics, and risk adjustment.
- Visibility
- One view across structural, logical, and analytical quality
- Traceability
- Drill from source to table to the exact failing test
- Impact
- Connect raw-data issues to affected analytics workflows
See the application in action
Inside the application
01 / 03
See quality across every connected source
Connect source freshness and record volume to readiness across the Core Data Model, marts, and enterprise applications.
Source-level scores surface the feeds that need investigation before downstream teams feel the impact.
Application views
What your team can do
Give every team a shared, healthcare-specific way to detect, prioritize, and resolve data issues.
Logical testing
Surface invalid, duplicated, inconsistent, and referentially broken records with precise counts.
Analytical profiling
Evaluate whether downstream outputs look complete and believable by healthcare domain.
Operational monitoring
Compare sources over time and focus engineering effort where quality risk is highest.
How it works
DQI creates a repeatable path from detection to validation.
- 1
Monitor every source
Review current structural, logical, and analytical status in one place.
- 2
Trace the issue
Drill into the affected table, test, or analytical domain to isolate the root cause.
- 3
Fix and verify
Correct the source or transformation, rerun Tuva, and confirm the issue is resolved.
Put Data Quality Intelligence to work on your data.
Walk through your current data sources and see how DQI would make quality risk visible to your team.