Data Governance Services

Start with one domain where poor governance causes visible pain, such as conflicting revenue figures or uncontrolled access to customer data. Define a small set of roles, a handful of policies and clear success measures, and implement them in the tools people already use. Once that domain shows results, extend the model. Councils and formal policies should be introduced only as fast as they are needed to make decisions, not as a prerequisite.

It depends on size, regulatory pressure and how independent your business units are. Centralized models suit smaller or heavily regulated organizations that need tight control. Federated models give domains ownership of their data within shared standards and suit most mid-size and large enterprises. Data mesh goes further, treating domain data as products. We assess your context and often recommend a federated model with central standards and platform-enforced policies.

A data steward is the business expert accountable for the meaning and quality of data in a domain. Stewards maintain glossary definitions, approve quality rules, triage data issues, review access requests and act as the contact point for questions about their data. To succeed, they need protected time, clear authority and tooling such as a catalog and issue workflow. We define the role, train stewards and set realistic expectations for their workload.

Not always. If your data lives mainly in one platform, native capabilities in Microsoft Purview, Databricks Unity Catalog or Snowflake Horizon may cover your needs at lower cost. Dedicated catalogs add value across many heterogeneous systems, with richer workflows, business-friendly interfaces and broader connectors. We compare options against your landscape, users and budget, and we can also implement open-source catalogs such as DataHub or OpenMetadata.

AI multiplies governance needs. Teams must know which data may be used for training or retrieval, whether personal data is included, who owns the source and how fresh it is. Governance provides classification, lineage, ownership and access policies that answer these questions quickly. We also help define AI usage policies, document models and datasets, and enforce document-level permissions in retrieval systems so assistants only reveal what each user may see.

We agree metrics at the start, such as the share of critical data elements with an owner, catalog adoption by active users, time to grant data access, quality scores for certified datasets, number of policy exceptions and time to answer audit requests. These are reported in a governance scorecard reviewed regularly with sponsors. Measuring outcomes rather than the number of documents produced keeps the program focused on real business value.