Governance that passes audit, enables mission.

Overview: why governance is the federal data differentiator

A federal agency with mediocre data governance ships slowly, defends poorly, and loses Inspector General audits. A federal agency with strong data governance moves faster, shares data safely with partner agencies, and can answer "where did this number come from" in thirty seconds. The difference is worth millions in programmatic dollars and is the single biggest predictor of whether a data modernization initiative survives its first Secretary change.

Governance in 2026 is not a steering committee and a SharePoint. It is software: automatic catalogs that register every new table, column-level lineage graphs captured from pipeline metadata, classification tags that travel with the data, access policies expressed as code and enforced at query time, and dashboards that prove to auditors the controls are actually in place. Precision Federal builds this stack — open-source where possible, enterprise when an agency has already paid for it — and wires it into the full data estate.

Our technical stack

• Catalogs: DataHub, OpenMetadata, Collibra, Alation, Atlan, AWS Glue Data Catalog, Unity Catalog (Databricks), Microsoft Purview, Apache Atlas, Apache Polaris.
• Lineage: OpenLineage (Spark, Airflow, Flink, dbt integrations), Marquez, DataHub lineage, Unity Catalog lineage.
• Classification & DLP: Immuta, Privacera, BigID, Microsoft Purview classification, AWS Macie, Azure Information Protection.
• Policy as code: Open Policy Agent (OPA), Rego, Lake Formation policies, Unity Catalog ABAC, Ranger, row-access policies in warehouses.
• Quality: Great Expectations, Soda, dbt tests, Monte Carlo patterns.
• Workflow: Jira Data Center, ServiceNow, custom React/Next.js portals for agency-specific stewardship.
• Identity: Okta Federal, Entra ID Gov, ICAM, Ping Federal, SAML/OIDC federation.
• Audit: Splunk Enterprise, Microsoft Sentinel, Google Chronicle, ElasticSearch for SIEM ingestion of governance events.

Federal use cases

• SAMHSA Privacy Act-governed behavioral health data (confirmed past performance): classification, handling, and downstream lineage on data subject to strict privacy restrictions.
• Army enterprise data catalog (pursuing): cross-command cataloging of sustainment, personnel, and training data.
• Navy data rights and CUI handling (pursuing): vendor-data rights tracking across sustainment and acquisition programs.
• FBI investigative data cataloging (pursuing): case-file classification, chain-of-custody, and restricted-access stewardship.
• HHS / CDC public health data stewardship: SORN accounting, de-identification certification, public-release pipelines.
• Treasury Evidence Act data inventory: enterprise data asset registration under the Federal Data Strategy and Evidence Act requirements.
• DHS cross-component data sharing: governance plane enabling CBP, ICE, USCIS, and TSA to share relevant data without breaching component boundaries.
• DOE national-lab data rights: IP and distribution rights tracking across labs.
• NASA science data open release: automated open-data release pipelines with classification gates.
• GSA federal-wide data commons: shared taxonomy, metadata standards, and lineage across programs.

Reference architectures

Architecture 1: DataHub as the federal catalog plane

DataHub deployed on EKS in AWS GovCloud or AKS in Azure Gov. Ingestion connectors for Snowflake, Redshift, Databricks, dbt, Airflow, Tableau, Power BI, Postgres, Oracle. OpenLineage emitters on every pipeline producing column-level lineage. Custom aspect types for CUI classification, SORN reference, retention schedule, and data steward ownership. Federation with the agency identity provider. Glossary tied to agency data taxonomy. Auto-propagation of tags through lineage. Jira integration for access requests and steward tasks.

Architecture 2: Unity Catalog governance on Databricks (Azure Gov)

Databricks Unity Catalog as the single governance plane over Delta Lake on ADLS Gen2. ABAC policies enforcing row-level and column-level access based on tags. Auto-classification via Databricks AI + Purview integration. Delta Sharing for cross-agency data sharing with revocable grants. Lineage UI native. Unity Catalog federation for external lineage sources (dbt, Airflow). Purview as the broader enterprise catalog federating across non-Databricks assets.

Architecture 3: Collibra / Alation enterprise catalog for a heavyweight agency

For agencies that have invested in enterprise governance tools, we extend rather than replace. Collibra or Alation as the business-facing catalog, OpenMetadata or DataHub as the technical metadata layer beneath, bridge connectors synchronizing technical metadata upward. OpenLineage capturing real-time lineage that Collibra's or Alation's UI renders. Stewardship workflows live in Collibra; engineers live in dbt and Airflow; the catalog stitches both worlds.

Delivery methodology

1. Discovery: inventory systems of record, SORNs, existing policies, existing catalogs, gaps. Interview stewards and engineers.
2. Taxonomy & policy: classification scheme (CUI categories, PII tiers, public), retention, masking policy, access model. Expressed in Rego or the catalog's native policy language.
3. Catalog bring-up: tool selection, ingestion connectors, first pass at cataloging, steward assignment, glossary import.
4. Lineage bring-up: OpenLineage on orchestrator, dbt, Spark. Column-level lineage surfaced in catalog.
5. Policy enforcement: row-level and column-level access policies pushed to warehouses, lakes, and BI layer.
6. Continuous monitoring: governance dashboards, coverage metrics, drift detection, audit evidence generation.
7. Training & handover: steward training, engineer training, runbooks, playbooks.

Engagement models

• SBIR Phase I — governance-focused prototype, $150K-$250K.
• SBIR Phase II — enterprise governance build, $1M-$2M.
• Fixed-price catalog bring-up — 90-180 days, $150K-$500K.
• T&M under prime on CIO-SP4, Alliant 2, GSA MAS, OASIS+.
• OTA prototype for rapid DoD prototyping.
• Staff augmentation — embedded governance engineer.
• Evidence Act support task under an existing evaluation contract.

Maturity model

• Level 1 — Inventory: spreadsheet catalog, ad-hoc classification, manual lineage.
• Level 2 — Catalog: technical metadata automatically ingested, steward assignments, basic glossary.
• Level 3 — Classified: CUI/PII tags enforced, row- and column-level policy, column-level lineage.
• Level 4 — Federated: cross-system catalog, cross-agency sharing, automated Privacy Act accounting.
• Level 5 — Continuous: governance evidence auto-generated, drift alerting, zero manual audit prep.

Deliverables catalog

• Catalog deployment (DataHub, OpenMetadata, or chosen enterprise tool).
• Ingestion connector catalog for every in-scope system.
• Taxonomy, classification policy, retention schedule.
• Rego / policy-as-code repository.
• OpenLineage integration across orchestrator, dbt, Spark, Flink.
• Steward role definitions and RACI matrix.
• Access-request and approval workflows.
• Governance dashboards and coverage metrics.
• Data dictionary generation pipeline.
• NIST 800-53 control mapping for AC, AU, MP, SC, SI, CA.
• Privacy Act documentation (PIA, SORN references, notice inheritance).
• Evidence Act data inventory registration.
• Training materials and stewardship playbook.

Technology comparison

• DataHub: open-source, strong lineage via OpenLineage, active community, extensible with aspects. Best for agencies wanting flexibility without license cost.
• OpenMetadata: open-source, simpler UX than DataHub, batteries-included connectors. Best for smaller governance programs.
• Collibra: enterprise-grade, production-grade workflow, expensive. Best for large civilian agencies with dedicated steward teams.
• Alation: similar to Collibra, strong BI-tool integration. Best for BI-heavy agencies.
• Atlan: modern UX, strong for teams doing data-mesh. Federal path still maturing.
• Unity Catalog: tightest integration with Databricks. Best when Databricks is the primary compute.
• Microsoft Purview: best for M365 / Azure-heavy agencies. Native Sentinel integration.

Federal compliance mapping

• AC-2, AC-3, AC-6, AC-16, AC-21: access enforcement, attribute-based controls, information sharing constraints.
• AU-2, AU-6, AU-12: access audit, anomaly detection over steward activity.
• CA-7: continuous monitoring dashboards as governance evidence.
• CM-8: data asset inventory as system component inventory evidence.
• MP-2, MP-3, MP-4: media protection via classification tags that travel with data.
• RA-2, RA-3: risk categorization on catalog entries.
• SC-7, SC-16: boundary enforcement via policy-as-code.
• SI-12: information management and retention enforced in catalog.

Governance is also how agencies implement Privacy Act routine-use limits, Evidence Act data inventory requirements, and Federal Data Strategy principles. We write the policy language, encode it in the catalog, and produce the evidence.

Sample technical approach: standing up DataHub for a civilian agency

The agency has 38 source systems, two warehouses, 110 dashboards, an aging SharePoint-based glossary, and zero automated lineage. Approach: deploy DataHub on EKS in GovCloud with Postgres metadata, Elasticsearch search, Kafka-based ingestion. Wire ingestion connectors for Snowflake, Oracle, Postgres, Tableau, Power BI, dbt, Airflow. Deploy OpenLineage on Airflow and dbt emitting to DataHub. Build custom aspects for CUI category, SORN reference, data steward, retention. Populate classification via hybrid approach: rule-based for obvious fields (SSN, DOB), ML-assisted for ambiguous ones (free-text descriptions), human review for the remainder. Assign stewards per domain. Wire Jira integration for access requests. Deliver steward training. Outcome: within 120 days, 95% of data assets cataloged, column-level lineage across all pipelines, classification coverage above 80%, and the auditor's "where did this number come from" question answered in clicks.

Past performance

Related capabilities, agencies, and insights

Governance touches every data-platform capability: data warehousing, data lakes, ETL / ELT, BI, responsible AI, observability. Agency pursuits: SAMHSA, Army, Navy, FBI, HHS, Treasury. Vehicles: SBIR, GSA MAS, CIO-SP4. Insights: Automating CUI classification, Evidence Act data inventory, DataHub vs Collibra.