Modern Data and Analytics Platform Implementation

Client

ESO is the largest software and healthcare data solutions provider to emergency medical services (EMS) agencies and fire departments in the US, serving over 10,000 SaaS customers and more than 500,000 users.

Challenge

Empowering Customers with Data-Driven Insights That Save Lives Driven by their mission to improve community health and public safety through the power of data, ESO pioneers and delivers the most reliable, innovative, and easy-to-use software and data-driven healthcare solutions on the market. One of ESO's key technology initiatives was the implementation of their new data analytics platform, which needed to be highly flexible and scalable, supporting 24/7 availability and spend optimization. A key challenge of the implementation was ensuring the proper ingestion of large volumes of data from a multitude of diverse systems, each comprised of numerous internal and external sources. A conventional ETL approach would not meet the requirement for real-time data flow.

Solution

Data Mesh and Real-Time Analytics Using Cloud Resources ESO implemented a data mesh architecture using Azure, Snowflake, and Business Intelligence and data visualization tools. This approach enables domain teams to ingest the operational data, build data models as data products, and combine information from every ESO domain to provide customers with a 360-degree view of performance through one single dashboard. This solution is a testament to ESO's commitment to delivering cutting-edge healthcare data solutions that drive real-time insights. Additionally, a dedicated data platform team provides domain-agnostic functionality, tools, and systems to build, execute, and maintain interoperable data products for all domains. These efforts align with ESO's strategy of leveraging data-driven healthcare solutions to transform operational efficiency and decision-making. Each domain data source has its own corresponding data pipeline, built mainly on Azure by leveraging tools like Azure Functions, Blob Storage, Service Bus, and Event Grid, among others. These pipelines have their own independent data lake, and they ingest a common Snowflake data warehouse with its unique database schema. The pipelines are responsible for transformations, aggregations and ingestion. Extraction is also part of the pipeline, generally based on event notification or change detection in near-real time (pub/sub model). Common libraries ensure standardized pipeline implementations, while a dedicated data platform team manages information flow, access mechanisms, and cross-product data infrastructure, enabling subsequent analysis by data consumers.