Data Science is disrupting business—as surely as electricity, telephony, connectivity and mobility disrupted it. That's a statement insurers find in most every trade publication they read. It's often much closer to home: many have to look no farther than their own organization to see the role data science plays in growth and profitability. Marketing and underwriting, for instance, habitually analyze data to target advertising, price policies, assess risks and more.

It's not universally adopted though. Many claims organizations in particular have yet to take full advantage of the value of all the data they routinely generate and store each and every day. Depending on the scope and reach of your data network, that can include:

• Customer data.
• Performance data.
• Historical data.
• Competitive data.
• Industry data.
• Partner data.

Too often, this data is left untouched: the rich knowledge it contains never harvested and analyzed.  There are a number of reasons for this. There's the investment involved, first in equipment (or Cloud services) and the operational costs to manage it. The second, and more significant, investment is in people: the data scientists and senior business analysts that uncover and extract value from data. There's often a cultural resistance—doing the status quo seems like the most attractive alternative to disruptive change. And that resistance might reach all the way up to fundamental corporate philosophy—which limits executive support for the effort. The tendency to place external, urgent goals over long-term business goals can halt data analysis initiatives in their tracks. Fighting fires isn't what data science does; data science prevents them. Data informs business. Data science transforms it.

Still, no matter the reason, many if not most claims executives are at the starting gate when it comes to getting the most out of their data. But now is definitely the time to get data science going if you're interested in realizing some of the potential benefits, including:

• Improving the accuracy of initial appraisals.
• Reducing time and money wasted in the claims processing.
• Establishing industry-wide benchmarks for assessing individual performance.
• Creating predictive and prescriptive frameworks to improve decision making.
• Opening the door to improved customer satisfaction and increased customer loyalty.
• Enabling "small data" [1] (how to humanize data and respond to the individual).