Understanding Data Standards in the Insurance Industry

In an insightful discussion with COVER Magazine, Angus Black, Director at BarnOwl Data Services, explored the importance of data standards and the intricacies of data sharing within the insurance industry. He identified three key elements that constitute a robust data set: clearly defined data fields, a minimum set of required data elements, and a set of rules governing data collection. Angus stressed the need for unambiguous definitions to avoid misunderstandings, which can arise from ambiguous terms like gross written premium or sum insured.

While regulatory frameworks provide a necessary foundation for data uniformity, achieving true data standardization across various systems is a challenge. Angus explained that insurance companies often operate on different platforms, both off-the-shelf and custom-built, and may have legacy systems in place. This creates hurdles for data consistency and repeatability. One significant issue is ensuring that data collected today remains the same when reprocessed in the future, which can be complicated by misunderstandings and changes in data requirements.

Angus emphasised the importance of flexibility in handling data collection processes, noting that overly rigid systems can create unnecessary barriers. BarnOwl’s approach involves collecting data as-is from various sources and applying the necessary transformations on their end, rather than requiring clients to modify their data at the source.

Automation plays a critical role in managing large volumes of data efficiently, but Angus underscored the continued importance of human oversight. Experienced data professionals must review outputs and flag potential issues for further investigation. By combining automated systems with human expertise, BarnOwl ensures that their processes remain both efficient and adaptable to the evolving demands of the insurance industry.

Key points

• Three Key Elements of Data Standards: Data standards are made up of clearly defined data fields, a minimum set of required fields, and clear rules governing how data is collected and processed.

• Regulation and Uniformity: Regulatory requirements provide a framework for data uniformity in the insurance industry, but maintaining this standard requires continuous adaptation.

• Challenges of Diverse Systems: The variety of systems, both bespoke and off-the-shelf, across and within companies creates significant hurdles for data consistency and sharing.

• Repeatability of Data: One key challenge is ensuring repeatability, where data collected today can be reprocessed consistently in the future without discrepancies.

• Flexibility in Data Collection: Angus emphasised the need for flexibility in processes, suggesting that collecting data in its original form and transforming it later can help overcome rigid system limitations.

• Automation vs. Human Oversight: Automation is crucial for handling large volumes of data, but human intervention is necessary to identify and resolve potential data issues.

• Clear Communication: It’s essential to have clear communication and definitions, particularly when dealing with ambiguous terms like gross written premium or sum insured.