4 Keys to Improving Insurance Data Quality

Hidden barriers to becoming a data-driven insurance carrier

It is reasonable to expect that data-driven decision making would follow the usual pattern of development and adoption: early adopters start out with centralized, carefully managed projects that eventually move into limited production. Then, as the value of the technology becomes clear, implementations begin to proliferate, resulting in more distributed use.

What drives this “standard” technology adoption pattern? Trust. Or more correctly, the lack thereof. Concerns about the complexity and cost undoubtedly play a part in slowing the adoption of data-driven decision making. However, efforts to become a more data-driven insurance carrier actually suffer from an even deeper layer of mistrust. The problem here is not just the technology and processes involved. It is the deeply rooted lack of trust in the data itself.

At this point, the science and technologies for data analysis are already quite powerful and well proven. In fact, the trend across the insurance industry is rapid advancement of business intelligence and analytics into the realms of artificial intelligence (AI) and machine learning (ML).

But it is exactly such advancements which are now making the underlying problem of poor data quality painfully obvious. When you apply advanced analytics, and especially when you unleash AI and ML to detect risk, prevent fraud, or improve operational efficiency, the impacts of bad data are magnified.

Insurance carriers too frequently have considered data quality issues unavoidable and uncontrollable, and therefore just another cost of business. But the unavoidable reality for any carrier looking to adopt data-driven decision making is that it cannot succeed without fully and actively addressing the age-old scourge of poor data quality.

This eBook will guide and inform you regarding how to overcome the root problems of data quality, not just identifying specific types and patterns of quality problems, but also clarifying the roles of data quality management and data governance in resolving them.