Enterprise Data Governance Platform

The Vendor is required to provide enterprise data governance platforms, services, and related offerings (collectively, “Offerings”) that can support a comprehensive, federated data governance program across the Agency’s hybrid data environment.
- Evaluating the market landscape and potential implementation approaches for an enterprise data governance platform that improves data trust, transparency, quality, controlled access, and auditability across operational and analytical use cases, while enabling a federated governance operating model.
- Strategic Context and Business Objectives
•    Integrated customer & stakeholder data: enable consolidated, governed views of customer and stakeholder information across systems to support coordinated service delivery and case management.
•    Supplier information management: enforce consistent supplier records and classifications across procurement and finance processes.
•    Regulatory compliance and auditability: ensure critical reporting data is complete, current, and traceable with audit trails.
•    Analytics and business enablement: standardize definitions and authoritative sources for reports, dashboards, and analytical outputs.
•    Data product governance: apply controls to published datasets (e.g., bronze/silver/gold tiers) used for AI/ML and operational processes.
•    Cloud migration and system consolidation: establish inventory, ownership, and controls to reduce rework and risk during transformations.
- Platform capabilities and services that may include, but are not limited to:
•    Enterprise-wide data cataloging and automated discovery/metadata harvesting.
•    Business glossary and domain-driven metadata stewardship.
•    Data quality profiling, monitoring, alerting, issue management, and remediation workflows.
•    Data lineage (including column-level where available) and impact analysis.
•    Policy management, sensitive data classification, access request/approval workflow, and enforcement integration.
•    Integration with hybrid data sources (on-premises and cloud), extract, transform, load / extract, load, transform, business intelligence (ETL/ELT, BI) tools, and identity providers.
•    Role-based dashboards and reporting for executives, technical administrators, and non-technical operations data stewards.
•    AI tools used in or as part of the solution for data discovery and compliance
•    Options to implement data governance enforcement including reporting and rectifying data overlap or non-compliance at all levels in the organization.
•    Ability to track data at different levels as appropriate for the catalog/owner in a cascading or waterfall approach.
- Cloud data platform components (representative):
•    Microsoft azure (primary cloud platform).
•    Azure synapse analytics (enterprise data warehousing and big data analytics).
•    Azure data bricks (data engineering and data science workloads; unity catalog enabled).
•    Azure data factory / orchestration tools (ETL/ELT and pipeline scheduling).
•    Azure data lake storage gen2 (data lake; medallion architecture – bronze/silver/gold).
•    Azure SQL databases (line department application data).
- Business challenges
•    Data is distributed across departments and systems; presenting an opportunity to further align definitions and strengthen authoritative sources.
•    Business and technical metadata varies in completeness and consistency, creating an opportunity to improve the discoverability and understanding of data assets.
•    Data quality monitoring and remediation are performed in multiple ways today, creating an opportunity for a more centralized, consistent approach across domains.
•    Data lineage visibility exists, in some areas, presenting an opportunity to expand end-to end lineage to support root-cause analysis and compliance or audit readiness.
•    Governance activities (e.g. Stewardship, policy exceptions, and approvals) are supported through a mix of tools and processes, creating an opportunity to streamline and standardize workflows.
•    Ownership and stewardship roles are defined across many areas, creating an opportunity to clarify responsibilities and decision rights across a decentralized environment.
•    Some governance tooling and workflows are optimized for technical users, creating an opportunity to better support non-technical operations staff serving as data stewards.
- Target-state capabilities and outcomes
•    Clear assignment of data ownership and stewardship, supported by workflow-driven accountability.
•    Improved data integrity, completeness, and consistency with measurable kpis and quality scorecards.
•    Controlled and auditable access to data assets aligned to roles, sensitivity, and policy.
•    End-to-end traceability (lineage) to support impact analysis, incident response, and compliance audits.
•    Self-service discovery and stewardship tools for non-technical operations staff, including no-code/low-code rule definition and human-in-the-loop exception handling.
•    Robust reporting environment for data governance review, discrepancy discovery and resolution, and audit reporting.
- Example use cases
•    Non-technical data stewards monitor data integrity and quality KPIS for critical operational datasets (e.g., asset inventories, work orders, service events) and manage remediation workflows for issues identified.
•    Business users discover trusted datasets via catalog search, understand definitions via business glossary, and view lineage to confirm fitness for use.
•    Data stewards review and approve data access requests, with policy-based routing, time bound grants, and complete audit trails.
•    Governance teams identify and classify sensitive data (PII/phi/financial) and validate masking/tokenization controls.
•    Technical teams perform impact analysis before schema or pipeline changes using interactive lineage and downstream dependency views.
•    Executive stakeholders monitor governance health metrics, stewardship activity, and compliance evidence through dashboards.