Challenges We Solve
Governance gaps that structured controls and audit trails eliminate.
Data Quality Decay
Bad data silently poisons every downstream decision. Score data quality at the source, detect decay trends, and enforce data contracts with automated monitoring.
Unknown Data Lineage
Nobody knows where this number came from. Map data lineage from source to dashboard, detect dependency breaks, and trace impact across every model.
PII Exposure Risk
Sensitive data leaks into test environments and analytics pipelines. Scan for PII, classify sensitivity, and enforce redaction policies automatically.
Schema Drift
Upstream schema changes break downstream models without warning. Detect schema mutations, validate data contracts, and alert owners proactively.
Use Cases
Real governance scenarios powered by DecisionLedger.
Uses the data quality scorecard model to certify every data source feeding executive dashboards, scoring completeness, freshness, and consistency with automated monitoring.
Zero uncertified data sources in production analytics pipelines
Runs PII discovery scans across all data stores, classifying sensitivity levels and enforcing redaction policies before data enters decision models or analytics pipelines.
PII exposure incidents reduced to zero with automated classification
Maps data lineage from source systems through transformations to final dashboards, detecting when upstream schema changes will break downstream models before they fail.
Schema drift detected 48 hours before it would break production models
Measurable Impact
Based on platform benchmarks across early adopters.
Connects With
Featured Models
Pre-built decision models ready to run with your data.
Analytics Cost Optimizer
Attributes compute and storage spend to products, teams, and workloads across the data stack. Identifies biggest cost drivers and recommends query tuning, schedule changes, caching, and tiering.
Data Contract Validator
Implements explicit producer-consumer contracts for key datasets and events. Validates freshness, schema, and business rules. Produces pass/fail evidence.
Data Quality Impact Model
Measures how data gaps, staleness, or uncertainty affect confidence in the decision outcome.
Data Quality Scorecard
Computes dataset quality scores (completeness, validity, timeliness, consistency) by domain and table. Routes failures to owners with prioritized fix queue.
Headcount Data Integrity
Catch HRIS data integrity issues using anomaly detection.
Lineage Dependency Mapper
Builds end-to-end lineage from source systems to semantic models, dashboards, and downstream consumers. Flags critical dependencies and breaking changes.
Pii Discovery Redaction
Scans data assets for PII patterns and classifies sensitivity tiers. Enforces masking, tokenization, or redaction rules and emits audit-ready reports.
Schema Change Detection
Monitors schemas for drift (new columns, type changes, missing fields) and runs compatibility checks before pipelines deploy. Generates go/no-go decision with rollback steps.
How It Works
Three steps to structured, auditable decisions.
Discover & Classify
Scan data assets, classify sensitivity, map lineage dependencies, and register data contracts across your entire data estate.
Monitor & Enforce
Continuous data quality scoring, schema change detection, and PII scanning. Automated alerts when quality or contracts breach thresholds.
Optimize & Govern
Track data quality trends, identify costly data pipelines, and generate compliance evidence for GDPR, CCPA, and HIPAA.
Your executive dashboard shows revenue up 12%. Your data team knows the source feed has been stale for 3 weeks. Who tells the CEO?
Collibra / Alation catalogs
Data catalogs that document metadata but don't score quality or enforce contracts
Manual data quality checks
SQL scripts run by engineers who have better things to do — and forget
dbt tests alone
Schema validation without lineage impact analysis or business quality scoring
Compliance spreadsheets
GDPR and CCPA tracking in documents that can't scan data or enforce policies