In the e-book chapter, “More Speed at Heart of Spark's Appeal for Big Data Users,” Novantas’ Kaushik Deka discusses the intricacies of creating an Analytical Workbench using Spark.
Capturing, managing, processing, and analyzing large amounts of data has quickly become table stakes with regard to analytic capabilities in the banking industry. To effectively compete, executives need the analytic sophistication to conduct customer behavior analysis and point it at business problems. But, the big question remains what kind of investments are necessary to extract actionable intelligence from large data sets?
In response to this burning question, Novantas “is using a Cloudera-based Hadoop and Spark system to run an application called MetricScape that it initially built for a bank early this year. Kaushik Deka, CTO and director of engineering for the company’s Novantas Solutions technology unit, said the application acts as a librarian of sorts for customer and financial data metrics, providing a governance layer that tracks things such as data lineage, definitions and dependencies.”
“The idea, Deka said, is to help data scientists pull together relevant data sets for analysis. In the case of the initial user, that involved looking at customer account histories, the results of previous marketing campaigns and other data to segment millions of bank customers based on their likely responsiveness to planned promotional offers. […] Novantas is also working conceptually on a second application that would put Hadoop and Spark at the heart of an automated rules engine aimed at providing bank employees with analytics information in real time. For example, bank managers dealing with customers looking to negotiate reduced mortgage rates could get on-the-fly rate recommendations based on the customers’ overall relationship with the bank, Deka said.”
Download the full e-book chapter here: http://bit.ly/29zxQVa
Reference: Craig Stedman, SearchBusinessAnalytics/TechTarget, 2016