The importance of data governance for your business
Data governance has become a central pillar of business digital strategy. Far beyond a regulatory requirement, it structures the way in which organizations collect, secure, and value their information.
What is data governance?
La data governance is no longer a simple technical option, but a strategic pillar for any company looking to secure its assets and stimulate innovation. It defines a set of rules and responsibilities to ensure that data is managed safely, compliantly, and effectively. For a Risk manager, mastering data governance is essential to pilot a risk management strategy adapted to current challenges.
Far from being a constraint, implementing good data governance brings immense value: it turns your information into a reliable asset, protects your organization against regulatory sanctions and strengthens the trust of your customers.
By clearly defining who has access to what, how the data is used, and who is responsible for it, you then build a solid foundation for growth.
Why is data governance critical for business?
One data governance solid transforms raw data into a strategic asset, guarantees compliance (RGPD, NIS2, DORA) and strengthens global cybersecurity. It reduces the risk of information leaks while promoting trust and informed decision-making.
Define governance and its place in the company
Data governance isn't just about technology. Above all, it is a management discipline.. It consists in setting up a clear organization around data, defining the roles and responsibilities of each person. The aim is to ensure that all employees understand the value of data and respect the rules established for its processing.
This governance creates a common language within the company, facilitating collaboration between technical departments (IT, security) and businesses (marketing, finance, HR). It makes it possible to respond in a coordinated manner to crucial questions: what is our most sensitive data? Who can access it? How are they protected?
The fundamental principles of data governance
A successful data governance strategy is based on several key principles that guide every decision and every process.
- Responsibility : each data set has a designated “owner” (Data Owner) who is responsible for its quality, security and use. These roles and responsibilities should be clearly documented.
- Transparency : Data management policies and procedures should be clear, accessible, and understood by everyone involved. Everyone needs to know how data decisions are made.
- Quality Of data : governance aims to ensure that data is accurate, complete, and consistent. Control and cleaning processes are in place to maintain a high level of quality.
- security and conformity : data protection is at the heart of governance. This includes implementing security measures to prevent unauthorized access and ensuring compliance with legal and regulatory requirements. The Privacy by Design is a key concept to be integrated right from the design of systems.
By applying these principles, businesses are establishing a true data culture. The platforms of Cyber GRC suchlike Egerie facilitate this process by modeling information assets and by linking governance and risk analysis.
Implementing these principles seems complex, but it's the foundation of a healthy data culture. With a platform like Egerie, you can model your information assets, link responsibilities, but above all, visualize the associated risks to make informed decisions.
Request a demo now to find out how to simplify your governance.
Why is a data governance strategy critical?
In a world where the volume of data is exploding, the absence of governance exposes the company to major risks: financial sanctions, loss of reputation and security breaches. A well-defined data governance strategy turns these challenges into opportunities.
Stronger security and better data protection
The first mission of governance is to strengthen security. By classifying data according to their sensitivity, you can apply appropriate protection measures. A clear governance framework defines access, storage, and processing policies, reducing the attack surface.
This control is crucial to comply with safety standards such as PCI-DSS for payment data or to align its posture with recognized frameworks such as the NIST framework. Without governance, data security becomes a series of reactive measures rather than a proactive and coherent strategy.
Regulatory and legal compliance
Regulations such as the GDPR, DORA, or NIS2 impose strict requirements for the processing and protection of personal and operational data. Data governance is the most effective way to ensure compliance. It makes it possible to document treatments, manage consents and respond quickly to requests from authorities or persons concerned.
One Regulatory watch active, integrated into your governance strategy, allows you to anticipate changes and adapt your processes continuously. Not having governance means navigating a complex legal ocean by sight, with a high risk of non-compliance.
Improving decision making
Reliable strategic decisions are based on quality data. Data governance ensures that the information used for business reports, predictive analytics, or artificial intelligence is accurate, up-to-date, and relevant.
By establishing validation processes and defining reference data sources, you eliminate inconsistencies and doubts. Managers can thus manage the company with confidence, based on a solid information base.
Cost optimization and efficiency gains
Good data management also saves money. By identifying and eliminating redundant, outdated, or low-value data, you optimize storage costs.
Additionally, clear processes and quality data reduce the time employees spend looking for or verifying information. This increase in efficiency frees up resources for tasks with higher added value, such as analysis and innovation.
Implement an effective data governance strategy
The implementation of data governance is a business project that requires a structured approach and the involvement of all stakeholders. It's a cultural change as well as a technical deployment.
1. Define governance objectives and strategy
Before deploying tools, it's critical to define what you want from your data governance. The goals should be aligned with the company's overall strategy.
- Examples Ofobjectives : reduce the risk of data leaks by 30%, ensure GDPR compliance on all customer treatments, improve the quality of marketing data by 20%.
This initial step involves identifying priority areas and getting support from management. Without strong sponsorship, the project is likely to get bogged down.
2. Identify and assign key roles and responsibilities
Data governance is not only a matter of Risk manager. It requires the creation of a dedicated organization with clear roles:
- Data Owner (Data owner), business manager who has authority and responsibility for a specific data set (e.g. the CFO for accounting data).
- Data Steward (Data Steward), operational expert who manages the quality, definition and life cycle of data on a daily basis, under the supervision of the Data Owner.
- Data Custodian (Data Custodian) which is a role often held by IT, responsible for technical security, storage and data access management.
- Chief Data Officer (CDO) or Data Director who defines the entity driving the global strategy, arbitrating conflicts and ensuring the coherence of the entire program.
3. Implement policies, standards and procedures
This step consists in formalizing the framework of governance. It's about writing clear documents that define the rules of the game for the entire company.
- Policies data: high-level documents that describe data management principles (e.g. classification policy, retention policy).
- Data standards: technical and semantic rules to ensure consistency (e.g. date format, customer nomenclature).
- Operational procedures: step-by-step guides for specific tasks such as requesting access to a database, the process of correcting an error, or managing a data breach.
These documents must be alive and regularly updated to adapt to technological developments and regulatory.
4. Choosing the right tools and technologies
Technology is a catalyst for governance, not an end in itself. Tools should support your processes, not dictate them. Key solutions include:
- Data catalog : to inventory, document and map all of your data.
- Data quality tools : to profile, clean, and monitor the quality of information.
- Metadata management platforms : to centralize definitions and data lineages.
- Platforms of Cyber GRC : of solutions such as Egerie make it possible to integrate data governance into a global vision of risk management. They help link data to business processes, assess associated risks, and pilot treatment plans.
One risk analysis in-depth will help you prioritize technology investments based on the most critical assets. Visualizing the impact of data loss on your business goals is critical.
Schedule an Egerie demo to see how our platform can help you map these dependencies.
5. Measuring and monitoring the effectiveness of governance
Data governance is not a one-off project but a process of continuous improvement. It is crucial to define performance indicators (KPIs) to monitor the effectiveness of your program.
- Examples of KPIs : percentage of critical data with a designated owner, average time to resolve quality issues, number of security incidents related to poor access control.
Regular audits and performance reviews make it possible to identify weak points and adjust the strategy. This feedback loop ensures that your governance remains relevant and effective in the face of new challenges. Risk management, including those related to third parties, should be a central component of this control.
Data governance use cases with Egerie
Many leading companies have already integrated the data governance in their risk management strategy thanks to Egerie. Here are a few concrete examples.
Theory is important, but practical examples better illustrate the value of data governance. Here are three companies that have optimized their data management and security thanks to the Egerie platform:
Decathlon: governance and risk management at the level of a global leader
Decathlon has chosen Egerie to structure its data governance and centralize its cyber risk management on an international scale. The company thus benefits from a collaborative approach that makes it possible to map its assets, to implement the right data processing strategies and to guarantee compliance with current standards.
Result : Decathlon was able to homogenize its data security practices, empower its teams in terms of governance and strengthen the resilience of all its services.
Postal Banking: data centralization as a basis for governance and compliance
La Banque Postale has made the strategic choice to centralize its data using the Egerie platform, thus placing unified information management at the heart of its governance approach. This centralization has made it possible to structure a robust framework that meets the strict regulatory requirements of the banking sector, while facilitating the global control of access, the traceability of treatments and the management of responsibilities between the various businesses.
Thanks to a consolidated view of its data, the bank has strengthened its ability to drive compliance, improve process efficiency and accelerate innovation. While ensuring data security and service quality in a highly regulated environment.
Data Governance FAQ
This section answers frequently asked questions about data governance, its scope, and implementation.
What is the difference between data governance and data management (Data Management)?
Data governance and data management are linked but distinct.
- La data governance is the strategic framework. It defines rules, policies, and responsibilities. It answers the question, “How should we manage our data?” ”.
- La managerial Of data is operational execution. It includes technical activities such as storing, backup, integration (ETL) and the security Of bases of data. It answers the question: “How do we implement governance rules? ”
In short, governance defines the strategy, and management executes it.
Is data governance only for big businesses?
No, absolutely not.
Any organization that collects, stores, and uses data, regardless of size, benefits from governance.
For an SME, the framework may be lighter and less formal. However, the basic principles remain the same: knowing what data you have, where it is, who is responsible for it, and how it is protected. The lack of governance can be even more devastating for a small structure.
Indeed, it has fewer resources to recover from a major security incident or regulatory sanction.
Where do you start to implement data governance?
The best starting point is a pilot project that focuses on a critical or painful business area. Don't try to rule everything at once.
- Choose a high-value use case: for example, improving the quality of customer data for marketing or securing HR data.
- Identify the data in question and map its life cycle.
- Appoint a Data Owner and a Data Steward for this scope.
- Define a few simple quality and safety rules.
- Measure the scores and communicate on the successes to obtain membership for the future.
This pragmatic approach makes it possible to quickly demonstrate value and build a positive dynamic.
What are the main challenges in implementing governance?
The challenges are more often human and organizational than technical:
- Resistance to shift, especially when employees may see governance as a bureaucratic constraint.
- Lack of sponsorship: without strong support from management, the initiative will lack weight.
- Perceived complexity or the fear of tackling a project that is perceived as huge can paralyze action.
What tools should be used for good data governance?
Data governance tools include data catalogs, quality platforms, and data management solutions. Cyber GRC suchlike Egerie, which offer a consolidated view of assets, risks and responsibilities.
