A top-ranked insurance company has issued more than 10 million auto policies nationwide. The company sells policies from a direct business unit and through more than 30,000 independent agents and brokers.
Like many organizations, this insurance company had millions of customer records in multiple data warehouses. Since the organization’s ability to write suitable policies was driven by having accurate information on its clients, maintaining the integrity of that data was essential.
Because of the sheer amount of data this company and its independent agents processed on a daily basis, it was inevitable that inconsistencies would occur. Different business units — and individual employees within these units — would apply different standards on customer records. With high volumes of data, those small inconsistencies could quickly snowball into major issues.
Since manually monitoring the data was impossible, the company sought a data monitoring solution that could automate this process. The company evaluated multiple vendors based on several key factors, including requirements that the solution include comprehensive monitoring and auditing functionality — powerful enough to address millions of records — and that business staff could manage the system without relying on IT staff. The solution would also need to operate seamlessly within existing applications, adding critical data governance controls to the current IT infrastructure.
The company selected the intuitive DataFlux interface to give business users a powerful platform to develop a complete set of business rules for maintaining high-quality data.
The company uses DataFlux to monitor multiple data warehouses, as well as an application that examines the appropriateness of policies. When new data enters the source systems in a monthly upload, the company can examine the new information in the tables, compare that data to the previous month, and then analyze the results against user-defined business rules.
Any differences found during this exercise are output to a database, where a data quality analyst examines the results to eliminate the explainable exceptions. The exceptions each month that cannot be easily reconciled are sent to a data warehouse programmer for investigation and resolution.
This company immediately saw success in the improved data quality in its data warehouse. The data warehouse supported better analysis of business events and provided more insight into trends in the customer base. The company also found that its initial success led to even more success in refining business operations.
The company discovered patterns in the nature of the problems that the monthly data quality run was uncovering. Because of the unique, intuitive DataFlux interface, business users were able to quickly build business rules to address these repeating issues – and build policies that can enforce corporate standards for data quality.
This allowed the company to improve its entire data quality process - making it more efficient by eliminating false positives while also increasing the number of potential problem areas that could be checked.