The term “data science” is only about 15 years old. It first showed up in a
College graduates are flocking to the job openings, but there’s still a gap. United States businesses will be short 1 million data scientists by 2018, and 40 percent of companies are already struggling to find top data analytics talent, according to a recent
Recruiting top data-science talent will continue to be challenging and expensive. But building a team of superstar data scientists is only one part of transitioning to a data-driven organization. Truly infusing an insurance organization with data-driven decision-making cannot start with hiring external employees and building stand-alone departments. It must start with frontline employees. While these individuals will never be the data scientists, mining the depths of systems and the Internet of Things for trends, they are the people with the insurance know-how to identify the problems big data —and data scientists—can solve. And they’re the ones who have to adopt and accept new data-driven processes and insights.
Data at the organizational level
Many companies are already making great progress on the path toward organizational data analytics, and they’re seeing significant benefits. Bain & Company performed
- Twice as likely to be in the top quartile of financial performance within their industries
- Five times as likely to make decisions “much faster” than market peers
- Three times as likely to execute decisions as intended
- Twice as likely to use data very frequently when making decisions
Data analytics is not just a bottom-line booster. Organizations that make more decisions based on data react to market shifts more quickly and execute change more efficiently. This is especially important in the insurance industry, where organizations’ proprietary competitive advantages are what set them apart and allow them to adapt and exceed customer expectations.
The agility and effective decision making that come with big data are vital to continually improving business operations and products. For example, data-driven decision-making will not only keep your claims process competitive through advanced data mining capabilities, but also it will define the future products and processes your company needs to be successful.
Data-driven changes start with frontline employees
Getting employees in claims, underwriting, customer service and other traditional insurance roles on board with new big-data initiatives is crucial. The first step in bringing these employees up to speed begins with identifying the analytical techniques and strategies your organization prioritizes. It is important to train frontline employees on these processes and make them as specific to your company’s operations as possible. When done right, this knowledge transfer not only teaches employees how to use new analytics-based techniques, but also it gets them thinking about all operations in terms of data, from identifying new risk parameters to setting optimal times for staff meetings.
After all, securing frontline buy-in for big data is as much about good old-fashioned employee motivation as it is about education. You can have the most advanced, streamlined analytical tools in the business, but if employees still favor doing things the old way, those tools won’t do much good. Employees need to be motivated to learn the new systems, help data scientists recognize what aspects of their job could benefit from big data, and adopt new processes. It won’t always be an easy transition. But the more data advocates you have, the more easily new data-driven techniques will be accepted and executed.
