How to set up data governance?

Updated March 2026 - Setting up data governance is not about creating yet another committee. This involves organizing the roles, rules, quality, security and uses of data in the company. In 2026, the subject has become central for several reasons: multiplication of tools, compliance requirements, cyber risks, ESG reporting, increasing use of AI, and the need to make management indicators more reliable. A company that produces a lot of data without clear governance often ends up lacking confidence in its own numbers.

To complete, also see ESG reporting, Compliance audit and Organizational audit firm.

Data governance should not be approached as a purely IT project. It is first and foremost a subject of organization and responsibility. Who can create, modify, use, share or delete data? What definitions does the company use for its main indicators? How does it manage access rights, corrections, histories and controls? As long as these questions remain implicit, the quality of analyzes and the security of use remain fragile.

Robust data governance often relies on:

• ▸identified data owners;
• ▸quality rules;
• ▸a clear logic of access and rights;
• ▸minimal documentation of definitions and flows;
• ▸update circuits;
• ▸monitoring of uses and incidents;
• ▸coordination with compliance and security issues.

Hayot Expertise Advice: effective data governance rarely starts with a large tool. It starts with clear responsibilities.

In other words, it is better to have simple, explicit governance that is sustained over time than a very ambitious plan that is never adopted by the teams.

For a long time, data governance was seen as a big business subject. This is no longer true. SMEs today accumulate data in their ERP, their CRM, their payroll tool, their Excel files, their HR tools, their marketing automation, their shared spaces and sometimes their AI environments. Since figures are used for management, customer relations, payroll, compliance or cybersecurity, the question of governance arises.

The first symptom of bad governance is often a loss of confidence: two tables give different figures, definitions change depending on the team, histories cannot be found, rights are too broad, or certain sensitive data circulates without sufficient control.

An industrial SME that multiplies the tools

Between the ERP, the maintenance software, the purchasing files and the commercial monitoring, the data circulates without any real common rules. The stock figure, nomenclatures and certain performance indicators are not always aligned. Data governance then begins with clarifying definitions and who is responsible.

A health startup that handles sensitive data

The compliance and security issues are very high. Governance cannot be reduced to orderly storage. It must integrate access rights, traceability, review of uses, documentation and coordination with regulatory requirements.

A multi-site network that wants to make its reporting more reliable

Each site provides its indicators in its own way. The seat is consolidating, but the comparisons are fragile. Clearer governance makes it possible to define the same entry rules, the same periods, the same definitions and better exception control.

1. Identify critical data

Not all data has the same value. We must first target those that really matter: customers, invoicing, HR, cash flow, stocks, ESG indicators, sensitive data, etc.

2. Appoint managers

Each important block of data must have a business owner and, if necessary, a technical relay.

3. Define a few simple quality rules

What is a complete file? who can correct? how to trace a modification? when to archive? These basic questions change a lot.

4. Map flows and access

We need to know where the data is born, who transforms it, in what tool it circulates, who accesses it and for what purposes.

5. Document essential definitions

Management indicators must be based on stable and shared definitions. Otherwise, the piloting becomes questionable.

6. Integrate security and compliance

Serious governance is not limited to quality. It also takes into account applicable rights, confidentiality, retention and obligations.

7. Establish a periodic review

Governance is not a project to be delivered once. It is a discipline to maintain, particularly during tool, process or organizational changes.

For a personalized analysis of your data organization, make an appointment with our experts. We can also help you connect governance, reporting and tooling via our support in digital transformation.

The most common pitfalls are:

• ▸believe that a new tool will solve the problem alone;
• ▸launch a committee without clarifying responsibilities;
• ▸document too heavily and lose teams;
• ▸forget sensitive data and access rights;
• ▸treat data as a purely technical subject.

External support often helps to maintain a good level of pragmatism: enough structure to increase reliability, but not to the point of unnecessarily slowing down the teams.

Where to start when starting from scratch?

The simplest thing is to start with the critical data and the major irritants: contradictory figures, poorly controlled access, vague definitions, poorly monitored sensitive data. Good governance often starts small and then scales up.

Do we need a specialized tool to set up data governance?

Not necessarily at the beginning. Many companies are already making strong progress with clear responsibilities, simple mapping, a few common rules and better configuration of existing tools.

Who should raise the issue in the company?

It takes a combination of profession, management and technique. If the subject remains only in IT, it will be incomplete. If he remains solely in the profession, he will sometimes lack technical robustness.

Does data governance only concern personal data?

No. Personal data is an important aspect, but governance concerns more broadly the quality, security, use and reliability of data in the service of the company.

What is the link between data governance and financial or ESG reporting?

The link is direct. If definitions, flows and those responsible are not clear, financial or ESG indicators lose reliability. Good governance therefore directly supports the quality of management.

In 2026, useful data governance is based primarily on clarity of roles, quality, traceability and consistency of uses. It is a condition of trust, not just a process subject.

?? Do you want to establish practical data governance in your organization? We can help you build a simple, robust foundation adapted to your maturity level. Make an appointment with an expert

The biggest risk is not getting off to a bad start. It's about framing well at the beginning and then letting the uses become vague again. Useful data governance is maintained by a few simple routines:

• ▸periodic review of sensitive access;
• ▸quality verification on a few critical datasets;
• ▸updating definitions when tools or processes change;
• ▸monitoring of incidents or data anomalies;
• ▸clear arbitration when data becomes strategic for management.

This approach makes it possible to avoid two extremes: forgotten governance and bureaucratic governance. The right level is one that supports confidence in the data without slowing down teams unnecessarily. When a company achieves this balance, it gains in both security, compliance and decision-making quality.

In many companies, data governance really progresses when management understands that definitions are not a technical detail. A turnover, an active customer, a margin, a workforce or an ESG indicator cannot change meaning depending on the service.

Setting stable definitions, documenting them and bringing them to life is therefore a real management subject. This is also what then allows the use of AI, automation or reporting on a more reliable basis. Without this in-depth work, the tools often give an illusion of maturity without real confidence in the results produced.