Modernizing insurance analytics from legacy data to cloud reporting

The situation

A life-insurance organization was modernizing analytics and automated reporting built on policy, rider, eligibility, and claims data stored in IBM DB2.

The design question

How could large operational datasets move into a reporting foundation that supported analysis while remaining understandable and supportable in production?

My contribution

I worked as part of the business-process transformation team designing the data-warehouse solution. I developed ETL processes, wrote ad hoc SQL for insurance analysis, explored demographic and policy patterns, and performed impact and root-cause analysis on pipelines and scheduled jobs.

The system

The architecture connected legacy operational data to structured reporting through repeatable extraction, transformation, and loading. Production support and failure diagnosis were treated as part of the system rather than an afterthought.

Outcome and lesson

The work established a clearer path from IBM DB2 data to cloud-based analytics and automated reporting. It reinforced that modernization succeeds when the new architecture is accompanied by traceability, validation, and a practical operating model.