11 Sep Health Payers: Schema Versus Data Lakes and Social Determinants of Health Efforts
With many payers embracing social determinants of health (SDOH) for “whole person” care, there are bound to be challenges when it comes to managing massive amounts of health data.
In addition, tackling social barriers, such as transportation access, safe housing, as well as health and diet, requires a major effort to standardize how data is collected, processed and integrated to develop programs that support patient well-being.
As such, many payers are most likely considering the options between schema/structured and data lake environments for making these datasets more actionable and valuable to their SDOH efforts.
Both options have their opportunities and challenges, and below is a quick summary.
The schema/structured environment serves as a more traditional storage mechanism (i.e., SQL and NoSQL). Essentially, they are built around the data elements that a payer is interested in accessing. This makes it easy to plan, normalize and standardize the data to drive analytics and make key decisions.
In the data lakes environment, the data is stored in a raw form without any really known parameters for indexing and accessing this data. The convenience is that you have a wider-range of data to access. Though accessing this data requires a high-cost of computing, hardware and infrastructure, where many technologies need to work in data to build structure for data on the fly.
Thankfully, the IMAT Solutions platform offers the best of both worlds. The platform was built for all types of data and data retrieval supporting the best of a schema based and data lakes solutions. It allows users to have all of the known fields in a structured environment, while still pairing the ability to access any unstructured data at anytime.
Please listen to this recent IMAT solutions podcast interview where we dive deeper into the differences between schema/unstructured and data lakes environments.
If you are a health payer, and would like to learn more about how you can best leverage both data environments, please contact us here.