Before:
Explosive Growth Identifies a Need for Data Adjustments
Vida Health’s explosive growth during the pandemic necessitated a revamp of its data technology infrastructure to support scalability. However, Vida’s small in-house data team lacked experience with modern data tools. The data team was highly skilled but lacked formalized expertise in modern data transformation technologies like dbt and Fivetran. They had been relying on custom-built solutions that were effective when the company was smaller but were not scalable.
According to Sr. Director of Data at Vida Health, Trenton Huey, “We needed to quickly develop a new data platform. Healthcare is a data-heavy industry, and with the significant growth we were experiencing, our current systems were struggling. We required a scalable architecture, increased automation, and expertise to help drive our data strategy and outcomes.”
Solution:
Implementing a Customized Modern Data Stack to Match Business Growth
Vida partnered with Data Clymer, a Spaulding Ridge company, to quickly deploy a reliable, scalable, and efficient data architecture by integrating dbt, Fivetran, and Looker on top of Vida’s existing BigQuery cloud data warehouse.
First, Data Clymer helped Vida implement Looker across its business, developing business dashboards and setting up KPIs. “Thanks to Data Clymer’s support, we saw a significant increase in business intelligence user adoption, going from 0 to 150,” said Trenton. “Since much of this technology is new for our team, it was helpful to have another person to bounce ideas off of and receive advice on the best approach.”
Next, Data Clymer streamlined canonicalization by implementing dbt and Fivetran. Vida’s customers are health plans and large employers with vast amounts of data that needs to be processed and standardized. With the new tools, Vida can efficiently onboard customers, stratify members, and optimize program delivery.
In addition to setting up dbt, Fivetran, and Looker, Data Clymer trained the Vida team on how to use the tools so they can continuously mature their healthcare analytics data program. The teams continue to work collaboratively to ingest additional medical data sources, create automated clinical workflows, and improve provider efficiency.