Considering Data as a Product in its Own Right

Although data is a key resource of the digital economy, it is not always easy to make the most of. To take advantage of it, organizations must better understand it by adopting management models that guarantee its quality, relevance and proper use in full compliance.

All organizations have a lot of data, but they often don’t realize the value of it. In an increasingly interconnected world, the amount of information exchanged continues to grow tenfold. More and more often, organizations are asking themselves how to take advantage of this data and, before doing so, how to properly manage this mass of information.

A new approach

Since the 1990s, organizations have tended to centralize data in a data warehouse or data lakes. “Today, players are becoming aware of the limits of this approach, which aims to centralize data in a common pot,” explains Anne-Claire Maréchal, Data Scientist at Codit, a Proximus Luxembourg group brand dedicated to accelerating the digital transformation of companies. “The Data Mesh concept aims to build a completely different architecture for data management. The idea is to transfer ownership of the data to different teams, such as those who produce it. The latter will be responsible for guaranteeing the quality and relevance of the data, enriching it to ensure its proper understanding, but also for ensuring its use within the company.”

Guaranteeing the relevance of usage

The data is no longer lost in a cluster of centralized information, but becomes a product in its own right. Within an organization, one piece of data can serve several departments. In a bank, for example, the risk rating established for a customer as part of the granting of a loan could be used by the collection department. However, it is necessary to ensure that the information available is well understood, that the processing carried out is relevant and, above all, that the use made of it is compliant. “Decentralized data management is accompanied by the implementation of governance associated with each piece of information. The team that owns the data will define access and authorized uses,” continues Anne-Claire Maréchal. At the organizational level, a set of rules will set the terms and conditions to facilitate the sharing of data, thus accompanying its development.

Lifting the limits on data use

Since data is considered a product, it acquires a value. It is the responsibility of its owner, within the organization, to guarantee its quality and to maintain it over time. As with any other product, it is also up to the owner to market it internally or externally or to value the knowledge associated with the information. The approach opens up new possibilities. In cybersecurity, information sharing is essential to better understand threats and prevent attacks,” explains Cédric Mauny, Head of Cybersecurity Services at Telindus. However, raw data can be particularly sensitive, and can also be placed under confidentiality constraints that limit its sharing possibilities. The Data Mesh concept removes some of these limitations, through the valorization of raw data directly by the teams in charge of security. It is then no longer a question of sharing the data but the product that results after valorization. The processing of raw data can, for example, reveal patterns or establish a score associated with risk. These are the elements that can be shared with third parties, and then in turn create value for the community, while respecting confidentiality needs and requirements.

If we talk about security, the centralization of data presents an increased risk in case of a breach. Decentralized management, following the Data Mesh concept, helps to minimize the data stored in one place. Because of the knowledge and proximity to the data, the teams in charge are the most likely to know its potential and guarantee its security.

Maximize value

In the context of deploying AI and Machine Learning, the approach guarantees the relevance of the models that will be implemented. “From the outset, the quality of the data is crucial to ensuring the relevance of the results obtained from the use of these technologies,” comments Anne-Claire Maréchal. If the data is poor or irrelevant at the outset, we risk missing the desired objective. By entrusting the responsibility for data to specialized teams, we ensure a better understanding of its value, meaning and the relevance of how it is used. Today, this is a key issue when extracting all the value from the information available in the company.”

Thanks for reading!


Want to know more? Feel free to reach out to Eva Gram, Head of Codit Luxembourg at, or the author,

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