Companies continue to struggle with how to implement an organic and systematic approach to data science. As part of an ongoing trend to generate new revenues through enterprise data monetization, products and services owners have turned to internal business analytics teams for help, only to find their individual efforts fall very short of achieving business expectations. Enterprise Data Science (EDS), based on the proven techniques of  Cross Industry Standard Process for Data Mining (CRISP-DM), is designed to overcome most of the traditional limitations found in common business intelligence units.

The earlier post “Objective-Based Data Monetization: A Enterprise Approach to Data Science (EDS)” was in initial cut a describing the framework. It defines data monetization, hypothesis driven assessments, objective-based data science framework, and the differences between business intelligences and data science. While it was a good first cut, several refinements (below) have bee made to better clarify each phase and their explicit interactions.

Data Science Architecture Insurance Prebind Example

In addition to restructuring the EDS framework and its insurance pre-bind data (all the data that goes into quoting insurance policies) example, it was important to document the data science processes that come with an overall enterprise solution (below).

Data Science Process


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