Aptitive created a data hub architecture that resulted in consistent and accurate reporting.

The Problem  

When data analysis is disjointed, businesses struggle to extract consistent and accurate enterprise-level insight. Our industry-leading automotive insurance client was a prime example. Comprised of multiple acquisitions, they strained to run consolidated reports from unique databases as a result of numerous policy and claim applications spread across their various lines of business (LOBs).

For example, when running operational  reports across important dimensions (state, LOB, insurance product, etc.), they struggled to piece together comprehensive insight. Thanks to an ongoing partnership with Aptitive, they knew our strengths with data management and the challenges of running reports in the insurance industry (especially on policy endorsements and claims transactions).

Our Solution

Some of the initial requirements were ambiguous, so the Aptitive team implemented an agile management approach to clarify the appropriate direction. We interviewed the various stakeholders to identify the business questions they wanted answered and the extensive data sources critical to future reporting. Once the project scope was clearly defined, we dove into delivery.

Using SSIS for ETL and SQL server, we built pipelines into a single “source of truth” our client could use to achieve greater accessibility and performance in reporting. Our logical data warehousing model integrated over 25 disparate data sources (all with their own unique schemas and business logic) into a central location. We unified meta-data, security, and governance to ensure that analysis across multiple lines of business would result in accurate and comprehensive insight.

Our team made the most of UAT, validating how well our centralized data warehouse satisfied the needs of business users. After the initial launch, our insurance client was slow to adopt their new capabilities. That’s why we worked closely with their internal team to showcase their new reporting potential, develop a better distribution ecosystem, and foster change management.

The Outcome

Our client now enjoys a dramatic simplification of their monthly P&L and operational reporting. A common source of data has cultivated more consistent and accurate reporting that increases agreement between corporate and various LOBs.

Their centralized data warehouse has also set the stage for greater innovation in the future. Data science models can soon be applied for more accurate underwriting, fraud detection, and reserve calculations. Plus, self-service dashboarding, deductive data discovery, and bigger and better reporting are now well within reach.

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