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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 three 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.
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.
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?
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.
Our team of data experts in Data Governance & Cloud Architecture can accompany your business by identifying and implementing a concrete road map.