The winning approach to turn your data into actionable insights?

Searching for real-time information on customers, business or activity is not new for companies. To meet this need, a number of approaches have emerged in recent years such as the Data Warehouse or the Data Lake, the logic of which is to centralize the data governed by the teams directly responsible for the data platform. 
For two years now, the concept of Data Mesh has been talked about as a revolution in the world of data since it would fill the gaps when it comes to data centralization on a platform. This approach is based on a distributed data model designed to break down the data silos that often exist within companies.


Data Mesh or Data Mess? 
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Centralization VS Decentralization

Both Data Warehouse and Data Lake are centralized architectures which have major disadvantages: they lack flexibility and the responsibility for the data they integrated is on the shoulder of the Data teams only. This way of working is more creating distances between the businesses from their data and leads to a slow response to business requests. 
However, it is possible to solve these problems without changing the physical architecture. It is no longer a question of migrating (from one architecture to another), but of moving towards a new approach. We must see the Data Mesh approach as an evolutionary step.

Benefits from Data Mesh approach

  • A strong autonomy left to the businesses to use the tools they need

  • Higher quality data by bringing data production and operation more closely in line with the business

  • Greater agility and time-to-market for businesses with fast-moving needs thanks to the decentralized vision of the Data Mesh

  • Rationalization of costs and mutualization of the platform

  • Reduced data exploitation costs for the company, especially through better management of the data operations

  • A federated governance

Quality data for business

The Data Mesh philosophy brings together all the dimensions of data management (Data Culture, Data Strategy, Data Governance, Data Architecture, Data Infrastructure, Data Services).  
Based on 4 founding pillars: domains, products, platform and governance, the Data Mesh concept provides a relevant model for any data-driven company, but is it enough?



data journey

Data Mesh in practice: Do’s & Don’ts


The most important thing to understand is that the Data Mesh approach, although it is groundbreaking, is not the key solution that will make you become data-centric overnight. In reality, this Data Mesh concept is a very good model to adopt a data-centric philosophy, but this approach does not explain the best practices to companies on HOW to implement and apply it technically, operationally & strategically.  
There is no point in rushing headfirst into Data Mesh by replacing your Data Lake or Data Warehouse if they are already performing their function. The common mistake is to start this approach with technology and forget about the objectives given by the business and the domains. 
                      Data background


Where to start your Data Mesh Implementation

The success of a Data Mesh implementation is based on a good understanding of the concept itself and a clear assessment of the challenges to be addressed. 

According to our Data experts, a few questions can help you before embarking on a large-scale Data Mesh project:

Get started with your Data Mesh Implementation


Our team of data experts in Data Governance & Cloud Architecture can accompany your business by identifying and implementing a concrete road map.                                          Discover the Data Mesh bootcamp
Benjamin Protais.jpg
Benjamin Protais
Data Expert
Pierrick Reglioni.jpg
Pierrick Reglioni
Data Expert