3 challenges facing insurers in data science implementation

Register now

Insurers are already using big data tools like Hadoop and NoSQL in their enterprises, but further investment faces challenges, according to an executive brief from Novarica.

With many new data sources available, like Internet of Things devices and advanced weather and geospatial data, data leaders at insurance companies want to invest further. To do so, they must manage three key challenges, according to the "Data Science in Insurance: Expansion and Key Issues report:"

Getting business buy-in

Data projects can be tough to tie to specific, compelling return on investment, according to Novarica. 'Carriers should consider alternate metrics, such as watching the trends in KPIs where data science initiatives have been operationalized, to understand the value," say writers Nancy Casbarro and Deb Zawisca, both VPs of research and consulting "Like all forms of technical innovation, willingness to test and learn is needed. Carriers without the capacity for test-and-learn efforts will ultimately lose out on potentially impactful insights and business model changes."

Attracting talent

Insurance isn't the only industry looking for top-level talent. It's also not the best-regarded. But it does have a secret weapon: Volumes of data, including both historical and new sources. "It makes it easier to retain talent," Casbarro and Zawisca say. "When they are unencumbered by administrative and data cleansing tasks that take away from the more sophisticated data work they excel at and enjoy, data scientists are generally less likely to look for their next employment opportunity."

Strategic alignment

But it's not just a matter of bringing in talent to work with massive amounts of data. Insurers require business-side champions to get the most out of data initiatives. "Commitment to operationalizing these insights is the next step that can be lost in funding or prioritization battles," say the authors. "Even if data science initiatives are aligned with the business strategy, the only way to get real value is by operationalizing the results."

For reprint and licensing requests for this article, click here.
Data science Data Scientist Chief Data Officer Data warehouses