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Data centric: revolutionizing the company through data

Data centric architecture

Many organizations call themselves data-driven or data-centric, and use these terms in a confusing way. But the two have very different meanings.

On the one hand, being data-driven means making decisions based on data analysis. It’s a state of mind in which strategic decisions are guided by the information gathered.

On the other hand, being datacentric is much more than that. Companies that are consider data to be assets in their own right. Data governance establishes an organizational framework and rules to support all data-related projects.

Data centric architectures

Being data centric also means setting up a data-centric architecture, designed as a robust whole capable of serving the entire company over time. It is secure, reliable and, above all, scalable in terms of volumes and uses.

In recent years, Big Data techniques have brought new styles of architectures – data lake, data hub, data mesh – allowing to answer almost any problem.

Initially developed by the web giants to cope with ever-increasing volumes, these techniques are now increasingly used in business computing, even though volumes remain relatively low. And for good reason, they bring new features. The most interesting one being thescalability of the storage space that can be expanded indefinitely thanks to the cloud. So, no more data silos: you can now have them in a single space and bring them together without limits and without compromising performance.

Benefits of the data centric approach

As a result, a data centric company has an undeniable advantage over its competitors because it can very quickly process all the data at its disposal.

For example, in case of war, pandemic or natural disaster… A manufacturer must be able to reorganize its supply chain quickly, even in real time. And it can do this thanks to the data centric approach that keeps all possible and imaginable data at its disposal with a controlled quality.

In contrast, a company that is not, takes days or even weeks to answer questions that can be vital to a quick response.

Becoming data centric is therefore a considerable challenge in the 21st century; but how to achieve it?

Become data centric

The four ingredients of a data centric organization are:

  • Alignment of the digital strategy with the company’s objectives
  • Scalable data architecture for innovative data science
  • Data governance
  • Reporting and data visualization

Alignment of the digital strategy with the company’s objectives

A transition to a data centric model requires an alignment of business objectives with the digital strategy.

Before this alignment can be achieved, the strategic needs of the business must be identified so that the technology can meet them. The digital strategy must satisfy the requirements defined by the different business lines of the company.

By making data accessible and usable by all its employees, a data centric company can reduce its time to market, by quickly changing its digital strategy to adapt to market changes.

Data architecture and data science

Once the strategies are aligned, a company needs to put them into practice and start leveraging its data.

As mentioned above, it needs to build a data architecture that puts data at the heart of its operations.

In recent years, the cloud has revolutionized architectures, offering major advantages. Unlike on-premises information systems, it eliminates the problem of limitedstorage space. It avoids the need for inflexible investments and offers great flexibility.

The cloud lets you entrust the management and evolution of your infrastructures to a specialized provider. The three world leaders, Amazon, Microsoft and Google, are seen as guarantors of long-term stability.

This simplifies the implementation of a scalable data-centric architecture. In fact, data architects have all the building blocks they need to concentrate on design.

This usually results in the ability to ingest, store, process and use data.

Finally, artificial intelligence and machine learning are increasingly being used to interpret data. This increases the performance of the data-centric approach.

However, without effective coordination with the trades, these technical resources would be largely ineffective. It is up to data governance to meet these requirements.

Data governance

Data governance defines the human, technical and organizational requirements for obtaining reliable data. These enable us to discover new opportunities, reduce costs and minimize safety and regulatory risks.

The three pillars of data governance
The three pillars of data governance

Reporting and data visualization

The end result, rich in opportunities, lies in the reporting and visualization of data. These essential tools allow the company to be steered towards optimal decisions in real time.

When companies first start looking at data, they often see it as a bunch of indistinguishable numbers. They need to understand the different ways of presenting data in a visual format, and most decision-makers are not usually data specialists or statisticians. This is why it is necessary to use data visualization tools.

Visualization software transforms data into easy-to-understand formats, such as charts or graphs. Business intelligence managers use these tools to create dashboards and reports. These visual elements, tailored to the needs of each individual, help everyone in the organization understand the data.


The natural evolution towards data centric strategies is a consequence of the increasing competition between companies. The very strong technical evolutions of these last years have largely accelerated it. Moreover, the cloud is almost a sine qua non for its success. Nevertheless, it must necessarily include alignment with the company’s strategy, supported by sustainable data governance.