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Data Re-architecture Consulting

Re-modelling of enterprise data warehouse to achieve flexible BI reporting with reduced turnaround time for large insurance company in Asia Pacific


  • Data Model :
    • Hindered self-service BI reporting.
    • Higher TATs in meeting business reporting requirements.
    • Insufficient data insights.
    • Higher IT support costs.
    • Higher time-to-market.
  • AI-Analytics | Process Automation :
    • Underutilized AI-analytics potential.
    • Impacted self-service AI-analytics capability.
    • Unexploited avenues of AI-analytics due to absence of business use cases.
    • Low operational efficiency due to lack of AI-enabled process automation.

Our Solution

  • Effective & flexible data model architecture enabling self-service BI reporting with lower IT maintenance costs.
  • Well-defined AI-analytics business use cases.

Final Result

  • Data remodeling in stepwise manner yielded the following ensuing faster-time-to-market, faster BI reporting and higher data insights with lesser IT-dependence:
    • Data Lake Storage Layer as the primary source.
    • Unified STAR Schema Model – for the EDW.
    • Summary tables for quick-persistent storage-based reports/dashboards.
    • ABTS for persistent storage-based analytics.
    • Data Lake [HDFS] as key source for ad-hoc analytics.
  • Implementation of Umbrella Business Cases through the following analytical models (creating positive impact on loss ratio, operational costs, enhancing productivity etc.) around AI-analytics within the space of Life and General Insurance:
    • An early Claim Prediction Model.
    • False Medical Prescription Entries Detection Model with AI-based Workflow.
    • An accurate Lead Generation Predictive Model.
    • An accurate Claim Abuse/False Claim Predictive Model.
    • Customer Churn Prediction Model.
    • Unfavorable Risk Outcome Prediction Model.