​​Top 3 data problems insurance carriers face​

The hard market has highlighted the critical need for understanding competitiveness and overall rate trends

Carriers sitting at a table looking at data.

​​In the current hard market, data can be extremely helpful to insurers to understand how the insurance market is moving in terms of competitiveness and overall rate trends. Successfully managing multiple data outlets has become increasingly critical, and oftentimes carriers face three common problems with their data.  

​1. Multiple data stores or worse, data islands  

​It seems like every day brings some new technology, new software, or new and necessary update to an existing system. For in-house technology teams, keeping up with these new requirements can feel like an unwinnable race where every new tool makes their work harder. The effort spent managing isolated or incompatible data sources only increases.

Having multiple, disconnected outlets of data can result in inefficient organizational processes that pose challenges for employees, partners, and policyholders. To compete in a leaner market, it is important for carriers to be able to navigate complex data systems in a timely and efficient manner. 

​2. Inconsistent data practices 

​Data collection, management, and use of customer information is unique to every insurance product. This makes comprehensive integration challenging, but not impossible. When data is collected from other systems, it needs to be refreshed, formatted, and cleaned. Otherwise, you risk the data becoming outdated and more difficult to consolidate. A recent analysis in PropertyCasualty360 about AI and property data notes, “This leaves insurers with the strenuous task of managing, storing, and extracting value from an ever-growing volume of data from disparate sources. Forrester reports that between 60% and 73% of all data within an enterprise is never analyzed at all.”  

​Inconsistent data can be a significant challenge for businesses, as it leads to inaccurate analysis, flawed decision-making, and operational inefficiencies.  

​3. Merging data without compromising data integrity 

​Merging data effectively involves a systematic approach to ensure that all relevant information is combined accurately without duplication or leaving out actionable details. By following a reliable process that includes data cleansing, mapping, conflict resolution, validation, and ongoing maintenance, carriers can effectively merge their data resources and ensure no important information gets lost. 

​Increasingly, many insurers are leveraging artificial intelligence (AI) to better navigate data consolidation. By automating processes to produce real-time insights from aggregated resources, artificial intelligence can vastly improve data management. 


​Fortunately, there are proven solutions available that can address these challenges and ensure more effective insurance data management. By addressing these data challenges proactively, carriers can improve both decision-making and operational efficiency, better manage risks and maintain regulatory compliance, and even forecast future underwriting trends more effectively. 

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