Global P&C Insurer Seeks to Build an Enterprise Data Warehouse
A Fortune 500 property and casualty firm approached Trianz to help them establish a more accurate and integrable way to organize, store, and manage its business datasets. As a global firm, its datasets were widely distributed across geographical areas and various business departments.
Business Challenges Around Data Silos and Reporting
The P&C client had data distributed across multiple geographies, business IT platforms, and departmental data silos. This data needed to be centralized. Reports were manually created, collated, and reconciled, leading to inconsistencies, errors, and little potential for real-time business insight into revenue and profitability.
Technology Components Supplied by Trianz to Build the Enterprise Data Warehouse
The team at Trianz identified technology components that aligned with project requirements for the P&C insurer:
Infosphere CDC was chosen for low-impact capture and delivery of data changes for key information management initiatives.
Informatica PowerCenter offered scalable, high-performance data integration functionality to centralize the client’s datasets.
IBM PureData System for Analytics (PDA) is a hyperconverged data warehousing solution that accelerates analytics and business intelligence operations with dedicated field-programmable gate array (FPGA) hardware.
Qlik was selected to provide visual analytical dashboards and interactive reports.
Aginity was chosen as an active analytics catalog to reuse SQL queries in analytics tools.
Cognos Framework and Active Reports were used to model metadata in the data warehouse, and to create active and easy-to-use reports.
How Trianz Worked to Implement a Data Warehouse for the Fortune 500 P&C Client
The client partnered with Trianz to develop an actionable roadmap for a data warehouse in its Personal and Business Insurance (P&BI) division.
Existing data sources were assessed, mitigating risk with data from unexpected sources. This also covered existing data sources and mapping business needs to arrive at common definitions across departments. Trianz also created a data tree view for the client’s ease of use.
Next, a Proof of Concept was piloted by ingesting data from a small selection of distributed data sources. Then, the enterprise data warehouse and data mart were constructed and integrated with reporting and analytics tools. This data warehouse was further developed at the late stages to integrate the client’s auto insurance arm.
Transformational Effects After Enterprise Data Warehouse Implementation
Trianz was able to mitigate risk by addressing data quality issues upfront for the client. Manual collation and reconciliation workflows were reduced, lowering the possibility of introducing human error.
By establishing common business definitions using Aginity, the client had data and reporting consistency across regions and countries.
Business users could now analyze and generate insight on revenues and profitability across all lines of business. This expanded analytics access to all employees, enabling greater awareness and productivity.
Finally, a return-on-investment model was created for the data warehouse, giving stakeholders clarity on the long-term value of the new solution.
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