The Road to Becoming Analytics-Driven

I recently had the opportunity to moderate a panel on becoming an analytics-driven business at the InsurTech Hartford Symposium. The session provided some illuminating insights on what insurers and startups should be thinking about when using data to make business decisions. Some of the highlights of the conversation were:

Lessons Learned in Data Initiatives

Building data models often isn’t the toughest part of a data initiative: it’s how an organization decides to use that data. End goal matters when it comes to building or modifying a database. Technology-driven objective like building a data warehouse or data-driven objectives like creating a “single source of the truth” are unlikely to provide real value without an understanding of business need. Building out a data strategy to align with a specific use case or business objective is how an insurer gets to real value.

What Makes Good Data and Data Processes

One panelist pointed out that data should function like a smartphone: incredibly complex, but able to be used by anybody. Organizations are not just looking for data to make decisions for them, they’re also looking for detail and reasoning behind the decision. It’s difficult for an individual person to blindly follow a decision from a black box, and it’s difficult for an organization to make changes without understanding why. Advanced analytics and machine learning technologies are maturing to the point where they not only provide answers but also “show their work,” laying out the relevant variables that influenced an outcome.

The Future of Data in Insurance

Data will have a role to play in hyper-personalizing insurance. As one panelist noted, “if you don’t hyper-personalize, you don’t exist.” Single source of truth is unlikely to be the ideal path to knowing more about consumers but combining data from different sources could add value. To this end, the future is in “buffed up data” and related technologies to enhance analytics such as AI and machine learning. Yet there can be a difference between what an organization thinks it should do and what the data is telling it to do, and without mature data analytics that have been tracked for results over time it’s important to keep humans in the process. As Novarica likes to say: cyborgs are better than robots, and it is unlikely that decisioning will cut out a human being and be purely automated through AI or advanced analytics.

Novarica’s recent reports on data and analytics provide more information on trends, challenges, and solution providers. And free to reach out to me at [email protected] if you’re interested in learning about how Novarica helps insurers with data strategy.

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