Enterprise data governance your auditors and AI teams can trust

Technical and business metadata converge in a catalog your analysts actually use: domains, ownership, PII flags, and certified datasets. Lineage is captured at job boundaries—Airflow DAGs, dbt models, Spark jobs on Databricks—so impact analysis is factual when schemas change.

We align glossary terms to warehouse columns and lake paths so AI assistants and BI tools surface consistent definitions, reducing shadow metrics across finance, growth, and product.

RBAC and ABAC patterns are mapped to real roles: who may promote datasets, who may export, and who may break glass for incidents. Segregation of duties is encoded in approval workflows—not email chains.

Retention, legal hold, and regional residency rules are attached as policy-as-code where supported, with exceptions logged for audit.

SLIs on freshness, completeness, and uniqueness are co-owned by stewards and platform teams. Breaches route to on-call with context—not generic alerts—so remediation is fast and documented.

We integrate with your existing ticketing and risk registers so governance work is visible to procurement and GRC stakeholders.

Whether you standardize on Snowflake secure views, Databricks Unity Catalog, or hybrid lake patterns on S3 and ADLS, we avoid duplicate policy engines. One mental model: classify once, enforce everywhere connectors run.