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From the Consulting Datagouv to the Operational Datagouv

Data production is exploding, collection, storage and processing costs are collapsing: the Data Revolution is already behind us. Today, the challenge for companies is to implement data governance to collect and make the most of this wealth of information available to them.

What is data governance?

For some, governance takes the form of “effective coordination of powers, resources and information”[1]. For others, it constitutes a “high performance needle […] which must contribute to the creation of sustainable value”[2]. One thing is certain: data is now part of a company’s assets, as are financial assets or intellectual assets. In this context, data governance must respond to a challenge: mobilising an increasingly large amount of information, with agility, in order to tackle problems in an innovative way.


Rethink the value chain based on data

This will not have escaped anyone: today, the largest market capitalisations are mainly from the tech world. And all the technologies that are emerging on the market are partly based on data. This change, which has occurred in the space of 15 years, has forced companies to rethink their value chain.


The all-process vision has run its course

Rethinking the value chain means “breaking” the sequentiality resulting from the process approach. These “tranches” of activity no longer allow companies to be sufficiently agile. Why? Simply because they are renewed over long cycles, based on KPIs that only reveal market transformations after the fact. In other words, when a company reorganises its processes, it is already too late: the market has changed, and new opportunities have been captured by more agile competitors.


Move to an approach based on business events

To ensure their agility, companies therefore need to adopt a less sequential approach, more based on “business events”. With such an approach, the value chain no longer consists of separate processes from each other, but of a range of ecosystems that bring together all the stakeholders in the activity. Within this new value chain, we then distinguish an upstream part, which includes suppliers, logistics providers, advertisers, etc. and a downstream part, where the customer is located.


The example of an order on Amazon

Take the example of a purchase on Amazon: when the customer validates their basket, they activate an event common to all stakeholders. This single action triggers both the direct debit, the start-up of the supply chain, the payment of advertising partners, etc.

But because it requires a large number of data, this approach must be based on effective data governance. Indeed, even before the act of purchase, Amazon must mobilise a very large amount of data. On products, first of all: the platform must collect and communicate to its customers as much technical information as possible or usage information. On the context, then: thanks to the data it holds on its customers, the company can promote certain offers relating to the period of the year, the weather, or the socio-economic situation of the buyer.


From data strategy to data governance

To move from data strategy to operational data governance, three resources are essential.

  • The data governance framework

It defines a data culture by grouping together rules and policies that reflect the company's strategic vision.

  • The semantic dictionary and data catalogs

Essential for good data governance, these tools define the meaning and use of data, according to business needs and objectives.

  • The chief data officer

The role of the CDO is to translate the data strategy into policies, test the maturity of its organisation in the face of data-related issues, and promote data literacy within the company.



[1]     Gilles Paquet, economist and founder of the Ottawa Centre for Governance Studies. 

[2]     Yvan Allaire and Mihaela Firsirotu, Strategies and Performance Drivers, 2003.