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Cube Dev > Case Studies > Leveraging Cube Semantic Layer for Data Consolidation in Healthcare: A COTA Case Study
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Leveraging Cube Semantic Layer for Data Consolidation in Healthcare: A COTA Case Study

Technology Category
  • Application Infrastructure & Middleware - Data Visualization
  • Application Infrastructure & Middleware - Middleware, SDKs & Libraries
Applicable Industries
  • Construction & Infrastructure
  • Healthcare & Hospitals
Applicable Functions
  • Product Research & Development
Services
  • Cloud Planning, Design & Implementation Services
  • System Integration
The Challenge

COTA, a healthcare company founded in 2011, specializes in combining oncology expertise with advanced technology and analytics to organize real-world medical treatment data for cancer research and care. They have access to millions of electronic oncology patient records, a data volume unmatched in the oncology healthcare industry. One of their products, the Real World Analytics (RWA) solution, helps clinicians and researchers make sense of fragmented and often incomplete electronic health records (EHR) data. However, COTA faced challenges with their existing off-the-shelf solutions like Qlik and Tableau, which required heavy customization and specialty configuration knowledge. They sought a more developer-friendly ecosystem that could handle their vast data and provide a single source of truth.

About The Customer

COTA is a healthcare company based in New York, United States, with a workforce of 101-250 employees. Founded in 2011, COTA combines oncology expertise with advanced technology and analytics to organize real-world medical treatment data. This data is used to guide cancer research and care. COTA builds solutions to better support oncologists' clinical decision-making and researchers' development of new drugs and therapies. Their products help clinicians and researchers make sense of fragmented and often incomplete electronic health records (EHR) data, providing insight into the patient population, treatment patterns, and disease outcomes.

The Solution

COTA found their solution in Cube, a semantic layer that they leveraged to save development cycles that previously involved writing custom queries, custom data munging, and processing. Cube became a foundational tool for COTA's products, with Cube Data Schema serving as the single source of truth for their data and Cube API powering their applications. COTA uses Google Cloud Platform to host and run their applications, including the Cube deployment. They decided to use microservices hosted on Google Compute Engine after initially exploring Google Cloud Functions. The COTA team also uses Angular on the front-end of their solutions and leverages TypeScript support in Cube. They use the Plotly charting library wrapped with the angular-plotly.js component for data visualization in their applications. To ensure efficient resource use, they implemented mutex support to synchronize multiple concurrent requests performed by the Cube API.

Operational Impact

  • The implementation of Cube at COTA has brought about several operational benefits. The use of Cube has simplified the process of data handling, eliminating the need for writing custom queries and data munging. The Cube Data Schema now serves as the single source of truth for their data, ensuring consistency and reliability. The use of Angular and TypeScript in Cube has also made it easier for the team to read and write code, and the advanced boolean logic has simplified the process of creating complex logic to get a result with a single API call. The team has also been able to take advantage of community support and insights on Slack and the community forum, as well as file bugs and feature requests on GitHub. Looking forward, the COTA team plans to migrate Cube to containerized Docker deployments to align with the rest of their infrastructure.

Quantitative Benefit

  • Expansion of data access by more than 300% during COTA's recent Series D funding announcement.

  • Significant reduction in response time for their per-customer or per-product queries due to dynamic schema generation.

  • Release of the minimum viable product (MVP) for their RWA product took about 6-8 months.

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