An insurance provider with operations throughout the world handles millions of policies annually. This company is one of the largest property and casualty insurers in the United States, with a significant presence in commercial and specialty markets, and is consistently listed among the Fortune 500. Like any insurance provider, much of the success of their business resides upon having a complete and accurate view of their customers.
With a history of growth through mergers and acquisitions, the company had data residing in divergent and incompatible systems. And, even though this same data was crucial to their decision-making, business owners had little control over it.
Householding was also a particular concern for the company. Without a single view of the customer across systems, the company had no easy way of identifying customers who held multiple policies with the company, or worse, they could not prevent mistakenly soliciting an individual who was already covered under a policy held by another member of the same household.
Poor data quality affected the company in many ways. The company discovered that it had been sending multiple solicitations to the same clients, approaching clients about policies for coverage they already had, and missing opportunities to offer clients and prospects services they genuinely needed.
The company decided to undertake a “philosophical change” and make data quality a top priority throughout the enterprise. For this effort, they needed a data quality solution with the ability to give business users control over the data.
The company selected DataFlux to increase the quality of customer information. The intuitive DataFlux interface allowed the company to establish business users as data stewards. Business users appreciated the logical, sequential layout that allowed them to construct jobs to rationalize, standardize and transform data, giving them the ability to manage business-critical data without relying on time-consuming and costly IT implementations.
DataFlux technology allowed business users to manage data across sources. Duplicate and incomplete customer records were isolated and corrected, and the company gained a more realistic view of customers by properly householding client information.
The company made data quality a part of its day-to-day operations. Existing data was corrected and standardized, and new data now conformed to the established standards.
DataFlux technology produced immediate results. In one instance, DataFlux drastically reduced a previously manual claims review process, resulting in a verified 93% savings on the exercise. The company established a Data Quality Center of Excellence to help replicate these initial successes throughout the enterprise.
Improved customer householding led to increased efficiencies and reduced costs. Multiple members of households covered under separate polices were merged into a single master record, allowing the company to provide better customer service and limit ineffective targeting of clients.
With DataFlux, the company became more active in integrating data quality into its operational philosophy. With more direct control over the quality of the data in their hands, the business users driving the decisions that directly affected the company’s profitability had more control over their ability to make the correct decisions.