Trianz was approached by a Fortune 500 insurance company that needed assistance with large-scale professional indemnity (PI) data migration. The timeline was rapid, and strict compliance rules needed to be integrated at the architectural level to comply with auditing requirements.
The client needed to migrate voluminous datasets from its mainframe system to a modern database environment. The data consisted of historical datasets and highly sensitive PI datasets. The rapid turnaround was necessary so that the client could achieve compliance ready for imminent auditing procedures.
Trianz identified a range of suitable technology components to achieve the project’s objectives:
Microsoft SQL Server was chosen on the Azure cloud as a database solution.
SQL Server Reporting Services (SSRS) was used to create, deploy, and manage paginated insight reports, accessible across the company.
SQL Server Integration Services (SSIS) was used to integrate historical and PI datasets and enable data transformation functions.
Microsoft .NET was used to run compiled SSRS and SSIS packages with C# code.
Python was used to interface with the MS SQL database, compatible across Windows, Linux, and macOS.
Trianz started by developing a reusable migration assistant tool for the insurance client. This would enable automation features for creating source metadata, control files for source data analysis, and dynamic SSIS packages. End-to-end extract-transform-load (ETL) automation allowed for several heterogeneous systems to converge into one structured database.
Historical data was migrated from source transactional systems built on Mainframe, SAP, and the client’s enterprise data warehouse (EDW) to a unified and scalable SQL Server database.
Robust data models were created to logically group data ready for predictive analytics and other ongoing business needs. This resulted in a centralized reporting architecture that met departmental requirements across the business.
Finally, MS SQL Server FileStream and FileTable enabled the integration of unstructured data relating to insurance policies and claims. These datapoints were stored as BLOBS and CLOBS in the source transactional system to enable a user interface for direct access to analytics tools.
Historical data is now readily available for ongoing business needs, including in-force policy conversion, open claims conversion, and billing conversion.
A unified and scalable data model allows for easy access to business information. The model supports predictive analytics, product development, underwriting, claims, distribution, operations, and billing workflows.
Hierarchical data was extracted from the legacy mainframe and added to MS SQL. This enabled immediate access to data across all business departments, with a centralized reporting architecture for business intelligence operations.
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