Choosing your Data Platform

In practical terms, what is a Data Platform?

The role of a Data Platform, or EDP (Enterprise Data Platform), is to centralise the collection of a variety of data, optimise its storage and manage its processing so that it can be used throughout the organisation. In this way, a single data platform can be used throughout the organisation, avoiding the creation of information silos!

Its architecture is designed to enable interoperability with other systems, adapt to increasing volumes of data (and users), and offer intuitive interfaces. In doing so, it supports business decision-making processes by providing relevant information in near-real time.

Because they manage “traditional” files and databases, as well as more complex solutions involving Big Data, Data Platforms are the vectors that feed information systems (IS). They are therefore vital. With the development of the Cloud, Data-Driven strategies and the IoT (Internet of Things), use cases are multiplying, and platforms need to keep pace by adopting a Modern Data Platform approach.

How does a Data Platform work?

A Data Platform refers to a technological infrastructure integrated into the information system, the main objective of which is to consolidate and transform raw data into usable information, to guide organisations’ decision-making, and also to facilitate the use of data by business tools and users. To do this, it integrates data from multiple sources, cleansing and structuring it to ensure its quality and consistency, and putting it to work for business processes and internal customers. Here are the main principles that govern it:

• Data collection: It’s the tool’s ability to bring together information from a variety of sources, such as internal databases, SaaS systems, real-time feeds, log files, IoT sensors and so on.
• Storage: This involves implementing solutions such as data lakes, repositories or data warehouses to store data securely and on a large scale. More specifically, data lakes are used to store unstructured or semi-structured data, while data warehouses are optimised for structured data and complex queries.
• Treatment and processing: These are data processing functions for cleansing, normalising, enriching and transforming data. These are known as ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) technologies.
• Analysis: It includes reporting, business intelligence and, potentially, AI-based approaches (data mining, machine learning, etc.) to discover trends and patterns and gain insights.
• Governance and safety: In other words, the mechanisms that ensure quality, regulatory compliance, flow supervision and data security, such as access management, encryption and audits.
• Interoperability: This is the ability of a platform to integrate and operate with other systems and applications, an essential element in ensuring the smooth flow of data throughout the organisation.
• Scalability: This is the ability of a platform to adapt to variable (often growing) data volumes and to support changing workloads, often via Cloud solutions.
• The user interface: Intuitive interfaces and unified data visualisation tools are essential if both technical and non-technical users are to work quickly and easily.
• Intelligence: Behind the notion of intelligence, the realities can be very diverse! The foundation is automation, whether in the integration, transformation and distribution of data or the processes that bring it to life. It also includes Business Intelligence approaches, including the use of Machine Learning and other AI approaches, for example, to understand and classify information, and to map and catalogue data automatically.

Focus on: Data Integration Platform vs Data Platform

Although the terms data platforms and data integration platforms are sometimes used interchangeably, they cover different scopes – that of data platforms encompassing integration! While a data platform represents a centralised architecture that manages, circulates and analyses dispersed data, a data integration platform focuses primarily on the circulation of this disparate data between the various applications of the Information System. It then prepares it for analysis, acting as an intermediary that facilitates the routing and transformation of this data between all the systems, but without storing or analysing it directly.