Client Background:

• Client: Multinational investment bank and financial services company
• Industry: Banking and Financial Services Industry
• Products and Services: Investment products and banking capabilities
• Area of Operations: More than 50 countries
• Number of Employees: More than 70,000

Challenge:

A multinational investment bank and financial-services company had experienced significant growth, and business projections anticipated the positive trend to continue. The subsequent growth in data would stress the company’s existing storage and computing capabilities, and operational risks would arise if the data became too siloed and caused increased on-premise expenses. To maximize its efficiency, visibility, and collaboration, the company hoped to migrate to a platform that would improve its ability to process large data sets while scaling to meet growth projections. It also hoped to save costs while enhancing overall security.

Solution:

The client wanted to offload a significant amount of their big-data workloads and consolidate their data lake on Azure to reduce operational expenses and enhance security for their entire ecosystem through a scalable platform that responds to clients more quickly, even during peak operational hours. With an aim to facilitate seamless platform modernization and faster delivery, Wipro developed a platform based on solution accelerator IntelliProc to automate big data workload conversion to a Spark equivalent and deploy it into Azure Databricks. The Databricks platform was set up to ingest data from various sources and perform analytics, eliminating the problem of siloed data. IntelliProc’s high-level automation of business logic conversion and data mapping validation features was designed to improve data integrity and accuracy for the client, which previously was a challenge for their teams.

Business Impact:

IntelliProc modernized existing big-data workloads at scale to leverage the benefits of Azure Databricks. The development, data quality, and testing phase guaranteed 35% effort savings, and implementing the Azure Databricks environment significantly reduced the on-premise expenses and helped in elimination of the traditional siloed data ecosystem which led to better collaboration among the data teams in a more secured environment. The use of Databricks improved client data team productivity by 20%, and the scalable environment provided better computing capabilities with increased operating efficiency and lowered infrastructure costs by 25%.

IntelliProc also converted big data workloads–Hive and Impala scripts into their Spark equivalent—which led to a more than 60% overall effort reduction. The converted scripts had no changes to the business logic, saving cost and time. The requirement gathering phase manifested up to 40% effort savings.