Onboarding like a Pro at a Large Reinsurance Brokerage

  • Processing more data, faster and providing a better user experience by automating customer data feeds ingestion.
  • Customers are able to upload and view their portfolio data within minutes, rather than weeks.
  • Lightweight solution architecture allows the broker to streamline data ingestion and push the process to their customers.
  • Employee productivity is increased and top analysts are able to focus on higher-value tasks.
  • Improved the quality of data by more than 50%, now traceable through various KPIs in different dashboards and reports.
  • Support company objectives and growth expectations via a flexible and highly scalable infrastructure.

A large reinsurance broker provides fast and reliable analytics to their customers and partners to help them make better business decisions. Processing data accurately and quickly enables this broker and its partners to anticipate consumer needs, respond to sudden challenges, accelerate market entry and optimize profits. One of the valuable insights brokers provide to their customers (insurance companies) is that they can analyze and predict the risk of their portfolios through the client’s analytics platform. This platform superimposes geological and other threat data onto customer portfolios, allowing customers to evaluate the impact these events will have in specific regions. These customers provide the client with structured data files of different layouts and site level information. In the past, ingesting and processing this content was largely an ad-hoc process, requiring manual Excel manipulation, and custom stored procedures to properly load this data into their various models. This data ingestion process was long and costly due to the labor involved in managing and cleansing customer data.

In order to improve the onboarding process of their customers’ data, the broker hired NGDATA to create a self-service solution, with a web front-end, that would allow customers to upload their data directly into their analytics platform on their own. One of the key requirements for this solution was to have an underlying process which would auto-apply many different business rules to the data in order to organize the information and get it in the format desired by the downstream analytics platform.


The NGDATA-built platform with data handling capabilities enabled the customers to upload data and have the results available in the analytics platform within minutes, rather than weeks. This capability is crucial when factoring natural disasters into risk models due to the unexpected nature and duration of these incidents. Some natural disasters can occur without any warning, and even if the customer is given several hours’ notice, the broker’s old process would have taken too long for their customer to make informed decisions. With this new implementation, the customer can now upload their data and visualize the risk within minutes, which potentially could help them save millions.

With an NGDATA-built solution that utilizes Informatica Cloud, the broker was able to automate customer data feeds, enabling them to process more data and provide clients with a better experience. The solution also enabled the client to streamline data ingestion and push the process to the clients, thereby offloading work from top analysts and focusing them on higher value tasks. The Data Quality optimization process helped improve the quality of data, as well as establish the mechanism to track and report on the quality of the data on an ongoing basis. The web application allows customers to upload their data either using a pre-defined template for quick turnaround time, or use a data-mapping wizard to help manually map their structured file to the target staging format. The wizard also makes more complex transformations available to the user, which helps in formatting the raw data file.

The solution NGDATA developed uses highly modular and reusable components for ingesting, preparing and loading the data, so that it could be easily maintained for future enhancements.

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