Simply connecting to data sources such as SAS, SPSS, MATLAB and R is only one piece of the puzzle.
Earlier in my career, I worked as a Pricing Manager in the auto finance industry. During my first weeks, the common topic of “Adverse Selection” and its negative impact on profitability, pricing response models and credit risk models was discussed in several meetings. This was a new term for me so I walked into my manager’s office and asked him to explain adverse selection to me. His answer:
“I’m not exactly sure how to explain it, but I know it when I see it”
He went on to describe the impact of pricing on the customer’s decision to buy. The biggest fear is that higher risk credits take advantage of poor pricing models and thus, blow up the ability to forecast losses properly. My passion for bringing complex analysis to the entire organization and deliver on the vision and the goal was born.
Bringing the Data Scientist Work to the Point of Decision:
For starters, building applications from data/output of traditional analytics software (SAS, SPSS, Matlab and R) is an extremely common use case in Financial Services. However, the analytics market often gets distracted/confused by the differences between "integration" and “connectivity". My response is yes, Qlik customers do both, plus a whole lot more.
Value is optimized though, when these model outputs are combined with other data sources in an associative model and provided to a broad population of business users.
Business users may not understand statistics but do understand that when an application tells them they are 'out of compliance,' 'under-utilized,' 'missing targets' or 'inefficient'.
Banks, insurance companies and security & investment firms are executing on the vision and achieving their goals through a governed, scalable, enterprise-class data discovery platform that goes far beyond tactically connecting to a single data source.Photo credit: liza31337 / Foter / CC BY