A leading family life insurance company needed to quickly analyze and use customer data while improving operational efficiency, reducing cost, and increasing revenue. The company sought to implement a data lake to link and integrate customer data, creating a 360-degree view of its consumers, an effort not previously successful with legacy systems and traditional operational data warehouses.

The data lake project was sponsored by the President and CEO, with the main goal of connecting data at the individual, household, and address levels in a rapidly scalable environment to support a multi-year data, analytics, and martech transformation. The lake serves as the client’s data nucleus for transforming opportunities to generate revenue, reduce cost, and operate as change agents in the organization.

Project Team
Over 30 internal and external stakeholders collaborated on the data lake project. Merkle and the client formed a 20-member cross-functional advisory group to identify data-driven use cases. A team of four data scientists developed use cases, and a governance committee of C-suite members prioritized them.

Phases and Timeline
The project was delivered on time in six months. The first phase was a nine-week discovery period. The second phase spanned ten weeks and included design and development. Integration testing was the third phase, which took place over three weeks, followed by a fourth phase, a three-week user acceptance testing. The fifth and final phase was production deployment which lasted less than a week. 

Tools and Technology
Working with Merkle, the life insurance company leveraged a Google Cloud Platform license for the data lake. The Core data resides in Big Query, with connectivity to other Google Cloud tools, as well as DataRobot and Tableau, and access is managed through a combination of MFA, VDI, and Citrix.