Data governance consulting: one version of the right number
Every department defends its own version of the right number, and boards end up arguing about which figure is true. We define where each data point comes from, who owns it, who may use it and under which quality rules, so decisions — and AI models — rest on reliable data.
What is data governance and what is it for?
Data governance defines where each data point comes from, who owns it and who may use it. It is the foundation AI governance rests on: what information a model may touch, what is out of scope and who answers for its quality. Without that foundation, every department reports its own figure and no model is auditable.
Owners and rules
One accountable owner per data domain (customers, products, finance, people) and the quality, access and retention rules that apply.
Catalog and lineage
Which data exists, in which system it lives, how it is transformed and which one the official figure comes from.
Public reference
The DAMA-DMBOK, published by DAMA International as the recognized framework for data management, serves as a guide to structure the functions; the scope adapts to the company's size.
Governance that orders the data before the model.
01Data assessment
Which systems generate data, where it is duplicated (CRM, ERP, spreadsheets) and which figures don't reconcile between departments.
02Governance model
Data domains, owners, committee and the decisions each one makes. What leadership decides and what it delegates.
03Quality and access policies
Capture, deduplication, validation and access rules, written and enforceable in the systems that already exist.
04Catalog and lineage
Inventory of critical data and where every indicator the board sees comes from.
05Controls for AI
Which data a model may use, which it may not, and with what traceability, integrated into the AI governance framework.
06Adoption plan
Training and measurement so the rules keep being followed after Cyrrus steps back.
Data governance solves a problem recognized in the first board meeting: two departments present different figures for the same indicator and the discussion turns into which number is true, not what to do about it.
Defining data governance means deciding, for each domain (customers, products, finance, people), who owns the data, which system the official version comes from, which quality rules apply when it is captured and who may use it. These are leadership decisions, not system settings.
We don't start from scratch or propose a year-long project: we audit the data the company already has — which systems generate it, where it is duplicated, which figures don't reconcile — and define the rules on the systems already in production. The CRM and the ERP are a frequent starting point in our projects, because that is where customers, sales and finance live.
Data governance is the foundation of AI governance: a model is only auditable if it is known which data it used, where it came from and who answered for its quality. That is why data controls for AI — what a model may touch and what it may not — are defined here and integrated into the AI governance framework, not as a separate project.
The DAMA-DMBOK, published by DAMA International, is the recognized reference framework for data management and serves as a guide to structure functions and responsibilities. In our view, a midsize company doesn't need to implement all of it: it needs clear owners, a catalog of critical data and rules that are followed in the systems it already uses. To prepare leadership we offer the data governance for executives workshop at Leadership Academy.
Read also: Data governance and deduplication in the CRM
Data orders what comes next
Before defining data governance, read this
Frequently asked questions about data governance consulting
When two departments present different figures for the same indicator; when the CRM or the ERP has duplicate customers and products; before an analytics or AI project, so the model doesn't inherit data with no owner; or when a customer or a regulator asks who is accountable for a data point.
Data governance defines where each data point comes from, who owns it and who may use it. AI governance rests on it and adds who approves each AI use case, which data a model may touch and who audits its decisions. At Cyrrus we work on both together.
No. Data quality (completeness, duplicates, validity) is one of the outcomes of governance. Governance defines who answers for that quality, under which rules and in which system; without owners, data cleanup repeats every year.
Not at first. Capture, deduplication and access rules are applied first in the systems that already exist (CRM, ERP). A catalog or quality tool is evaluated later, with the same vendor-independent criteria we use in every selection.
A data assessment, the governance model (domains, owners, committee), the quality and access policies, the catalog of critical data with its lineage and the data controls for AI, plus the adoption plan.
It decides who owns each domain, which indicator is the official one and which data stays out of AI's reach. The data governance for executives workshop at Leadership Academy prepares those decisions.
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