Revolutionising Insurance with Data Mesh Approach

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Overview of Data Mesh in insurance - a magnifier on a sheet of paper

Project Overview

Technologies

  • AWS Glue
  • Streaming ETL
  • Kinesis Firehose
  • SNS/SQS
  • AWS Lambda
  • Redshift
  • AWS S3

Our partner had organisational scalability issues caused by a centralised approach to data handling. We did shift left on data ownership and built a new data platform based on the Data Mesh approach.

Client overview - a sheet of paper

Client

Policy Expert

An esteemed InsurTech player holding a position at the forefront of the UK insurance market. They successfully leverage proprietary technology and data analytics to revolutionise the home and car insurance industry. 

Policy Expert logo

Headquarters

London, United Kingdom

Industry

Insurance

Challenge of Data Mesh in insurance - a lightbulb

Challenge

Our partner’s Centralised Data Warehouse setup, once effective for its monolithic software architecture, was proving restrictive for the evolving demands of modern business. Struggles with scalability, flexibility, and responsiveness issues, had taken a toll on the organisation’s operations and overall performance. 

The challenge was to replace this outdated approach with a more effective and scalable solution that could address the ever-changing needs of the company while upholding data integrity and security.

A hand with a lightbulb

Our Approach

There were several approaches on our minds to tackle this challenge. Eventually, together with our client’s Data Team, we decided that the best course of action would be to adopt the Data Mesh framework. Why? 

We picked it due to its promotion of domain-oriented data management. It allows teams to work independently, making changes to their data systems without disrupting the whole organisation. Additionally, the Data Mesh approach, at its core, fosters a more distributed model, reducing reliance on a single team and enabling multiple teams to work concurrently, which takes care of any potential bottlenecks. This leads to enhancing agility and flexibility, leaving plenty of room for faster development and iteration.

Having joined forces with our client’s Data Team, we built an exemplary Data Mesh slice. The responsibilities on our side included defining data products and data contracts by implementing tiered products aligned with the Medallion Architecture data design pattern. We also saw to creating data lakes that collected all kinds of information from our client’s event-driven architecture. Meanwhile, our partner’s Data Team provided us with the infrastructure necessary to handle the data products we defined, designed, and developed.

A hand with arrows moving up

Effects

The approach we took has yielded the following results:

  • Enhanced data quality: Our focus on data ownership and quality at the source was picked up by each of our client’s domain teams. This led to a huge improvement in data management practices and overall data quality within every domain. 
  • Improved time to market: In the insurance industry, data is key to measuring the outcome of an initiative. In the previous setup, the data team was responsible for making sure that all data was properly gathered before going live with an initiative. With the Data Mesh approach, domain teams handle this responsibility, and there are no handoffs before going live, which improves time to market and enables faster organisation learning.
  • Increased data privacy and security: Thanks to handing over ownership and responsibility for data to specific domain teams, they are more likely to be aware of and take responsibility for the sensitive data within their domains. This means a strong focus on safeguarding data assets.
Effects of Data Mesh in insurance - rocket

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