Data Governance Consulting and Implementation Services

Data governance consulting from PS3G gives your organization clear ownership of its data, written rules for how that data is defined, protected and used, and the tooling that keeps those rules working after we leave. It is for organizations of every size: a founder whose customer list lives in four places, a growing company whose reports disagree with each other, or a department inside a larger group that has to answer to auditors and an AI steering committee. We assess where you are, design a framework and operating model that fits, write the policies, name the owners and stewards, set up quality monitoring and a catalog, and stay on as long as you need us to make it stick.

Data governance that fits a 20-person company and a 2,000-person one

Data governance is the set of decisions, roles and rules that determine who is accountable for your data, what “correct” means for each important field, who may access what, and how long you keep it. The practice is the same at every size; what changes is how much you write down and how many people are involved.

A 20-person company needs one owner per critical dataset (customers, products, orders, employees), a two-page policy, a shared glossary of ten or twenty business terms, and a monthly half hour to review data problems. That can live in a spreadsheet and a SharePoint page. Our data governance for small business engagements look like this: light, fast and documented well enough to survive a key person leaving.

A 2,000-person company needs the same things multiplied across domains and systems: a data governance council, domain owners in finance, operations, sales and HR, named stewards, published standards, automated quality checks, and a catalog with lineage so an analyst can trace a board-report number to its source. That is what people mean by enterprise data governance, and it needs an operating rhythm, not just documents.

Our data governance consulting services are scoped to where you sit on that range. We do not bring a 2,000-person program to a 20-person company, or hand a 2,000-person company a spreadsheet.

When you need data governance consulting

Most organizations call a data governance consultant after one of these has happened more than once:

• Conflicting reports. Finance reports one revenue number, the sales dashboard shows another, and nobody can explain the gap because “customer” is defined differently in each system.
• Audit or customer findings. A SOC 2 auditor, a bank or a hospital system asks who owns your data, how it is classified and how you enforce retention. The answers are in people’s heads.
• AI projects stalling. A chatbot or forecasting pilot works in the demo and fails in production because the data is incomplete, stale, or was never cleared for that use.
• Duplicate customer and product records. The same customer exists three times with three addresses, and the CRM, ERP and billing systems disagree on which is right.
• Regulations and contracts. HIPAA, GLBA, PCI DSS, state privacy laws or a customer contract require classification, access control and retention rules you cannot currently prove you follow.
• A migration or new platform. You are moving to the cloud, replacing an ERP or building a data warehouse, and nobody agreed on definitions before the pipelines were built.

If two or three of these sound familiar, a data governance assessment is the right first step: it is short, and it tells you which problems are worth fixing first. This is general information, not legal advice; check the current official text of any regulation that applies to you.

What we deliver

Every engagement is built from the components below. A fixed-scope project might include three; a managed program includes all of them over time.

Deliverable	What you receive	Why it matters
Assessment and maturity score	Interviews, a system and dataset inventory, a scored maturity rating per capability and a prioritized findings list	Tells you what to fix first and gives leadership a baseline
Framework and operating model	A written framework aligned to the DAMA-DMBOK knowledge areas, with a centralized, federated or hybrid operating model that fits your structure	Decides who decides, so governance does not depend on one person
Policies and standards	A data governance policy, classification and retention standards, data quality standards, access and sharing rules, and an exception process	Short, approved documents that auditors and new hires can actually read
Roles: owners, stewards, council	Named data owners per domain, business and technical stewards, a council with a charter, and a RACI for the decisions that matter	Accountability is the part most programs skip, and the reason they fail
Data quality rules and monitoring	Rules for critical data elements (completeness, validity, uniqueness, timeliness, consistency), automated checks and a scorecard reviewed on a schedule	Turns “the data is bad” into a list of specific, assignable defects
Catalog and lineage tooling	A business glossary, a catalog of datasets and reports with owners, and lineage from source to report, in the tool that fits your stack	Lets anyone find trusted data and trace a number to its source
Master data management	Golden-record design for customers, products, suppliers or patients; match, merge and survivorship rules; the integration pattern that keeps systems in sync	Ends the duplicate-record problem at its source
Retention and classification	A classification scheme (public, internal, confidential, restricted) applied to real datasets, a retention schedule, and the controls that enforce both	Required by most regulations and contracts; reduces what you must protect
Training	Role-based sessions for owners, stewards and everyday users, plus short reference guides	People follow rules they understand and helped shape

Our master data management services and catalog work connect directly to our data engineering and analytics and BI practices, so pipelines and dashboards are built on the definitions governance has agreed. That is also the practical difference between data governance and data management consulting: governance decides the rules, engineering applies them.

Our 90-day start

A data governance program that tries to govern everything at once governs nothing. We start with ninety days, three phases, and the two or three data domains that matter most.

Weeks 1 to 4: assess

We interview the people who produce and consume your most important data, inventory systems and datasets, sample quality on the fields that drive revenue, compliance or operations, and score your maturity. The output is a short report, a data governance roadmap and a recommendation on which two or three domains to start with.

Weeks 5 to 10: quick wins on critical data domains

For each chosen domain (customer, product, patient, vendor or employee) we name the owner and steward, agree the definitions of its critical data elements, write the quality rules, fix the worst defects, and publish the glossary entries and first catalog pages. The point is a visible result: a report people trust, a duplicate problem that stops growing, an audit question with a written answer.

Weeks 11 to 13: operating rhythm

We set up the council or working group, its agenda and cadence, the quality scorecard, the issue log and the exception process. Then we hand over or stay on, depending on the engagement model you chose. Either way, you leave with a data governance implementation that runs on a calendar, not on enthusiasm.

Ask for a fixed-scope assessment and we will tell you which domains to start with.

Tool-agnostic: Microsoft Purview, Collibra, Alation, Atlan, Informatica, open source, or spreadsheets

We do not resell governance software, so we recommend the tool that fits your stack, budget and the number of people who will actually use it. The rules come first; the tool records and enforces them.

Option	Typically fits	Notes
Microsoft Purview	Organizations on Microsoft 365, Azure, SQL Server, Fabric or Power BI	Data map, catalog, classification, sensitivity labels, lineage and data quality in one service; a natural fit for .NET and Azure shops
Collibra	Larger organizations with many domains and formal workflows	Mature governance workflows, glossary, policy management and data quality
Alation	Analytics-heavy teams that want a catalog analysts will use daily	Search-first catalog with usage-based ranking, governance workflows and lineage
Atlan	Teams on a modern cloud data stack (Snowflake, Databricks, dbt, BigQuery)	Active metadata, column-level lineage and integrations with the tools engineers use
Informatica	Organizations with heavy integration, quality and MDM needs	Cloud data governance and catalog, data quality and master data management in one platform
Open source (OpenMetadata, DataHub)	Teams with engineers who can run and extend a platform	Catalog, lineage, glossary and quality tests without license cost; you own hosting and upkeep
Spreadsheets and SharePoint	Any organization in its first ninety days	A glossary, owner register and issue log in a shared workbook; move to a tool when it stops scaling

Cloud data governance adds one more layer: the native catalogs and access controls of Azure, AWS and Google Cloud, and of platforms such as Databricks Unity Catalog and Snowflake. We design the governance model to work across them rather than being locked to one. Our guide to choosing data governance tools compares the options in more depth.

Data governance for AI: the prerequisite

Every AI use case, from a support chatbot that answers from your documents to a model that forecasts demand, is a consumer of your data. If the data has no owner, no quality rules and no record of what it may be used for, the AI inherits those problems and adds its own.

AI readiness means four governance questions have written answers: which datasets may be used for training or retrieval and who approved it; whether the data contains personal, health or confidential information; where it came from and how fresh it is; and which access rights apply, so a retrieval system never surfaces a document to someone who could not open it directly.

We treat data governance as the first phase of AI consulting and carry the answers into the AI governance program that follows. That order is slower for the first month and faster for every month after.

Engagement models

• Fixed-scope project. A defined deliverable at a fixed price: an assessment, a policy set, a catalog implementation, or the full 90-day start.
• Dedicated team or staff augmentation. A data governance consultant, data quality analyst or catalog engineer inside your team on a monthly basis, scaled up or down as the program grows.
• Managed services. We run the operating rhythm under defined SLAs: stewardship support, quality monitoring, catalog upkeep, council facilitation and reporting.
• Advisory. An assessment, a data governance strategy and roadmap, or a fractional data leader who attends your council.

How we start: a first conversation to understand the problem, a short assessment, then a written proposal with scope, deliverables and a fixed-scope quote. The same path applies across all our services, whether you are a founder or a division of a larger group.

Why PS3G

We are a data governance company that also runs a regulated data platform. We build and operate our HIPAA-compliant EHR and telehealth platform, DocVilla, used by medical practices across multiple states. Classification, access control, retention, audit trails and data quality are things we live with daily in our own product, not just recommend.

We have been building software for more than ten years from our headquarters in Wilmington, Delaware, with a delivery center in India for scale and round-the-clock support. Our engineers are certified across Microsoft and open-source stacks, so we can implement governance in Purview and SQL Server as comfortably as in OpenMetadata and PostgreSQL. And because we also provide technology consulting, data engineering, analytics and AI development, governance connects to the systems we help you build and run instead of ending as a forgotten document.

Related services

• AI governance: the policies, risk assessments and inventory that sit on top of governed data.
• Big data and data engineering: the pipelines, warehouses and lakehouses that governance rules are applied to.
• Data analytics and BI: dashboards and reporting built on agreed definitions.
• IT compliance consulting: SOC 2, NIST and policy work that depends on classified, owned data.
• HIPAA compliance consulting: PHI classification, minimum necessary and retention for healthcare organizations.
• AI consulting: use-case selection and proofs of concept that start with data readiness.

Guides on this topic

• What Is Data Governance? A Plain-English Guide for Growing Companies
• Data Governance Framework: Components, Examples and a Starter Template
• Data Governance vs Data Management: What’s the Difference?
• Data Governance Best Practices: 12 That Actually Stick
• Data Governance Policy: What to Include (with a Free Template)
• Data Governance Roles and Responsibilities: Owners, Stewards, Councils
• Data Governance Plan and Roadmap: From Assessment to Operating Rhythm
• Data Governance Tools Compared: Purview, Collibra, Alation, Atlan
• Data Governance in Healthcare: HIPAA, Quality, Interoperability
• Data Governance for AI: The Prerequisite Nobody Wants to Fund
• Data Quality Management: Dimensions, Rules, Tools and Process
• Data Lineage: What It Is, Why It Matters and How to Capture It
• Data Classification and Data Retention Policy: Templates and Examples
• Master Data Management (MDM): What It Is and When You Need It
• AI Governance vs Data Governance: Differences and Overlaps

Frequently asked questions

What does a data governance consultant actually do?

A data governance consultant assesses how your data is owned, defined, protected and used today, then designs and helps implement the framework, policies, roles, quality rules and catalog that fix the gaps. The work is part facilitation (getting finance, operations and IT to agree on definitions and ownership) and part implementation (configuring tools, writing rules, training stewards). The output is a program that runs without the consultant.

How long does a data governance implementation take?

A first, useful version takes about ninety days: an assessment, quick wins on two or three critical data domains, and an operating rhythm. Extending governance to every domain and system is a year-one roadmap in most organizations, and the program then continues as routine operations. Timelines depend on how many systems you run and how quickly owners can be named, so we scope them after a short assessment.

Do we need to buy a data governance tool before we start?

No. The first ninety days can run on a shared workbook: a glossary tab, an owner register, an issue log and a quality scorecard. A tool such as Microsoft Purview, Collibra, Alation, Atlan or OpenMetadata becomes worth it when the number of datasets, users or systems outgrows the workbook, or when you need automated classification, lineage and quality checks.

Is data governance only for large companies?

No. A small company has fewer systems and people, so governance is lighter, but the questions are the same: who owns the customer list, what “active customer” means, who can export it, and how long you keep former customers’ data. A two-page policy, three or four named owners and a monthly review are enough to start, and they make audits, due diligence and AI projects easier later.

How does data governance relate to AI governance?

Data governance covers the data: ownership, definitions, quality, classification, access and retention. AI governance covers the models and systems that use that data: inventory, risk assessment, acceptable use, human oversight and monitoring. AI governance depends on data governance because every model question (what was it trained on, may we use this data, is it accurate) is answered by the data program. We usually run them as one connected effort.