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Tapping into Big Data to achieve unparalleled operational efficiency



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Data lake

Our client, a major financial institution, set a goal to adopt Big Data by consolidating its data into a single lake. Their team developed a system that compiled information originating from different sources, like their CRM, their contacts, and their loan history.

Our mission: prove how effective Big Data can be

This project wasn’t only intended to update the client’s data management processes – it was also an opportunity to test Big Data’s potential within their organization. With that in mind, they had two goals: determine best practices for the Big Data ecosystem, including dev, configuration, DevOps and integrate the data lake using different software, including SAP’s platforms. By bringing all their information together in one place, our client wanted to paint a clearer picture of its clientele and optimize the services provided by their advisors. To accomplish this, we helped them:

  • Validate and improve their original architecture,
  • Create a Cloud environment supported by Azure,
  • Develop Big Data with Databricks and Spark,
  • Consolidate, clean up, and standardize their existing data,
  • Get the most out of their data using artificial intelligence,
  • Train and support their internal team.

The key to success : exceptional agility that instils confidence

By establishing a human partnership rooted in close collaboration and knowledge sharing, we were able to revolutionize our client’s data management and organization processes.

Over the course of this project, our client not only realized that Big Data worked perfectly within their environment, but also that it could be used as a lever to create value every single day

Luc Vaillant, Big Data Consultant

Benefits of this technological shift

With the tailor-made platform that we created; our client now has:

  • A global, consolidated view of the client’s data to help improve services offered,
  • The ability to expand the data lake in the future, if needed,
  • Data updates that are 5 times faster thanks to parallelized processes,
  • Optimization of various operations thanks to integrated AI powered by Big Data.

Technological environment

  • Azure
  • Data Factory
  • Databricks
  • SQL Server
  • Power BI

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