Cloud Data Insurance

Pekin Insurance Accelerates Decision Making with Data Migration to Snowflake’s Cloud Data Warehouse

Pekin Insurance dramatically cuts costs and democratizes data for accelerated decision-making and business agility by migrating its data to Snowflake’s Cloud-based Data Warehouse.

Business Need

Pekin Insurance was struggling to derive the right value for their data and reports due to significant data accessibility limitations with their on-prem systems. Given the rapid pace of the digital economy, Pekin recognized the need for a modern data platform to foster a data-driven culture.

Tools and Technology

Pekin decided to leverage Snowflake’s Cloud-based Data Warehouse on AWS, which offers considerable flexibility for scaling, computing, and storage. With ValueMomentum’s technical experience with the platform, Pekin converted thousands of tables in multiple programming languages into SQL and migrated historical data and daily data onto the Snowflake database.

Success Factors

ValueMomentum’s technical expertise and the close collaboration between Pekin and the ValueMomentum team allowed Pekin to overcome the project’s challenging timeline and put the migration on-track to complete by December 2020.


For Pekin, the initial investment driver was cost-savings—with a one-third predicted reduction in functional operational costs, the data migration to Snowflake is predicted to pay itself by year end. Also, by allowing Pekin to generate reports near real-time and derive valuable insights from their data for agile decision making, the Snowflake cloud data warehouse migration will put Pekin one step closer to their goal of becoming a truly digital insurance company.

With ValueMomentum’s expertise, we were able to migrate our data platform from on-prem to the Snowflake AWS. This migration will allow Pekin to shift significant resources from maintaining the data platform to quickly creating business insights and value for our business partners.

Head of Data Science/Director of Data Management, Pekin