ExpertiseData

With the increasing digitalisation of processes and businesses, for several years companies have faced a major challenge: how to process ever-increasing amounts of data as quickly as possible and as inexpensively as possible.

Data Intelligence must respond to these data processing and analysis challenges while keeping an eye on the main objective of developing growth, investments and informed decision-making in companies in line with the regulations.

The benefits

With the increasing digitalisation of processes and businesses, for several years companies have faced a major challenge: how to process ever-increasing amounts of data as quickly as possible and as inexpensively as possible.

Data Intelligence (and more generally Data Science) must respond to these data processing and analysis challenges while keeping an eye on the main objective of developing growth, investments and informed decision-making in companies in line with the regulations.

To address this major challenge, we have developed expertise in Big Data technologies through:

  • data ingestion
  • storage
  • analysis
  • restitution
  • governance

Moreover, in an environment where data security must be a priority, this year we decided to train and certify 100% of our collaborators on GDPR before the end of the year.

Our beliefs

To meet the challenges of Big Data and effectively measure a company’s performance (producing those famous KPIs) and input decision-making (Dashboards), several steps are important:

  1. Architecture: grouping all data

It is essential to facilitate data processing by deploying a new Big Data architecture that groups all the company’s data. This new architecture must also do away with storage silos and check the veracity of the data.

  1. Pipeline: optimising the value chain

A company’s data value chain should result in the construction of data pipelines that ingest, store, analyse and deliver data seamlessly.

That’s why our experts work with our customers to identify high-quality useful data but also to make the right architecture choice so that data platforms generate value.

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