Banking and Finance

With business getting an overwhelming response, the customer base and associated data sources started to grow exponentially. The company had been collecting large volumes of data every day and the time taken to segment one customer was three days. This made the campaign department handicapped in pushing any new campaigns on a focused note, and the only option was to do a mass campaign on random customers, expecting the conversion ratio to go up.

SOLUTION

Whiteklay leveraged the Hadoop architecture to overhaul the data models and warehousing infrastructure of the client, significantly enhancing their data processing capabilities. As a result, customer segmentation, a task that previously took three days, was completed in just 20 minutes using a modest 10-node distributed computing setup.
This transformation harnessed the power of Hadoop in conjunction with the Spark computing and processing framework, along with in-memory database caching techniques. By optimizing data processing efficiency, Whiteklay’s solution facilitated faster decision-making and improved data governance within the big data ecosystem for the banking and finance sector.