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Healthcare Insurance Provider

Enterprise Objective

  • One of the largest US healthcare insurance providers needs to handle huge amount EDI and Patient data to gain insights in timely manner.

  • The data engineers and analysts need to be empowered with using self-serve advanced analytics and delivering top quality results quickly.

Business Use Cases
market-analysis.png

  • Customer had identified several use cases.

  • Automated Ingestion and transformation of complex patient data using orchestrated workflows.

  • Automated connectivity with Data lakes, EMR Cluster and Airflow Scheduler for processing of petabytes of data through 100+ daily jobs.

Challenges
Complex, Siloed & Time-Consuming Solutions

  • The current data processing processes are very complex and time-consuming. The development and execution tools are siloed and difficult to manage. They involve a lot of local coding and little automation, often lacking a regulated process.

Difficult to Get Value Out of Data Analysis

  • Lack of extensive Data Quality Assessment.

Hard for Data Analysts, Scientists, Engineers and Product to Collaborate

  • In the absence of a powerful single-pane-of-glass data platform, it is impossible for users to collaborate. Users need no-code or low-code solutions.

Inability to Scale Out Use Cases

  • Need to scale from local machine to cloud. Need to boost productivity and reduce time-to-market while accelerating the solutions.

Solution

Fast development and deployment on top of AWS. 
Quick delivery of Business use cases and fast Time-to-Market

Sparkflows was installed in a secure air-gapped cloud environment.

The DevOps admin quickly configured secure connections with clusters and databases.

Data engineers were able to quickly connect to data sources like S3, DynamoDB, Redshift, and RDS.

Using the 450+ functions in Sparkflows, developers created scalable ETL workflows and automatically generated distributed code.

The jobs ran distributed on EMR, allowing for easy scalability.

​With access to 80+ ML algorithms, users were able to quickly build ML models.

Multiple teams collaborated efficiently by sharing projects and workflows in a secure manner.

Extensive data quality assessments were conducted.

Business teams quickly built analytical apps and powerful reports within the same project.

Faster insights were derived from the data.

15X

Increase in User Adoption

25X

Reduction in Time to Market

20X

Increased Collaboration

10X

Higher Use cases

15X

Overall Cost Reduction 

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