Uncovering THE BUSINESS VALUE OF DATA

INTEGRATEDANALYTICS

Uncovering THE BUSINESS VALUE OF DATA

A Fortune 500 property and causality insurer needed a more accurate, integrated way to organize, store, and manage data. Having a global footprint, their data was dispersed across multiple regions, platforms, and lines of business. Reports from respective regions were being manually collated and reconciled, creating inconsistencies and rendering real-time business insights impossible.

The client partnered with Trianz to develop a strategy and an actionable roadmap for a data warehouse for its Personal and Business Insurance (P&BI) division. We thoroughly assessed the client’s exiting data sources; mitigated risk for data from unexpected sources; mapped business needs and arrived at common definitions across lines of business; even built a business view of their data tree for ease of use. We then piloted a PoC by ingesting data from a few locations, and built an enterprise data warehouse and data mart with reporting capabilities. The data warehouse was later extended to include other data sources, including their auto insurance, creating an enterprise data warehouse.

Building Blocks

Select the plus signs for more information.

THE BUSINESS CHALLENGE

With data spread across multiple platforms and geographies, manual collation and reconciliation resulted in inconsistent information that prevented real-time insights into revenue and profitability.

TECHNOLOGY COMPONENTS

  • Infosphere CDC
  • Informatica Powercenter
  • Netezza(PDA)
  • Qlik
  • Aginity
  • Cognos Framework
  • Cognos Active Reports

THE APPROACH

  • Started with data analysis and profiling across geographies and LOB
  • Conducted workshops to facilitate decisions and agreement on common definitions
  • Centralized all source data in a staging area and built data tree
  • Developed proof of concept for two countries and later extended to ten other countries
  • Rolled out enterprise data warehouse to include auto lines of business across all countries

TRANSFORMATIONAL EFFECTS

  • Mitigated risks by addressing data quality issues upfront
  • Established common business definitions across regions and countries
  • Enabled business users to analyze and unearth insight on revenue & profitability across LOB
  • Developed an ROI model for global enterprise data warehouse initiative