In a world where data has become as vital an asset as money or human capital, companies need to collect, analyse and explore data to gain insights. This is where data platforms come in. These technological environments are built to centralise, manage and add value to data in a consistent and secure way.

How a Data Platform Manages the Entire Data Lifecycle?

A data platform is a technological solution that enables the management of the entire data lifecycle, from collection to exploration by business teams such as data analysts and data scientists. It contains multiple layers that perform the necessary tasks:

The first layer is dedicated to data ingestion from multiples sources like transactional bases, IoT objects, APIs, Excel files...

The next layer is usually the storage layer, whose purpose is to organise and preserve raw or transformed data. Examples include a data warehouse, which groups structured information together to make analysis easier, and a data lake, which is used for advanced analytics.

Then, the transformation layer is where the transformation and governance tools come into play. These can be ELT/ETL tools, a data catalogue, or even data quality and compliance rules.

Finally, the access layer contains all the analytics services, such as Power BI, as well as AI and machine learning tools. This layer also includes data visualisation tools.

In general, data platforms are built using cloud architecture to take advantage of its benefits, such as the ability to replicate data and prevent loss, or to increase storage capacity. However, many organisations still opt for on-premises solutions based on their specific requirements and constraints.

Why Data Platforms Are Essential for Organisations?

A data platform is a valuable asset for your company's data strategy, as it prevents data silos and ensures a unified view of information. Even more, it has the advantage to make the data reliable and to ensure compliancy with the regulations (GPDR, AI act...)

It also allows business teams like data scientists and data analysts to quickly explore the data and to create new data use cases.

Thanks to its security layers and tools, a data platform enhances the protection of sensitive data, such as personal information, against cyber threats.

Ultimately, from a financial perspective, a data platform optimises storage and processing costs.

Exploring the Main Types of Data Platforms and Their Roles

There are several main types of data platform, each serving different purposes. A data warehouse is designed primarily for analysing structured data and allows organisations to run complex queries efficiently. Popular examples include Snowflake and Google BigQuery.

 A data mart is a specialised subset of a data warehouse, tailored to a specific business line or department. This allows teams to quickly access relevant data without querying the entire warehouse.

In contrast, a data lake offers large-scale storage for raw structured and unstructured data. Solutions such as Amazon S3 and Azure Data Lake provide the flexibility to store diverse data types in their native formats.

The lakehouse model combines the strengths of both approaches by merging the flexibility of a data lake with the performance of a data warehouse. Databricks is a well-known example of this hybrid model.

Additionally, a data hub focuses on integration and sharing across systems. Acting as a central point where different data sources can connect, it enables seamless access and collaboration across the organisation.

Finally, specialised AI/ML platforms are designed to support machine learning workflows and MLOps. Tools such as Vertex AI and SageMaker allow data scientists to develop, deploy and manage models on a large scale.

Data Platforms: The Cornerstone of a Modern Data Strategy

In recent years, building a data platform has become essential for any business looking to develop a data strategy. Beyond technical infrastructure, it has also become a strategic foundation that enables companies to transform information into a tangible asset.

The successful adoption of a data platform hinges on three key factors: robust governance, a shared data culture, and an architecture that evolves based on the organisation's needs.