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).
Find Your Place on the Data Science Innovation Curve
Today, insurers that are using data science to drive specific processes are realizing excellent results, but for most, it hasn’t begun to deliver its full value across the enterprise. CCC finds that as you look at the value of data² and combine the value of big data with value of small data you can disrupt your products, your processes and even your business model. Pinpoint where you are on the Data Science Innovation Curve below; the more you can leverage deep historical and predictive data and apply it in an individual way in real time with the customer, the more disruptive you can be.
Keys to Claiming Your Data
To be sure, change is required in order to embrace and benefit from data science. This change doesn’t need to be overly costly or cause you to entirely reinvent your business, but it will need to include three key components to be successful.
To get the full value of data science, there has to be a strategic alignment across the business. Field offices, regions, corporate offices, and partners have to be on the same page. That’s easier said than done. As you implement new processes driven by data science, your people will have to learn to do things in new ways and to operate under new standards. It’s not compliance you’re looking for though. You want more; you want commitment. You want an engaged team asking the creative questions that improve processes and accelerate growth.
While the debate about artificial intelligence is ongoing at the highest levels of philosophy and science, we find that in most cases it is the combination of the human and the machine that prevails, not one alone. Still when scale and speed are paramount, a fully automated solution could be in the future.
You have to dedicate resources and money to this—data science is not a second-level priority, using shared and borrowed resources. You need to dedicate an infrastructure on premise or in the Cloud. Data Scientist is not a part-time job: it’s a strategic role in your organization. Data Scientists understand your business, your workflows and your goals, and then uncover ways to improve them through data analysis, actionable insights and prescriptive recommendations. The demand today for Data Scientists is skyrocketing. Leading researchers, including McKinsey Global Institute, predict that by 2018 there will be more than twice as many openings for data scientists than available talent.³
Executive support is a must-have. The trick here is not to try and do too much at once—to stay focused and above all patient. Think of which specific processes in your business will show the quickest and most impressive results. A quick win for example could be a situation where you already have a tool to select one of many alternative processes. Making that tool “more intelligent” allows data science to shine without the cost of developing systems to deliver the model to the user, since you would merely be swapping out the decision engine.
Moving First or Moving Last Decides Winners
If investment in a dedicated team is too much too soon, or your data isn’t broad or deep enough or its buried deep in legacy systems, then working with an established provider can work. But getting started now is critical as the history of business is filled with leaders who didn’t recognize and take advantage of disruptive technology. For example:
- The President of Western Union viewed the telephone as a toy and failed to invest in it.
- Newspapers around the country were shuttered because they were too slow to adopt the web.
- In 2000, Blockbuster rejected an offer to buy Netflix for $50 million (Netflix revenue in 2013 was nearly $4.5B).
- Taxi companies were blindsided by the impact of companies like Uber.
Hindsight is always 20/20, but you get the point that resting on your laurels can quickly turn on you.
The best way to avoid being a late mover on the data front is to act now. Carve out a small team that is protected from day-to-day operations, such as from urgent fires that distract from achieving long term strategies. Turn that team’s focus to finding predictive models with optimal use cases; important, practical projects that have a high probability of success, and that align with a larger company strategy. But keep the team on its toes, as your analysts uncover value in your data, they’ll likely find new directions to take. You’ve got to be nimble so you can continue to move quickly. Speed doesn’t mean haste, don’t be impatient, stay focused on the use-case processes where you’ll generate the best results.
We’ve seen this measured approach enable some early data wins, while setting the foundation for future, broad applications of data science. For example, one claim executive used our predictive MOI product, which recommends routing totaled vehicles for salvage and repairable vehicles to the appropriate appraisal source. Because CCC built a model and developed a use case that deployed the science in real time in use with the carrier’s live interaction with the consumer, the carrier was able to realize more than 3 percent of LAE savings. The culture and executive support that enabled this success did not happen overnight. Yet results grew exponentially. Success builds on success; adoption barriers get smaller; and support is reinforced.
If you want to dig deeper into your own data or learn more about CCC’s solutions, please contact John Haller at email@example.com.
³ McKinsey Global Institute. “Big Data: the Next Frontier for Innovation, Competition and Productivity.” 2011