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Our world has become increasingly connected, with more and more data becoming available every day. To keep up, insurance carriers need to make sure that they’re basing their business decisions on high quality data—which often requires having both an analytics partner to help sift through the noise and provide valuable insights, and a well-built internal team. Meanwhile, the insurance industry continues to evolve and become more competitive, and customers expect data-driven solutions. Because of this demand, insurers are now investing heavily in their own in-house analytics capabilities as well as data scientists with computing capabilities. The demand for analytically derived products will only increase globally, which is why carriers need to make the investment in data scientists today.
The data scientist role is evolving to meet these needs. The term data scientist has been around for decades, but over the past 5 years, its usage had exploded. Any individual analyzing data wants to be called a data scientist. As the demand and pay has risen, so has the use of data scientist titles.
Contrary to popular belief, a data scientist is not an “Actuary 2.0.” The actuarial field is very specialized to focus on solving insurance problems, both analytical and non-analytical. In general, actuaries are trained to determine the profitability and appropriate pricing of insurance risks. And while the role calls for using statistics to solve problems, the data has generally been very limited and structured. Furthermore, their analytical approaches have been well-defined and heavily regulated within their space.
Defining the Role
The true definition of a data scientist is someone who has the ability to solve a problem using data and a number of different approaches. A data scientist leverages structured and unstructured data. They are highly dependent on today’s computing capabilities to find signals in the vast data stores. This doesn’t mean data scientists don’t use traditional statistics, but it’s just one tool in their toolbox.
Every industry that leverages analytics is competing for data scientists. In fact, Glassdoor’s annual list of the 50 best jobs in America has ranked being a data scientist as one of the top jobs for four years running. It’s no wonder that the demand for data scientists has gotten tight and is getting tighter every year. Colleges are expanding the number of programs across the globe, but there will likely be a supply shortage for a long time.
Why is There a Lack of Data Scientists?
Almost every industry has recognized the valuable insights leveraged from data their business captures or creates. The most successful companies have been those able to find competitive advantages leveraging this data both internally and externally; therefore, the demand for these skills has only increased over the past 10 years. Computing capabilities and cloud access have grown exponentially, so the ability to process this ever-increasing data has made the investment in your in-house data science team a viable solution at scale.
Data Scientists and IoT
Today, IoT is providing more data to carriers than ever. But how can the insurance industry leverage this data? One of the biggest challenges with IoT data is the growing number of device types and the shortened lifecycle of technology products. Keeping up with new devices and standardizing the data is a constant challenge. Carriers need to partner with data and analytics companies who can utilize data scientists and manage the ever-changing landscape of IoT devices into one consistent outcome, but they also need to build their internal teams to manage this data. It becomes a significant challenge when this data capability is not an insurance company’s core competency.
The adoption of more data and analytics is the competitive advantage carriers are focused on today. Carriers want more attributes for their data scientists to evaluate. They want the ability to test data faster and with larger files so they can make quicker decisions.
Consumers benefit greatly when carriers use data driven solutions with IoT devices, like decreased claim frequency and severity. As costs declined and data processing capabilities increased, it is possible to basically place a sensor to most processes in a house or vehicle where insurance claims could originate. This means there is a wave of data waiting to be discovered, and carriers should build data science teams now in preparation.
I believe that the roles of the actuary and the data scientist will merge into one over time. Actuaries understand how insurance companies operate and which profitability triggers should be pulled. Adding data science skills, like computing capabilities, into this already highly analytical team will expand their value even more to the insurance industry. Actuaries are already starting to incorporate data science approaches to their already broad capabilities. Some companies may choose to have separate organizations, but the right mix of the two areas will become a competitive advantage.
I’ve also noticed an increasing demand for cloud support. As carriers move their own systems to the cloud, they need data and analytics delivered seamlessly. They’re always looking for ways to save expenses while minimizing impact to their risk exposure. They are looking at ways to swap out expensive data models they can feel confident will perform well over the long term.
It’s an exciting time for data scientists—more data than ever before is being collected and modeled. Data-driven products allow insurance companies to streamline their existing processes and are meant to complement their existing workflow. Human judgment and data expertise will always be required to in a successful business strategy. With smart homes and IoT devices gaining popularity, carriers should prepare for an explosion of new data to work with by building their data science team internally and partnering with an analytics team with deep knowledge and strong capabilities in the space.