Building a data platform is essential if you want to develop a complete data governance framework.

In the current era of generative AI, organisations are well aware of the need for a comprehensive data strategy to manage data issues and respond appropriately to business demands.

There are three main approaches to conceptualising a data platform today. Firstly, bear in mind that the final choice must meet business needs. It would not be appropriate to launch a project that does not fully comply with the initial targets, would it?

In this article, I will present each concept and try to guide you towards the most suitable data platform.

A Single Source of Truth for Enterprise Analytics with a Modern Data Warehouse

A data warehouse is a good option to consider for your data platform. It involves gathering large amounts of structured data from multiple sources and systems. This makes it ideal for your business teams to use for analysis.

Indeed, a data warehouse contains collections that have been cleaned and transformed prior to final loading, thereby optimising the speed and reliability of analysis.

Teams will be able to perform complex queries. This also ensures that everyone works with consistent, standardised data, thereby reducing the risk of errors.

In terms of data governance, a data warehouse facilitates the implementation of data policies and compliance regulations, enhancing data trust and accountability.

Unifying Structured and Unstructured Data with a Data Lake

On the other hand, a data lake is another data architecture to consider. Unlike a data warehouse, a data lake can gather both structured and unstructured data from multiple systems.

It offers flexibility, as it can ingest raw data in its initial state for experimental analysis. This method has the advantage of allowing the data schema and associated structure to be defined later.

In addition to flexibility, the data lake offers speed of processing, with the transformation process performed after the raw data has been loaded. Furthermore, a data lake is ideal for big data scenarios involving IoT and social media.

The management costs of a data lake are lower than those of a modern data warehouse, and it offers more opportunities for advanced analytics and machine learning algorithms.

Driving Trusted, Real-Time Insights Through a Data Hub

Beyond Data Lake and Data Warehouse, you might consider developing a data hub platform. However, it demands more efforts across all the organisation and communication between employees.

The concept of the data hub is based on centralised, controlled data governance, which is necessary to unify all data sources into a single point of exchange to answer team data requests.

One of the main features of using a data hub is the possibility to monitor and analyse in real-time the data flows between operational systems. It enables the gathering of data for different business and technical needs and reinforce data availability.

Another interesting feature is the ability to speed up the integration of new data flows thanks to the data hub's capacity to reuse existing data pipelines and reduce maintenance efforts.

In conclusion, there is no perfect solution, it depends on your needs and your existing infrastructure. But it might be a good option to develop a data hub platform to encourage a data culture within the internal processes.

In addition, a data hub can adapt to changes in data policies and future challenges. We live in a world that is evolving quickly in terms of technological innovation. Data moves fast, and each company must be ready to adapt.