Data Governance Consulting Services

ALCO reporting, credit risk metric certification, regulatory exam-ready pipelines, NAIC and FINRA records. Banking and credit union engagements.

FOIA-ready pipelines, audit-ready compliance reporting, IPEDS and CEDS data, CMMC controls. Federal and state agency engagements.

IPEDS reporting automation, enrollment and retention metric certification, accreditation data packages. University engagements across the US.

OEE metric certification, supply chain visibility data, quality control governance. Industrial and discrete manufacturing engagements.

We design the operating model: roles, decision rights, and the framework (DAMA-DMBOK, DCAM, or a hybrid) that fits your team's size and your audit obligations, not a template lifted from a Fortune 500.

We define, document, and certify every KPI end to end, so Finance, Sales, and Operations all pull the same revenue from the same source instead of arguing about it in the board meeting.

We map each critical data asset to a named business owner, write down what stewardship means for that person, and set the escalation path for when a number breaks.

We document where each number comes from and who is allowed to see it, so a metric on a dashboard traces back to its source system in one step, not a week of follow-up.

We set the quality bars each certified asset has to clear, wire the checks into the pipeline, and name who is on the hook when one fails.

We write policies that hold up in a regulatory exam and map controls to the standards your sector answers to, NIST AI RMF, ISO 42001, HIPAA, or NAIC, so governance is defensible instead of a binder on a shelf.

We define, document, and certify every KPI across Finance, Sales, Operations, and Marketing so every department queries the same number from the same source. See how we ran this for Kaiser Permanente.

We map every critical data asset to a named business owner, define stewardship responsibilities, and establish an escalation path for data quality issues.

We assess your current governance posture against AI readiness requirements: data lineage, model input certification, bias documentation, and audit trail completeness. We map controls to NIST AI RMF, ISO 42001, and Gartner TRiSM.

Two certified dashboards show different monthly revenue because Finance uses booking date and Sales uses close date, and no one has ever written down which one is correct.

A board member asks about a metric in a dashboard and no one in the room can trace it back to a source system. The answer is always 'we will follow up.'

The data science team built a model but cannot get it to production because the governance team cannot certify the input data meets audit requirements.

The reference framework for data management functions. We use it to scope what governance actually covers in your org.

The Data Management Capability Assessment Model. We use it to score current governance maturity and target the next level.

Risk management framework for AI systems. We use it to map governance controls to AI model lifecycle stages.

The first ISO standard for AI management systems. We use it for audit-ready AI governance documentation.

Trust, risk, and security management for AI. We use it as the operating-model reference for live AI deployments.

What most clients actually run. Lightweight enough for a 50-person team, defensible enough for a Fortune 500 audit.

We start with your most painful metric disagreement. Usually it is revenue, headcount, or customer count. We define that one metric end to end, document it, certify it, and use that process as the template for everything else. See our 90-day governance engagement breakdown for the full sequence.

Three signals matter. First, ask whether senior consultants stay on the engagement past kickoff or get swapped for juniors at week three. Second, ask for a fixed deliverable list with named owners and dates, not a hours bucket. Third, ask which framework (DAMA-DMBOK, DCAM, NIST AI RMF, ISO 42001) they map their work to. A firm that cannot answer all three should not be on the shortlist.

What is the difference between data governance and information governance?

Data governance is about structured data: metrics, KPIs, tables, columns, pipelines. Information governance is broader and covers unstructured records too: emails, contracts, PDFs, retention schedules. The two overlap in regulated industries (healthcare, finance, government) where the same data has both an analytics owner and a records-retention owner. We focus on data governance and partner with records-management firms when the engagement crosses into formal records retention.

Data management is the operational work: pipelines, storage, modeling, quality testing, observability. Data governance is the decision layer that sits above it: who owns each asset, what each metric means, who can access what, what quality bars apply. You can run data management without governance and produce technically correct dashboards that nobody trusts. Governance is what closes that gap.

Data quality is about whether the data is correct. Data governance is about who decides what correct means, who is responsible when it is not, and how those decisions are documented and enforced. Both are necessary. Governance without quality is just paperwork. Quality without governance is just cleaning.

Yes. We have implemented all three and run engagements where the catalog tool was already chosen. We are tool-agnostic on selection: a well-maintained dbt project plus a shared metric dictionary in Confluence is a defensible lightweight starting point, and we recommend a dedicated catalog (Alation, Collibra, Atlan) only when the certified-asset volume exceeds what a document-based system can manage.

A focused Metric Certification Sprint for a single domain (Finance or Sales) starts at the equivalent of a 6 to 8 week engagement with a senior consultant. A full enterprise governance framework rollout across 4 to 6 domains typically lands in the 3 to 6 month range. We price by deliverable, not by hours bucket, so the scope and the cost are decided up front. Request the audit for a fixed quote.

A focused metric certification engagement for a single domain (Finance or Sales) typically takes 6 to 8 weeks. A full enterprise governance framework takes 3 to 6 months depending on the number of domains and the maturity of existing documentation.

Data governance consulting is the work of deciding who owns each piece of business data, what every metric means, who can access it, and what happens when a number is wrong, then building the framework and operating model that enforces those decisions. In practice it covers metric certification, ownership and stewardship, access and lineage, quality rules, and a data catalog. The goal is one trusted set of numbers, not a policy binder nobody reads.

A data governance consultant diagnoses why nobody trusts the numbers, then fixes the cause: undefined metrics, missing ownership, and no audit trail. We define and certify the priority KPIs, assign named owners and stewards, set the quality and access rules, stand up the catalog and lineage, and train your team to run the model. The senior consultant who scopes the work stays on it, we do not swap in juniors at week three.

It depends on how many domains and data products come under ownership, your regulatory exposure, and how much governance you already run. Governance is roughly 80% people and process and 20% technology, so the cost is set by your operating model, not a catalog license. A focused metric certification sprint for one domain is a 6 to 8 week engagement; a full enterprise framework across several domains runs 3 to 6 months. We price by deliverable, not a hours bucket, so the number is agreed up front.

No. A catalog like Alation, Collibra, or Atlan indexes your data and stores definitions, but a tool with no owner goes stale within months. Data governance is the ownership, the certified definitions, the quality rules, and the operating model that make the catalog worth having. We implement a catalog when the certified-asset volume justifies it, but the program is the people and process around it, not the license.

A focused metric certification engagement for a single domain like Finance or Sales typically takes 6 to 8 weeks and gives you a working template. A full enterprise governance framework across four to six domains takes 3 to 6 months, depending on how many domains are in scope and how much documentation already exists. You see certified metrics in the first sprint, not at the end.

Data governance is the set of practices that decides who owns each piece of business data, who can access it, what each metric means, what quality rules it must pass, and how changes are tracked. Good governance is the difference between five departments reporting 'revenue' and five departments reporting the same revenue.

A compliance binder. We don't ship policy documents that nobody reads.

A blocker. Governance is built so the work scales, not so it stops.

A separate practice from data quality. Both are necessary. We cover both.

We do not start by buying a catalog. We sequence the work so ownership and definitions land before any tool, and your team can run the operating model after we leave.

Score current governance maturity against DAMA-DMBOK and DCAM, and find the metric disagreement that hurts most.

Define and certify the priority metrics end to end, with a written definition and source for each one.

Map every critical asset to a named owner and steward, with a real escalation path when a number breaks.

Hand over the operating model, the workflows, and the training so governance holds instead of going stale.

Governance is roughly 80% people and process, 20% technology, so scope is set by your operating model, not a license. These are the factors that move the effort.

Governance scales with how many domains, metrics, and data products you bring under ownership.

Regulated sectors add controls, audit trails, and documentation. Financial institutions spend roughly 4 to 7% of IT budget here.

Starting from no ownership differs from formalizing controls you already partly run.

Cataloging, lineage, and policy-automation depth affects setup, but most of the work is people and process.

You want stewardship and policies people follow, not shelfware.

You only need metrics defined once: see Semantic Layer Engineering.

You want it run for you ongoing: see Managed Data Readiness.

Your data is scattered and needs consolidating first: see Data Foundation.

A catalog tool indexes data. A governance program is the ownership and operating model that makes the data trustworthy. Here is the difference between hiring a data governance consultant and buying a license.

Stewards, workflows, and a model your teams run after we leave.

One certified definition of revenue every department reads from.

Data governance is what makes your data ready for AI. It is the work of agreeing on what your numbers mean, who owns them, and what happens when they are wrong, the foundation every AI project needs and most skip. We build data governance frameworks practical enough for a 50-person analytics team and rigorous enough for a Fortune 500 audit.

Data governance decides who owns each piece of business data, who can access it, what each metric means, and what happens when a number is wrong. Thinklytics builds governance frameworks practical enough for a 50-person analytics team and rigorous enough for a Fortune 500 audit, so five departments finally report the same revenue.

Data governance consulting is more than buying a catalog and hoping people use it. It is the ownership, the definitions, and the rules that make data trustworthy, plus the operating model that keeps it that way after we leave. Here is the work, and what each part delivers.