Interpreting Insurance Data Language


As a member of the data team at Accelerant, my typical day often involves speaking multiple languages. While I am working on my Spanish skills (hopefully for a future trip to Spain!), the languages I’m focused on are those of data within insurance, and the barriers they create can severely limit a carrier’s ability to support innovation and add value to its customers. That’s why we at Accelerant spend so much time here: translating between these languages ultimately allows us to better support and create value for our Members.

Within insurance, there are often several ways of referring to the same piece of information. A policyholder’s occupation could be referred to as their trade, industry, line of business – the list goes on. Each of these pieces of information, which are often captured in columns of data and referred to as a field, has some nuance that makes it unique from the others. Using the examples above, a trade could refer to an industry that is also not a line of business. This creates several different “languages” of data, which need to then be translated as our Members work with their customers and we work with our Members.

For professionals with industry experience, translating between different data languages can be semi-straightforward based on experience and exposure, and the nuance between these languages can be maintained. However, if you are newer to the industry or, more importantly, a specific company, the nuance can be lost, and different data languages become incorrectly conflated. This can result in meaningful negative impacts on productivity and success. Legacy technology greatly increases the chances of incorrectly conflating different data languages, resulting in poor insights and lost value for all parties. At Accelerant, our focus on data governance prevents these issues, allowing us to provide strong insights and added value for our Members and risk capital partners.

Across the insurance value chain, lost nuance between data languages can provide serious obstacles. When aggregating data from multiple counterparties, carriers without data governance practices can conflate data that belongs in different fields into the same field. This renders the data dangerously useless, as these problems compound when additional technology and analytics are overlayed. If a single field contains misinformation, any analytics generated from that information are likely wrong, reducing any value the carrier could hope to add to their customers through advanced analytics or other technology.

While a fresh data governance mindset is essential in addressing these problems, legacy technology often prevents proper data governance. Most legacy technology has no way of tracking the difference between different fields, resulting in the same field conflation problem described above but on a much larger scale (with hundreds of fields). Since legacy systems don’t support effective data governance, carriers with these systems are forced to either conflate field values (as described previously) or silo their data by counterparty. The former causes inaccurate predictions that harm customer success, while the latter prevents the carrier from leveraging their benefits of scale. As a result, even carriers with the correct data governance strategies can still fail to add as much value as a governance-minded carrier with the right technology.

At Accelerant, we combine a data governance mindset with the right technology to provide industry-leading value to our Members and risk capital partners. As an example, one of my projects this summer involved transitioning our referral tracking system to a modern solution that utilized data governance practices like item standardization, field validation, and more. The whole Accelerant team worked seamlessly across underwriting, actuarial, technology, and member relations to ensure our field definitions were consistent universally and that no fields were conflated. The new tracking system will be a great success for both our Members and us, and can realistically improve referral efficiency by 50%+.

The development of this referral tracking system is an excellent microcosm of data governance at Accelerant. We place a heavy emphasis on data standards and governance from the top-down and bottom-up to manage each piece of data. This allows us to effectively translate between different data languages without any loss of nuance and optimize our data intelligence.

Combining our high-quality and high-fidelity data with our InSightFull analytics platform allows us to leverage insights across Members to help grow and improve their businesses. Our dedication to data governance, combined with our strong analytics tools, enables us to provide unsurpassed value to our Members now and for years to come.

Spencer Hylen

Data Analyst Intern