Data Access, Skills, and Sponsorship Top Concerns at Analytics Special Interest Group Meeting

As organizations grow to be more data-driven than ever, managing the data and analytics models used to make business decisions has become a top focus for carriers. At the first Analytics Special Interest Group meeting of the year, Novarica VP Eric Weisburg and I were joined by Louis DiModugno of The Hartford Steam Boiler and Allen Thompson of Hanover Insurance Group to lead a panel discussion that touched on top challenges carriers are facing with their analytics efforts, along with building business sponsorship around analytics models.

In a live poll, event participants rated their top three challenges in their analytical efforts as: data access (33%), skills (26%), and sponsorship (18%). Our panelists detailed their challenges as well, with one stating that 50-80% of the time building an analytics model has been focused on data cleaning. One opportunity that a participant brought up was the double-edged sword of using pre-fill data not only as a tool for customer satisfaction, but from a data mastering standpoint to help with data quality and accuracy.

Contributing to the challenge of data access is the lack of appropriate skills. Not having the right skill set to handle data models can lead to self-sabotaging situations; one participant stated that they experienced difficulty due to not having people with the right skills handle the data, therefore complicating models further. While insurers may look externally to fill the skills gap by working with third-party vendors, some participants cautioned against working with vendors who would run a black box operation with carrier data and stressed the importance of finding vendors who are willing to work cooperatively and let carriers view their algorithm.

Some stressed the importance of sponsorship, stating that “with sponsorship comes investment, which comes with better access to data, the ability to hire in needed skills, and to have the leadership of that sponsorship lead in the adoption of data models.” Indeed, carriers have found success in gaining adoption and success through having the business teams drive the establishment of the data organization.

Some carriers have worked with other business teams to build the hypothesis, work with analytics teams to build out the model, and reflect the findings within a confidence interval. Our panelists agreed that “a lot of it is making sure we bring them for the ride—making sure they’re part of the hypothesis, measurements, and educating those who will be using [the models]” to bring trust to both the data and the model. If business teams do not trust the models, they may run the models but not tie their decisioning to them or evaluate results retrospectively.

Novarica has an array of Special Interest Group sessions planned for 2021. Please register now to join the pre-event dialogue. Details and registration for our Special Interest Group meetings can be found here.

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