Welcome to the data era! Data has become one of the company’s most important assets that requires the implementation of a framework adapted to its exploitation. Far from being limited to its technical aspects, its governance covers real strategic challenges. Explanations.
Like all buzzwords, the term Data Governance leads to various and often erroneous interpretations. The most common temptation is to reduce the topic to the simple technical management of data, as if governance was limited to the creation of catalogues or to databases administration.
In fact, it requires to take a step back and look at data management as a whole. Data Governance therefore includes all practices related to the data processing within the company to create business value. It therefore represents one of the key elements of the company’s strategy.
Why Data Governance?
Would you board a plane whose pilot and equipment have not been thoroughly checked before? From the airline company’s point of view, these procedures are of course a safety priority, but they also contribute to optimizing and managing the various operating parameters of the aircraft to ensure a return on investment.
Data Governance follows a similar logic. Various procedures enable to check the data quality, control the type of processing carried out and secure the whole system. It therefore offers the company the ability to comply with current laws and regulations, as well as the resources to align the data valuation process with the business strategy.
How to implement Data Governance?
The first step is to define the scope of this governance by identifying the different types of data used in the company. This data must be classified and analyzed according to its characteristics in order to assess the associated risks and opportunities. We can then begin to work on the different governance systems in place to determine the tools and processes that best suit the business context.
The integration requires the definition and implementation of certain processes and guidelines, whose effectiveness will be monitored within the governance system using specific performance indicators. For example: the evolution of the number of claims over time is a good way to verify the governance of a personal data set.
Finally, the approach is combined with a review of the responsibilities and roles within the organization: with or without a Chief Data Officer (CDO), it is necessary to monitor the evolution of the data and ensure the continuous improvement of the governance system. As a matter of fact, this is not a one-time project, but a long-term approach that must be adapted both to the business changes and to the regulatory framework transformation.
What about security?
This is the common principle of Data Governance: data is valuable to the company, as it is to other less than kind players. The exploitation and enhancement of data begin with the implementation of appropriate security measures adapted to its level of criticity. Security is therefore taken into account as soon as the data-driven strategy is defined.
What happens next?
As soon as you manage data, you need to implement an effective governance policy in order to control the associated risks. It is the responsibility of the top management to be aware of these issues and to boost the project, while making sure to measure the effectiveness of the tools and processes implemented. An adapted support at this level appears to be a main asset.
However, as it is often the case when it comes to structuring processes, event if the first step is the most difficult to make, especially because it involves deploying new resources, the benefits of effective governance come very quickly. They serve the entire company, both in its core business and in its support functions, and help to make its entire business ecosystem more dynamic.