Data Quality, Policy Engine & Stewardship
Automated quality monitoring with ML-powered anomaly detection, a declarative governance policy framework, and stewardship workflows that assign accountability to every data asset
Automated quality rules enforce data standards across every dataset. Quality scorecards provide visibility into data health trends over time.
- 50+ built-in quality rule templates
- Quality scores by dataset, domain, and owner
- Historical trending and improvement tracking
- Automated alerting on quality degradation
Automated Data Quality Monitoring
Define quality rules for completeness, accuracy, consistency, timeliness, and uniqueness — then let the platform enforce them automatically. Quality scorecards track trends over time, showing whether data health is improving or degrading across every dataset.
Quality rules can be configured per table, column, or dataset using a declarative syntax. The platform ships with 50+ pre-built rule templates covering common quality dimensions. Custom rules support SQL-based validation for organization-specific requirements.
Quality scores are computed on every data refresh and tracked historically. Dashboards show quality trends by domain, source, and data owner — enabling targeted remediation where it matters most.
- 50+ pre-built quality rule templates (null checks, range validation, format verification)
- Custom SQL-based quality rules for complex validation logic
- Quality scorecards by dataset, domain, and data owner
- Historical trend tracking to measure improvement over time
- Alerting when quality scores drop below configurable thresholds
ML-Powered Anomaly Detection
Machine learning models learn normal data patterns and flag anomalies automatically — volume spikes, distribution shifts, unexpected nulls, format changes. Catch data issues before they impact downstream reports and decisions.
- Statistical anomaly detection on data volumes and distributions
- Schema drift detection catches unexpected column changes
- Freshness monitoring alerts on stale or delayed data
- Root cause analysis traces anomalies back to source systems
- Configurable sensitivity thresholds to reduce false positives
Declarative Policy Engine
Define governance policies as declarative rules that the platform enforces automatically. Map policies across regulations (PDPA, GDPR, internal standards) so one policy satisfies multiple compliance requirements. Version every policy change with full audit trail.
The policy engine supports a hierarchical policy framework: organizational policies inherit from regulatory templates, and department-specific overrides can be applied where needed. This reduces duplication while allowing flexibility.
Impact analysis shows which data assets and processes are affected before any policy change is deployed. This prevents unintended enforcement consequences.
- Declarative policy syntax for human-readable governance rules
- Cross-regulation mapping: one policy satisfies PDPA + GDPR + internal standards
- Policy versioning with full change history and rollback
- Impact analysis before policy deployment
- Automated enforcement with exception management workflows
Data Stewardship & Accountability
Assign data owners and stewards to every domain and dataset. Steward dashboards show accountability metrics: quality scores, open issues, pending approvals. Approval workflows ensure changes to critical data assets go through proper review.
- Data owner and steward assignment per domain, dataset, and column
- Steward dashboards with accountability metrics and KPIs
- Issue tracking and resolution workflows for data problems
- Approval workflows for schema changes, access grants, and policy exceptions
- Governance council support with meeting templates and action tracking
System Architecture
How It Works
Define Rules
Configure quality rules and governance policies using templates or custom SQL. Map policies to regulatory requirements.
Assign Stewards
Designate data owners and stewards for each domain. Set up approval workflows and escalation paths.
Monitor Continuously
Quality rules run on every data refresh. ML anomaly detection monitors patterns 24/7. Alerts notify stewards of issues.
Enforce & Improve
Policies are enforced automatically. Quality trends are tracked over time. Stewardship dashboards drive accountability.
Define Rules
Configure quality rules and governance policies using templates or custom SQL. Map policies to regulatory requirements.
Assign Stewards
Designate data owners and stewards for each domain. Set up approval workflows and escalation paths.
Monitor Continuously
Quality rules run on every data refresh. ML anomaly detection monitors patterns 24/7. Alerts notify stewards of issues.
Enforce & Improve
Policies are enforced automatically. Quality trends are tracked over time. Stewardship dashboards drive accountability.
Use Cases
Financial Data Quality
Ensure accuracy of financial reports by monitoring data quality across accounting systems, ERP, and data warehouses.
Regulatory Compliance
Map PDPA and GDPR requirements to enforceable policies. Demonstrate compliance through quality scorecards and audit trails.
Master Data Management
Maintain consistency of customer, product, and reference data across systems with automated quality enforcement.
Data Migration Validation
Validate data quality during system migrations. Compare source and target datasets with automated reconciliation rules.
Analytics Trust
Build confidence in analytics by ensuring underlying data meets quality standards. Quality badges on datasets indicate trustworthiness.
Cross-Department Governance
Establish governance councils with representatives from each department. Track policy compliance and quality improvements organization-wide.