--- title: "Data Governance and Quality Consulting | Smart Data" url: https://www.smartdata.net/services/data-governance description: "Data governance and quality consulting: quality rules in the pipelines, lineage in Purview or Unity Catalog, and access controls for PHI, with audit evidence." source: "Smart Data (smartdata.net). Markdown copy of the page; the HTML page is canonical." --- Modern data platforms # Data governance and quality consulting 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. Platforms we build on - [Microsoft Fabric Including Power BI](https://www.smartdata.net/services/microsoft-fabric-consulting) - [Databricks Lakehouse builds and migration](https://www.smartdata.net/services/databricks-consulting) - [Snowflake Architecture and optimization](https://www.smartdata.net/services/snowflake-consulting) Our data governance services ## Governance enforced in the pipelines 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 ### 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. - Validation rules, freshness checks and anomaly detection embedded in data pipelines - Data quality scoring and monitoring dashboards for stewards and leadership - Remediation workflows that route quality issues to the right team with context Lineage and cataloging ### 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. - Automated lineage from Microsoft Purview, Unity Catalog, dbt and Azure Data Factory - Data catalog deployment with business glossaries and ownership mapping - Impact analysis tooling so teams know what downstream systems are affected before making changes Tools we work in - Microsoft Purview - Unity Catalog - dbt - Azure Data Factory Access control ### 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. - Role-based access control (RBAC) aligned to organizational structure and data classification - Dynamic data masking and row-level security for sensitive fields (PII, PHI, financial) - Access audit logging and periodic review workflows for compliance reporting Compliance readiness ### 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. - HIPAA-governed data environments with PHI tracking, encryption and audit trails - Data pipelines with change management, lineage documentation and access logs - Evidence generation automated from platform metadata for audit and regulatory review AI governance ### 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. - Context for your database: definitions and relationships an AI agent can read - Access rules inherited from the platform, applied to the person asking - Model and query cost tracked before AI workloads scale Why it comes first ## Governed data first, then AI on top 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? ### One governed place Sources land once, in one platform, instead of being copied into every tool that asks. ### Keep the evidence A raw copy of every source stays put, so a mistake in your logic doesn’t become a permanent mistake in your history. ### Access follows the person A report, an analyst or an AI agent sees only what the person asking is allowed to see. ### A curated layer for AI The checked, business-ready version is what AI reads, so its answers reflect how the business runs today. Governance and AI ## Not sure your data is ready for 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. [Source: Databricks State of AI Agents](https://www.databricks.com/resources/ebook/state-of-ai-agents) Our process ## How we build governance that lasts We start from what already exists in your environment. ### 01Discovery and architecture review 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. ### 02Policy and framework design We define data classification schemes, access control models, quality rules and stewardship responsibilities based on what the assessment revealed. ### 03Governance infrastructure build Quality monitoring in the pipelines, catalog and lineage tooling deployed, access controls configured and compliance evidence generated automatically from platform metadata. ### 04Ongoing support and optimization 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 ## Industries we work in ### Healthcare Patient, pharmacy and clinical data systems held to HIPAA, including where AI is allowed to touch them. - Medicaid programs - Pharmacy services - Clinical documentation [Healthcare](https://www.smartdata.net/industries/healthcare) ### Manufacturing and industrial ERP, shop-floor and supplier data on one platform, so plant reporting and finance reporting reconcile. - SAP and ERP reporting - Shop-floor integration - Supplier portals [Manufacturing and industrial](https://www.smartdata.net/industries/manufacturing) ### Retail and distribution Order, inventory, credit and customer data connected across portals, branches and the platforms behind them. - Vendor and customer portals - Order and credit systems - Data platforms [Retail and distribution](https://www.smartdata.net/industries/retail) ### Financial services Bank data platforms, reporting that holds up to an audit and managed support for the financial systems the business runs on. - Bank data platforms - Managed ERP support - Legacy migration [Financial services](https://www.smartdata.net/industries/banking-financial-services) ### Insurance Claims, member and policy systems for health plans and insurers. - Health plans - Claims data - Member apps [Insurance](https://www.smartdata.net/industries/insurance) Case studies ## Data governance in practice $16M+ ### in lost Medicaid revenue returned to the health plan 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+. - SQL Server [in lost Medicaid revenue returned to the health plan](https://www.smartdata.net/case-studies/nfloc-platform-rewrite) ### Reducing data quality issues across four migrated programs 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. - ETL & Data Pipelines - SSIS - MuleSoft [Reducing data quality issues across four migrated programs](https://www.smartdata.net/case-studies/enterprise-grade-data-quality-for-a-pharma-services-provider) ### SAP ECC 6.0 to Microsoft Fabric for a global industrial group 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. - Microsoft Fabric - SAP Integration - Lakehouse Architecture [SAP ECC 6.0 to Microsoft Fabric for a global industrial group](https://www.smartdata.net/case-studies/sap-ecc-microsoft-fabric-industrial) Next step ## Talk to us about scope One call about your systems and what needs to change. You get a written proposal if the work fits. A few practical questions ## Work through the decisions Questions we hear on first calls. Data 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 ## Where governance connects ### AI on your data AI readiness starts with governed data. AI on your own data inherits the access rules, definitions and quality checks governance puts in place. [AI on your data](https://www.smartdata.net/services/ai-on-your-data) ### Data engineering Build the pipelines, warehouse and transformation layer that governance policies actually protect and monitor. [Data engineering](https://www.smartdata.net/services/data-engineering) ### Microsoft Fabric consulting Purview, workspace boundaries, row-level security and sensitivity labels set once in Fabric for every report and model. [Microsoft Fabric consulting](https://www.smartdata.net/services/microsoft-fabric-consulting) ### Data strategy An assessment of your sources, pipelines and gaps, and a roadmap before you commit to a platform or an AI investment. [Data strategy](https://www.smartdata.net/services/data-consultancy) ## Which data will your AI read first? Tell us where your data lives and where governance breaks down today. Here is what happens after you reach out. ### A short call about your data Where the data lives, who uses it and where quality, access or lineage break down. ### An engineer looks at what’s running Scope comes from your systems as they are, before anything is priced. ### You get a written proposal Covering scope, team, approach and price. [info@smartdata.net](mailto:info@smartdata.net) [(937) 886-9166](tel:+19378869166)