Insurance Technology Impact Awards: Components of a Successful Data and Analytics Project


Insurance Technology Impact Awards: Data and Analytics Winner PanelAite-Novarica Group’s eleventh annual Insurance Technology Impact Awards program reviewed 65 examples of insurer IT initiatives that delivered real business impact. On Thursday, July 28, I hosted the Insurance Technology Impact Awards Winner Panel: Data Initiatives, featuring three case studies of exceptional data and analytics projects that had business impact.

Panelists representing the Impact Award Winners for Data and Analytics were Mark Dash, Chief Technology Officer, Information Management and Technology at Penn Mutual; Laks Krishnamoorthy, Vice President, Enterprise Data Management at EMC Insurance Companies; and Abhijeet Kuray, CIO/CTO at Mosaic Insurance. Successful data and analytics projects used an iterative approach, had collaborative/multidisciplinary teams, and benefitted from modern data tools.

Using an Iterative Approach

All our panelists described the use of an iterative approach as instrumental to the success of their projects. One panelist described the process as a “careful, surgical approach with lots of retroactive verification studies.” When implementing new predictive models, for example, one panelist described their iterative process as “turning the knobs”–start the amount of data put through the model at a threshold, see if the model is behaving as expected, then turn the threshold dial up.

Collecting end-user feedback in each iteration was also a core part of the project rollout. Whether an iteration was six months or one week, panelists described meetings with product owners in the business to receive feedback and set priorities for the next iteration. One panelist stated that engagement from underwriters had become so proactive that they would reach out with feedback before the meeting was even set. Data is most useful when it’s providing insights that are applicable and actionable. Part of focusing that lens is successive iteration.

Collaborative/Multidisciplinary Teams

Iterative approaches lead to effective collaboration, and collaboration between business and IT was a major success factor in these data projects. Project teams sought participation from the business as end users, but also at other stages throughout the project. For example, one panelist described how subject matter experts within the business were identified to assist with data validation and with ensuring data quality during the conversion process.

These projects benefitted from collaboration among team members as well. One panelist described their use of a multidisciplinary project team: “We assembled a blended team, which was new for us–software engineers, data science engineers, an actuary, a medical director, and others.” This team composition allowed team members to learn from each other throughout the project.

Modern Data Tools

Panelists agreed that the success of their data and analytics projects hinged on the availability of modern data tools. One panelist stated, “The kind of processing we had to do was possible with the tech available today, but we would not have been able to do it five years ago.”

Another panelist pointed out that modern tools have become more widely accessible, even for smaller companies: “A large component of our success is due to how technology has evolved, which helped with data acquisition, and we could quickly integrate with multiple data sources with APIs. It has been much more available and affordable for a small size company to be able to quickly access this technology.”

For the full list of 2022 Data Initiatives Impact Award winners and the full case studies for the winners and applicants in this category, read Insurance Technology Case Study Compendium 2022: Data Initiatives. To learn more about our Insurance Technology Impact Award series, please reach out to me at [email protected].