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Micropole Data Cloud and Digital Transformation consultancy

A European banking group evaluates the ability of Microsoft Azure environments to meet its needs.

As one of Europe's leading financial services groups, with over 150 years of expertise, the banking group supports 26 million customers on a daily basis through its 131,000 employees in 66 countries around the world.

Context

As one of Europe's leading financial services groups, with over 150 years of expertise, the banking group supports 26 million customers on a daily basis through its 131,000 employees in 66 countries around the world.

The group has three complementary business lines: retail banking in France, international retail banking, insurance and financial services, and corporate and investment banking.

Key figures :

  • 26 million individual, corporate and institutional clients
  • 131 thousand employees
  • 66 countries

Challenges

The banking group is currently facing a problem around its data volume. The entity of the group processing the financial data of the organization is limited in the processing done in part in MVS systems (COBOL code) involving increasingly important risks related to obsolescence and increasingly rare and expensive skills.

The group is also confronted with the limits of the possibilities offered by its very busy On-Premises Big Data environment, which is not able to handle the new needs of the business.

 

In order to no longer be restricted by the constraints of a physical environment and to mitigate risks, the entity wants to evaluate the ability of the Azure Cloud environment to meet current needs without increasing complexity and operational costs. The banking group wants to optimize its infrastructure while minimizing the effort of portability of current processing.

The diversity of data processing services in Azure offers many valuable insights but requires further analysis depending on the project context.

Methods and Solutions

Micropole worked alongside the banking group's entities processing the organization's financial data in order to define decision trees enabling the teams to better frame the implementation of their transformation projects.

Our experts have defined multiple use cases:

  • On the consumption of data representative of the various projects to be implemented in Azure
  • Implement project processing in Azure PaaS services
  • Measure the performance (processing times, financial costs and carrying costs) of these services
  • Ingestion and Exposure of data, refined and modeled in the Big Data On-Prem environment, for consumption in Power BI Service
  • Data ingestion, modeling and exposure, refined without the On-Premise Big Data environment
  • Ingesting, Refining, Modeling and Exposing data for consumption in Power BI Service
  • Use case development in Azure Databricks and Azure Synapse

Benefits

  • Performance analysis gives teams the decision-making tools they need to port their projects to Azure more quickly and efficiently

Other references

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