Data Centric Model
Building a reusable data heritage to sustainably industrialise data and AI uses
The data-centric model involves placing data at the core of the organisation, designing it as a common strategic asset rather than a by-product of applications, projects, or one-off uses. The aim is not merely to centralise information, but to build a coherent, reusable, and sustainable data asset capable of serving multiple needs without being rebuilt for each new use case.
This approach addresses a classic difficulty: despite increasing data volumes, this data often remains fragmented, siloed, and too dependent on source systems.
The data-centric model has become strategic because organisations can no longer simply stack data projects, pipelines or analytical uses without an overall coherence. As demands increase, an approach that is too product-oriented or too application-dependent creates new silos, even within a centralised platform.
In this context, many companies do have data, but it remains scattered, duplicated, or difficult to reuse from one use case to another. Each new need leads to specific developments, additional reprocessing, and strong dependency on source systems. The consequences are well known: high development costs, extended deadlines, growing technical debt, and difficulty in industrialising analytics or AI use cases. We, on the contrary, highlight that maximum data reusability drastically reduces development costs and time to market for use cases.
The data-centric model addresses this issue by building a data asset organised into logical layers, independent of applications and designed to serve multiple uses. It is therefore not simply a matter of installing a data platform, but of adopting a data asset management approach.
Purely technological approaches also show their limitations here. Centralising without rethinking the organisation, governance and the way data is produced is not enough. A data-centric strategy requires clarifying responsibilities, protecting the consistency of the common heritage, and preventing each team from recreating its own silos. This then allows new indicators, new analytical services and new AI uses to be built more quickly on an already structured foundation.
How does this expertise translate at JEMS?
At JEMS, the data-centric model is approached as a progressive transformation of the organisation, data and teams, to emerge a truly usable common asset at scale. With this approach, we are not just building a platform, but a sustainable framework to make data a reusable and scalable enterprise asset.
Diagnostic
We are analysing the existing setup, sources, flows, warehouses, uses, and team organisation in order to identify redundancies, weaknesses, and opportunities for sharing.
Heritage
We structure data according to an asset-based logic, in layers, independent of sources and uses, in order to maximise its reusability. This logic is at the very heart of the JEMS offering.
Organisation
We are setting up an organisation that protects the common foundation while allowing business uses to advance quickly. We are formalising this approach with a Core Team responsible for the constitution of the estate and the Feature Teams who work with the business on use cases.
Governance
We integrate the principles of quality, consistency, safety, and traceability to prevent the platform from recreating new silos over time.
Business Value
Adopting a data-centric model allows you to move away from a system of constant duplication and enter a system of capitalisation. Data is no longer rebuilt for every need, but organised as a common foundation serving multiple uses. It is this reusability that improves efficiency, consistency, and scalability. This approach helps to eliminate silos, build a data asset, and accelerate time to market.
This value is reflected as much in everyday projects as in broader analytics or AI transformation ambitions. The organisation gains clarity, speed, and robustness.
Data is less duplicated and more easily reused.
Development and maintenance costs are decreasing.
New analytics and AI uses are deploying faster.
Trades share a more coherent vision of the activity.
Data heritage is becoming more robust in the face of evolving sources.
Scaling up is done without rebuilding the base for each project.
VISION & PERSPECTIVE
In the medium term, the data-centric model will become a prerequisite for organisations wishing to sustainably industrialise their data and AI applications. As architectures become more complex and use cases multiply, companies will need to think less in terms of isolated projects and more in terms of reusable, governed, and scalable assets. Our approach aligns with this by placing maximum reusability and independence from sources and uses at the heart of the model.
This evolution will also strengthen the role of governance, automation, and AI. Advanced uses, including generative AI and agentic systems, will require a cleaner, more stable, and better-structured foundation to be industrialised. For us, the data-centric model is therefore not just an architectural target. It is a way of organising the company to sustainably reduce technical debt and accelerate innovation.
TO GO FURTHER...
Case study
To build a data-centric ecosystem for better management of the property network's activity
FAQ
What is a data-centric model?
This is a way of organising data as a common asset, independent of sources and uses, to encourage its reuse across the company.
What is the difference between data-driven and data-centric?
A data-driven approach often addresses specific, one-off needs or uses, whereas a data-centric approach aims to build a sustainable and reusable foundation.
Why is the data-centric model important for AI?
Because it provides a more reliable, better structured, and more stable database, which is essential for industrialising analytical and artificial intelligence uses.
When should you adopt a data-centric model?
When data is scattered, too dependent on source systems, difficult to reuse, or when each new project recreates part of the foundation.
Why isn't a centralised platform always enough?
Because a product-oriented approach can continue to recreate silos, even within a centralised platform, if the common heritage is not considered as such.
Why entrust this subject to JEMS?
Because JEMS articulates layered structuring, data asset logic and Core Team / Feature Teams organisation to build a truly usable data-centric model.
The data-centric model transforms data into a common, reusable, and sustainable strategic asset. By structuring a dataset that is independent of sources and uses, organisations gain coherence, speed of execution, and the ability to industrialise their analytics and AI use cases. At JEMS, this approach isn't limited to an architecture: it also relies on an organisation, governance, and methodology designed to last over time.
