Data Governance
Measure data quality the way the business experiences it.
Autonera scores each domain across quality dimensions, shows whether quality is improving and turns failing rules into owned remediation work.
The capability
Quality as an operating measure, not an annual cleanup
Data quality problems are usually discovered downstream: a filter on the website misses half the stainless fasteners because material is written three ways, or a reorder fails because two items share a part number. By then the cost is already incurred.
Data Quality Management measures quality continuously. Rules are grouped by dimension: completeness for missing values, validity for values outside governed lists or formats, consistency for conflicting representations and uniqueness for duplicates. Scores are calculated per domain, category, source or owner, so teams can see where quality is weak rather than a single enterprise number.
Each failing rule links to the affected records and to remediation: a bulk correction, a routed stewardship task or an agent-prepared fix awaiting approval. Trends show whether quality is improving after a supplier onboarding, a migration or a policy change.
How it works
A data quality dashboard you can act on
Six fastener records are scored across four dimensions. Remediate a failing rule to see the dimension scores and trend update.
Overall quality
79.1
Fasteners domain · 6 records
completeness
83.3
validity
83.3
consistency
83.3
uniqueness
66.7
TREND · LAST FIVE MEASUREMENTS
Sample domain data. The latest point updates as you remediate.
- completenessName present
- completenessMaterial present
- completenessSupplier assigned
- validityUnit in governed list
- consistencyMaterial uses controlled casing
- uniquenessSKU is unique
Problems it solves
Where this work usually breaks down
Quality nobody measures
Teams sense the data is poor but cannot show where or how much.
Cleanup projects that decay
A one-time cleanup improves data for months, then new sources degrade it again.
Scores with no owner
Dashboards report problems without assigning anyone to fix them.
Rules scattered across tools
Quality checks live in import scripts, spreadsheets and reports with different definitions.
Functional capabilities
What Data Quality Management includes
- Quality dimensions
- Organize rules by completeness, validity, consistency, uniqueness and custom dimensions.
- Domain rules
- Define rules for products, suppliers, customers, locations and reference data.
- Scoring and segmentation
- Score by domain, category, source, brand or owner.
- Trend monitoring
- Track quality over time and alert on deterioration.
- Remediation paths
- Fix failures through bulk updates, stewardship tasks or approved agent corrections.
- Governance dashboards
- Give data owners and leadership a shared view of quality and progress.
Autonomous operations
How autonomous execution changes data quality management
Agents evaluate rules continuously and prepare remediation, while data owners define rules, approve corrections and decide priorities.
Agents carry out
- Evaluate rules on every change
- Prepare corrections for routine failures
- Route complex failures to stewards
- Alert owners on deterioration
People decide
- Define rules and thresholds
- Approve bulk corrections
- Set remediation priorities
Evidence recorded
- Score history by dimension
- Corrections and approvals
- Rule versions in effect
In industrial operations
A fastener distributor stabilizes quality after a supplier consolidation
Three supplier catalogs were merged into one fastener range. Units, material values and part numbers conflict.
- Quality rules for the fastener domain are activated across four dimensions.
- Scores are segmented by source supplier to show where problems originate.
- Unit and casing issues are corrected in bulk after approval; duplicates go to stewardship.
- Quality is monitored weekly, with alerts if a supplier's next file lowers scores.
Result: The team can show quality by supplier and dimension, and new problems are caught on arrival.
Related capabilities
Works alongside
Validation & Completeness
Category-specific rules, severities and completeness scores with clear explanations.
Feature guideData Stewardship
Owned work queues, service levels and evidence-backed exception resolution.
Feature guideData Profiling & Rule Discovery
Profile a new dataset and turn observed patterns into reviewed validation rules.
Feature guideData Lineage & Audit
Trace every value from source through transformation, approval and destination.
Capability areaData Governance
See where Data Quality Management fits in the wider Data Governance capability.
Buyer FAQs
Questions evaluators ask
Which quality dimensions are supported?
Completeness, validity, consistency and uniqueness are standard. Additional dimensions such as timeliness can be configured.
Does this cover master data beyond products?
Yes. Rules can apply to supplier, customer, location and reference data domains.
Can corrections be applied automatically?
Routine corrections can be applied within approved policy. Others require review.
How is this different from Validation & Completeness?
Validation enforces rules on individual records. Data Quality Management measures, trends and manages quality across domains.
Where do rules come from?
They are defined by data owners and can be suggested from profiling.
Your next step
See Data Quality Management on data like yours.
Bring a real file, catalog or record set. We will walk through how Autonera would handle it and where your team keeps the final say.
Book a Demo