Is Data Mesh the ultimate model for data-driven businesses?

The Data Mesh concept breaks with the data centralization models used in the past decades. Data Mesh is based on federated decentralization and redistribution of responsibilities, and all of this leads to a strong commitment from the company.

Data Mesh to leverage data and create value across the enterprise

We have seen the challenges faced by organizations in evolving data management solutions and frameworks to meet ambitious data aspirations.

Why is there such a gap between aspirations and achieved goals? There are a number of reasons, including a lack of maturity and skills, as well as poor execution of the data strategy.

We can also see that within companies, culture and organization make it difficult to scale data initiatives, decentralizing data to a monolithic platform (such as a data warehouse, data hub or data lake). In fact, the centralization of data optimization and governance is actually a real problem.

What if Data Mesh can bridge these gaps?
In fact, it's a real game changer, because it is based on a distributed model and a global response at the enterprise level. Data Mesh is not a technology solution. Data Mesh is a perfect model for the data-driven enterprise. As it brings together architecture, organization, methodology and governance, with the same objective: to involve the entire company in the challenges of optimizing data as a strategic asset.

From Data Mess to Data Mesh 

The emergence of the Data Mesh approach can be seen as a solution to the problem that companies are facing: silos.

It is because of these silos that we have seen so much effort, and failures, regarding data governance and the multiplication of analytical uses within the enterprise.

As we know, companies are organized in silos: departments, services, divisions are structured by functions, products, sales channels. This automatically creates data silos that make it difficult to use data for analytical purposes throughout the company.

The only answer to the problem of silos to date has been to centralize data by creating data warehouse, data lake or data hub solutions.

Data Mesh represents a real breakthrough. This model is based on the main idea of no longer fighting against the problem of silos, but rather taking advantage of them. The Data Mesh concept proposes a federated approach to maintain the best possible control over data assets and uses.


What is Data Mesh? 

A decentralized socio-technical approach designed to federate data management and access at scale, Data Mesh is a powerful transformation tool. For enterprises, it's an opportunity to take control of their data landscape.

The Data Mesh concept is based on the three key principles of decentralize, distribute and federate.
Its creator: Zhamak Dehghani. We owe the creation of Data Mesh to her in 2018, through a founding article. Since then, its contours have continued to take shape. In April 2022, it was the subject of a book entitled "Data Mesh: Delivering Data-Driven Value at Scale".

Optimizing data at scale through Analytics

The operational implementation of data mesh is still in development. So large enterprises are looking to deploy it iteratively.

Zhamak Dehghani describes Data Mesh as a decentralized, socio-technical approach to managing and accessing data for analytics and, more importantly, at scale. Decentralization emphasizes the break with the pursuit of extreme centralization applied until now.

Data Mesh is therefore not just a technical concept, nor is it the sum of technological bricks. Nor should it be limited to architecture and infrastructure issues. It covers all aspects of data management: organization, skills, methodologies, governance, architecture, etc.

Data Mesh aims to provide a framework for the enterprise and its users to manage and create new products (Data). The notion of scaling analytics is at the heart of the concept, and is an answer to the obstacles faced by organizations.

Data Mesh is based on the principle that value is intrinsic to each data product, its level of interoperability and the result of its correlation with other data products.

Domains, Products, Platform, Governance: the 4 pillars of Data Mesh 

As a global approach, the implementation of Data Mesh is based on four pillars: 

  • Data Domains This part of Data Mesh consists of a business breakdown of data and uses. The businesses, which are responsible for the domains, naturally become autonomous and responsible for the conduct of all their data initiatives
  • Data as a product 
    With Data Mesh, data becomes the product, in line with other product-oriented digital areas.

  • Self-service Data Infrastructure as a Platform 
    this pillar  concerns the technical and application infrastructure. Data Mesh requires the availability of an interoperable platform for all data domains.

  • Federated data governance
    In the Data Mesh model, data governance raises several issues, including the interoperability of domains. It is through this pillar that data protection and the data dictionary are defined and implemented through unified governance rules and standards


We believe that Data Mesh is the first truly global concept to serve the data-driven enterprise. It combines all the components that are useful for building and implementing effective Data strategies.

In top of that, the Data mesh offers possibilities and methodologies allowing companies to regain power and autonomy in the production, governance and exploitation of data.

This agility in the creation of Data products and their sharing is an opportunity for the company to reduce the time-to-market to adapt more swiftly to market changes, both in terms of use and competition. 

In summary, the Data Mesh has all the features needed to support companies that want to put data at the core of their decisions to become data driven.