A Fortune 500 provider of health insurance with operations in 40 U.S. states.
A Unified Enterprise-Wide Data Strategy
A Fortune 500 health insurer’s data integration and data sharing limitations with newly acquired companies were hindering its growth strategy. They engaged Improving to define and implement a unified enterprise-wide data strategy.
Our client struggled with siloed and inconsistent data, a lack of a centralized location for data, and no clear patterns between departments for the ingestion and consumption of data. In addition, newly acquired companies had different data strategies, making it difficult to unify their efforts and bring products to market efficiently.
The Improving team established a general scope for a new modern platform and designed and implemented an architecture to create an enterprise-wide data fabric, a self-service data mart, advanced analytical capabilities, and allowed for future integrations.
We identified the full scope and the design required to set up Snowflake and Azure environments. We helped define business functions behind over eight canonicals and their sources. The combination of Azure and Kafka allowed us to dramatically speed up data ingestion to Snowflake from many various sources. We created a centralized data services hub to establish an enterprise-wide data fabric. Snowflake provided the backbone and served as the data warehouse for the system, creating a self-service data mart that allowed easy access to the data.
Along with the data warehouse implementation, we constructed various ingestion and consumption methods to be used in combination with Snowflake. We built software for easy management and creation of custom APIs for data stored in Snowflake, helping streamline development and reduce any issues when we encountered setbacks. We also built several self-service tools to help their data engineering team manage new requests to the platform and taught them to use technology to automate tasks they would previously do manually.
The Business Benefits
The new architecture eliminated years of technical debt and enabled contributions to the larger enterprise more quickly and created a set of ingestion and consumption patterns to allow easier access to data enterprise-wide.
The project identified business gaps, helped eliminate the siloing of data, and created a unified approach to handling the enterprise’s data. They now have a centralized location for all of their data and can integrate with a Machine Learning Platform, a self-service data mart, and advanced analytics, and have more flexibility to grow with their business capabilities.
This modern platform provides data from various departments and acquired entities to other realms of the organization, giving them the tools to make smarter business decisions.
The Improving team understood the business needs and aligned the right team resources to implement the solution, including Platform, Microservices and APIs, and Snowflake and Modelling Teams. We involved the right internal client teams for an easier transition of the platform and brought them up to speed on the new platform technologies to manage and maintain the project for the long haul.