Big data is transforming the insurance industry. Staggering amounts of data from inventive new sources are flowing into organizations’ systems, and data scientists are creating the models that will reshape industry best practices across the board — from claims to customer service.
There’s just one problem: data scientists don’t necessarily know much about insurance.
Harvesting and interpreting data from telematics, wearables and text mining only creates value if it helps insurance professionals do their jobs. Frontline insurance employees are integral to helping data scientists determine which information deserves their attention and what insights will have the biggest impact on the bottom line.
There’s just one problem there, too: insurance professionals don’t necessarily know much about data analytics.
This separation will only become more apparent and limiting as data-driven decision-making pervades more and more business operations. Many insurers, especially in the property-casualty and life insurance sectors, are moving quickly in their journey to using analytics to drive organizational decisions, according to a McKinsey report.
Within companies transitioning to analytics-driven organizations, there are significant opportunities for frontline insurance professionals who can develop their data analytics knowledge and bring data-driven decision-making to their work and effectively collaborate with data scientists.
Nationwide, there’s certainly a high demand for these connectors. McKinsey also estimates that by 2018, U.S. industries will need an additional 1.5 million managers and analysts with an understanding of how big data can be applied to their fields.
So how can frontline insurance employees become data-driven decision-makers? Start with this three-step approach.
1. Focus on data literacy
Developing a data-driven mindset has to start with wrapping your head around the basic concepts of big data. You should be able to articulate the difference between structured and unstructured data and supervised and unsupervised learning. You don’t necessarily need to know how to develop a linear regression model, but you should be familiar with the concept and how it’s used.
It’s important to note that big data analytics is a relatively new and growing discipline. Industry practitioners and academics may use different terms to describe the same principles, and the field is littered with acronyms, slang and jargon. What one organization may call data mining may be known as knowledge discovery somewhere else. It’s important to make sure your learning is in line with how your organization talks about data analytics.
2. Sharpen your data mindset
After you’ve developed the foundational knowledge around data science specific to your organization’s lingo, you can start thinking about problems and operations in terms of data. That means championing your company’s current efforts to bring data analytics to traditional insurance practices.
Don’t just learn the basics of the new systems. Strive to understand the data science behind them, and how they impact operations and company revenue.
As you master your organization’s new data-driven procedures, start thinking of additional ways data can solve existing problems or improve efficiency. Can available data be analyzed differently to create new insights? Should different information be collected from customers, agents, adjusters or other professionals?
3. Hone your data skill set
The third step in becoming a data-driven decision-maker comes down to using your knowledge and perspective to help your entire organization think in terms of big data. You should be as comfortable talking about machine learning with a data scientist as you are talking about claims triage or expense ratios with fellow insurance professionals.
With an understanding of how data analytics works and where it will take the insurance industry, you’re poised to become an advocate for the specific improvements that will differentiate your organization. You’ll be able to join the cross-department teams responsible for integrating data principles into traditional insurance functions and company operations.
Getting a grasp on big data in the insurance field is a crucial first step toward becoming a data-driven decision-maker. But the benefits of that new understanding won’t be fully realized until you can translate your knowledge to organizational improvements that benefit your company’s bottom line.
And the clock is ticking. While there’s currently a shortage on managers and frontline employees with an eye toward data, that won’t last forever. The benefits to organizations, and individuals who develop data expertise, will truly be transformative.
