Solving Insurers’ Data Quality Issues

Learn how leveraging advanced analytics can set you apart from your competition

Mark PriebeMarch 21, 2017

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Data quality is the backbone to any analytics program. According to a study done by West Monroe Partners published in Digital Insurance, “poor data quality is the biggest challenge for insurers looking to implement advanced big data and analytics in their enterprise.”

The study titled, “Data Driven Insurance: Harness Disruption and Lead the Way,” surveyed 122 insurers. Of those surveyed, two-thirds of all respondents said that data quality is the biggest challenge when it comes to implementing an analytics program. Some other alarming results from the survey included 51% of respondents said that inaccurate data was the biggest risk. In addition, only 57% of insurers surveyed said they believe that their companies are fully realizing the benefits of an advanced analytics program. These are not good results in today’s data driven world.

Why This Matters

In the insurance industry, leveraging advanced analytics can set you apart from your competition. With a good analytics program, insurers can take a customer-centric approach by analyzing the complete lifecycle of a customer. They can then use that data to drive increased customer satisfaction, deliver assets at the right time, and improve the overall bottom line.

By understanding and embracing the value of a customer base, insurers are able to stand out from the competition. But success is still dependent on data quality. An analytics solution that incorporates data quality and analytics into a single solution is the recommended best practice to optimize your analysis.

The Solution

A data integrity and analytics solution automatically monitors an organization’s data flow as it continuously moves throughout an enterprise. The solution conducts field level checks into the completeness, type conformance, value conformance, and consistency of data at its origination, identifying any potential data quality issues at the point of creation.

By analyzing data both at summary and transaction level as data is exchanged and transformed throughout the data supply chain, insurers can verify, balance, reconcile, and track that all of that data being processed is accurate. Once the data is accurate, insurers are able capitalize on their quality data to run advanced analytics to solve complex business problems with greater confidence.

To learn more about data quality and data integrity in the insurance industry, check out this recent report done in conjunction with industry analyst firm, Novarica.

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