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Cloud journey

It is essential for financial professionals to collect and use as much data as possible to secure investments, optimize performance, and maximize revenues. Our client wanted to modernize their analytical infrastructure so that they could process more data. The different business intelligence infrastructures that were in place at the time required a great deal of effort from the business lines to integrate data in different formats, from different sources, which were scattered and therefore difficult to access.

Our mission : shifting a BI platform in a data lake

Our mission was to accompany our client in an ambitious Big Data project and help them migrate their business intelligence to a Cloud-hosted data lake. Among other things, we have:

  • Established a proof of concept with Azure Suite,
  • Accompanied the client in their strategy of storing structured and unstructured data in a data lake,
  • Enabled the classification, prioritization, consumption, and visualization of data.

The key to success : a solid change management approach

Adding Big Data technologies to an existing analytics ecosystem can cause some turbulence. In fact, some technologies are different and require new skills from the teams. Therefore, it was necessary to rely on effective change management. By promoting agility, transparency, and transferring skills through coaching and training, we were able to carry out this project successfully.

Evaluating an asset, analyzing stock market trends… Financial professionals need to cross-check as much information and varied data as possible to help them make strategic decisions. A data lake is the technology that enables them to centralize all of this data into a single structure, making it easily usable by any line of business. It saves valuable time and resources in a highly competitive industry.

Matieu Bachant-Lagacé, Big Data Expert

Benefits of this technological shift

The migration of a BI platform in a data lake brings a wide range of opportunities and more specifically:

  • Centralization of a considerable amount of data, volumes, formats and sources,
  • Better classification, consumption and data visualization, enabling simplified decision-making,
  • An easier integration of new data sources, making the platform as extensible as desired,
  • Much faster data consumption.

Technological environment

  • AWS
  • Snowflake
  • Denodo

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