The “Silver Tsunami” of baby boomers entering their 80s presents unique challenges for analytics practitioners, particularly in the senior care industry. More than any other industry, senior care requires scalable data architecture built from loosely coupled components because of these trends.
One growing sector within senior care is medically tailored meals MTM. Seniors often have a high prevalence of co-morbidities or chronic illnesses, necessitating meals tailored to their medical needs. MTMs provide a convenient, affordable, and high-quality option for seniors to manage chronic illnesses without relying on medication, offering insurers an opportunity to reduce hospital visits and medication costs.
For a company like Heritage Meals in the MTM sector, data engineering aims to connect patient and business data with health outcomes to track program effectiveness. This involves securely integrating healthcare data (diagnoses, medications, outcomes) with operational data (meal plans, delivery tracking, patient feedback) to optimize patient care and business operations.
Key considerations include:
Data Sources: Utilize electronic health records (EHRs), customer relationship management (CRM) tools, and operational systems for meal assembly and supply chain management.
Data Ingestion and Storage: Use microservices like FiveTran or Estuary for batch processing/streaming data into cloud environments like AWS, Snowflake, Google Cloud, or Microsoft Azure.
Data Transformation: Convert unstructured data into columnar formats for conversions with SQL. Improve cataloging and orchestration of the pipeline with analytics engineering tools like DBT. -
Serving: Make data analytics-ready by creating the views and data tables for business users, or stage those resources for feeding back into other systems with Reverse ETL.
Analytics: Turn insight into action with BI/visualization tools like Tableau or Power BI.
Regulatory compliance in this context is absolutely critical so security must weigh heavily into any decision, along with data management, DataOps, architecture, orchestration, or software concerns. As the food business is thin-margin, maximizing our storage costs would be critical as well. Regardless of the vendor or tool, our primary objective is to build scalable and loosely coupled systems for the pig-in-a-python that is the the baby boomers population growth. See the directed acyclic graph (DAG) below for an overview of this framework.
The key role of any data pipeline is to deliver business value, so driving actionable insights from analytics remains our north star. To that end, I have developed a mock-up of a dashboard for an executive at Heritage Meals.
Cheers