Modern data platforms
We build governance into the data platform, so it’s safe to point reports, analysts and AI at your data: quality rules in the pipelines, lineage from source to report and access tied to how sensitive the data is.
Our data governance services
AI has made governance urgent: models, retrieval pipelines and automated workflows are reading data that often hasn’t been classified, access-controlled or quality-checked.
Data quality frameworks
Quality rules define what good data looks like at the source, check it as it moves through the pipelines and raise failures before they compound. At one health plan, our team noticed a nursing facility file was mishandling records; the plan filed corrections and $16M+ in lost Medicaid revenue came back.
Data lineage and cataloging
When something breaks in a report or a model, the first question is always "where does this data come from?" We implement cataloging and lineage tracking that makes data assets discoverable, documented and traceable from source to consumption.
Access control architecture
Access that’s too loose is a compliance risk, and access that’s too tight breeds shadow copies and spreadsheets. We match roles to how sensitive each data set is and enforce the rules in the platform itself, with row-level security and masking where PHI or financial data sits.
Compliance readiness
We build compliance controls into the data platform, so audit evidence comes from how the platform runs: access logs, lineage and change history are already there when the auditor asks.
Governance for AI on your data
AI should read governed data, not raw copies. We give a model the schema and a written description of what the data means, the same way engineers give coding tools context, and it answers by writing a query against data the person asking is allowed to see. The query comes back with the answer, so it can be checked.
Why it comes first
Copilot, agents and AI search answer from whatever they can reach. Before that, the data has to be in one place, checked and limited to what each person is allowed to see. Then the real questions have answers: where did this come from, what’s in it and can we show it’s right?
Sources land once, in one platform, instead of being copied into every tool that asks.
A raw copy of every source stays put, so a mistake in your logic doesn’t become a permanent mistake in your history.
A report, an analyst or an AI agent sees only what the person asking is allowed to see.
The checked, business-ready version is what AI reads, so its answers reflect how the business runs today.
Governance and AI
Companies that use AI governance put 12x more AI projects into production than those without it. The AI Enablement Guide covers the rest of the research.
Our process
We start from what already exists in your environment.
We map your current data sources, pipelines, access patterns and quality baselines, starting from your data readiness assessment if you’ve taken it. The first phase stands on its own: you keep the roadmap whether or not you go further.
Deliverable
A governance maturity scorecard and a prioritized roadmap that identifies the highest-risk gaps.
We define data classification schemes, access control models, quality rules and stewardship responsibilities based on what the assessment revealed.
Quality monitoring in the pipelines, catalog and lineage tooling deployed, access controls configured and compliance evidence generated automatically from platform metadata.
Ongoing
We transfer ownership to your internal teams with documented procedures, monitoring dashboards and stewardship workflows in place. Ongoing advisory is available as your data assets grow, regulations change and AI adoption introduces new governance requirements.
Industries
Patient, pharmacy and clinical data systems held to HIPAA, including where AI is allowed to touch them.
Clients include
ERP, shop-floor and supplier data on one platform, so plant reporting and finance reporting reconcile.
Clients include
Order, inventory, credit and customer data connected across portals, branches and the platforms behind them.
Clients include
Bank data platforms, reporting that holds up to an audit and managed support for the financial systems the business runs on.
Clients include
Claims, member and policy systems for health plans and insurers.
Clients include
Case studies
Healthcare
$16M+
Hired to add a state-mandated code to a health plan’s nursing facility file, our team noticed the process was mishandling records. The plan confirmed it, filed corrections and recovered $16M+.
Healthcare
Turnover had taken the onboarding knowledge with it. Recurring data quality issues across SSIS and MuleSoft feeds were fixed, onboarding was documented and four programs were migrated.
Manufacturing
Six operating entities on one SAP ECC 6.0 system, brought into Fabric with no RFC bulk extraction and entity-level security. The client's Basis and SAP teams found the process much easier than they expected.
Next step
One call about your systems and what needs to change. You get a written proposal if the work fits.
A few practical questions
Questions we hear on first calls.
Ask us about your situationData management is the operational work of moving, storing and transforming data, and governance is the set of policies, roles and controls that keeps that work consistent and measurable. Many organizations have the management in place without the governance layer.
It depends on the complexity of the data environment and the number of source systems involved. A governance assessment comes first and delivers a maturity scorecard, gap analysis and prioritized roadmap, and the implementation of governance infrastructure (quality monitoring, cataloging, access controls) is scoped from what it finds.
No. Governance starts with your data, classification needs and quality baselines, and the tool choice comes after the requirements are clear. Many organizations already own governance features they barely use, in Microsoft Purview, Unity Catalog, Snowflake or dbt. We use what you have before recommending anything new.
Quality issues, inconsistent definitions and ungoverned access in your data show up in what AI produces from it, so we put governance in place before AI reaches production data.
At least the basics. An assistant answers from whatever it can reach, so get the data it will read into one governed place, check the quality of what it reads first and make sure access follows the person asking. Start with the sources that matter most to the first use case, not a company-wide program.
We build governance aligned to HIPAA (healthcare data and PHI) and to general requirements for data privacy, access management and audit readiness. The approach is the same for each: put the controls in the data platform so evidence is generated automatically.
Many governance programs stall because they focus on organizational change (committees, stewardship roles, policy documents) without building the technical infrastructure to enforce and measure those policies. We assess what exists, identify where enforcement gaps are and build the monitoring and automation layer that makes your existing program operational.
Related services
AI readiness starts with governed data. AI on your own data inherits the access rules, definitions and quality checks governance puts in place.
Learn moreBuild the pipelines, warehouse and transformation layer that governance policies actually protect and monitor.
Learn morePurview, workspace boundaries, row-level security and sensitivity labels set once in Fabric for every report and model.
Learn moreAn assessment of your sources, pipelines and gaps, and a roadmap before you commit to a platform or an AI investment.
Learn moreNext step
Tell us where your data lives and where governance breaks down today. Here is what happens after you reach out.
Where the data lives, who uses it and where quality, access or lineage break down.
Scope comes from your systems as they are, before anything is priced.
Covering scope, team, approach and price.
SQL Server
SSIS
MuleSoft
SAP Integration
Lakehouse Architecture