A man in a suit and tie holds a tablet horizontally above which floats in holograms a futuristic graphical interface representing the regulations and policies of digital data

The transition to a data centric approach is a major challenge faced by companies today. The use of data no longer concerns only certain technical teams but all of a company’s businesses. On a daily basis, each employee handles data of varying complexity and sensitivity. In addition, the emergence of big data has led to a dramatic increase in the volume and complexity of data. To succeed in this transition, we must first and foremost consider data as an asset that we must know how to take advantage of. Valuing your data assets requires, above all, knowing them, securing them, and guaranteeing their reliability and quality. Organizing this work is data governance.

General definition

The governance of (those working with) the data includes the organizational structure the procedures and tools that enable organizations to manage their data as an asset to increase revenue and productivity, while reducing their security risks and regulatory compliance.

Data as an asset of the company

Assets are everything that a company owns and brings value to it. These assets can be real estate, machinery, vehicles, technological equipment, patents, but also financial assets such as stocks or bonds that provide a longer term return.

Similarly, data is an asset because it generates value immediately or potentially later if the opportunity arises. For example, collecting as much data as possible on its customers – in compliance with the GDPR or CCPA – allows it to better respond to its present or future needs. This data is therefore an asset that will generate value for the company, in the same way as the other assets usually recorded.

Benefits of data governance

Globally, data governance allows to increase the performance of a company following these levers:

Increased sales and productivity

Data governance helps increase revenue by improving data management, which leads to better overall business productivity. Here are the axes that seem to us the most important:

Better knowledge of data and its life cycle

The data catalog is the bible of the chief data officer and all people who work with data. It is the one that will allow you to discover – or not to miss – new business opportunities.

Its essential features are:

You will learn more in our dedicated article.

Better roles and responsibilities

Data governance also allows for greater accountability by assigning permissions and roles, making it much easier to determine who is responsible for what data.

We generally distinguish between data owners, who make decisions about the data in their business domain, and data stewards, who are responsible for the day-to-day documentation, quality and, more generally, compliance with the rules of corporate data governance.

Better data quality

Data is of good quality when it represents reality accurately. And only in this case, its use is reliable.

Data governance facilitates data quality management within the organization. It is a discipline that establishes quality standards and rules to control it: measurements, prevention, data cleansing…

Having good quality data increases operational efficiency.

Improved operational efficiency

If you have well-managed data and the ability to analyze it, you can improve operational efficiency in many areas.

For example:

Better decision making

Through its ability to orchestrate data management at a global level, data governance facilitates and improves the use of data, leading to better decisions with greater confidence.

A l’inverse, une analyse qui se base sur des données erronées, incohérentes ou incomplètes implique une prise de décision biaisée qui contribue à des erreurs stratégiques et des pertes financières parfois importantes.

Cost reduction

Improving the use of data thus contributes to reducing costs:

Risk reduction

Data governance also reduces at least three types of risk:

Disadvantages and difficulties to consider

Implementing a data governance strategy or program also means challenges and drawbacks to consider. Thus, the following points should be considered:

However, all of these issues can be overcome and should in no way deter you from deploying data governance in your organization.

Difference between data governance and data management

Data governance is to be differentiated from data management.

Data management is the set of activities that enable the management of data within an enterprise. It includes the following activities:

Data governance is the link between the company’s strategy and the management of its data, which it supports without constraining.

Example of analogy between financial assets and data as an asset
Example of analogy between financial assets and data as an asset


Here is a first approach to data governance, which presents the general concepts without going into details.

To date, many frameworks have been proposed by major strategy consulting firms. They are usually complex and expensive to implement.

What you need to remember is that there is no simple framework that can be adapted to all types of companies. Each organization must therefore implement its own data governance.

At Data Éclosion, we advocate a simple, agile and modular approach that meets the client’s priority needs.