With Big Data the buzzword of the day, insurers are struggling to make the transition from using analytics for point solutions scattered about their companies to developing centralized enterprise analytic capabilities. During the past year, we've seen the appointment of enterprise analytics officers with one title or another — such as Kimberly Holmes' appointment as XL's senior VP of strategic analytics and Murli Buluswar's role as chief science officer at AIG's P&C company, Chartis — showing understanding and commitment to analytic competition on the part of senior leadership. These companies competitors will need to reach the same understanding, but that's not all they'll need to do.
Buluswar's mandate at AIG/Chartis shows how seriously the company is taking the opportunity, as described in a recent article in the Wall Street Journal, "Hoping to Strike Profit Gold, AIG Ramps Up in Data Mining":
The chief science officer of AIG's property-casualty operation [Chartis], Murli Buluswar, this year has assembled an analytics team of about 100 economists, physicists, data scientists and other specialists—mostly outside hires who came to AIG this year—to handle the task.
The team is beginning to delve into reams of in-house data—and, just as important, an ever-growing pile of outside information—in search of new information about its products, customers and the atmosphere in which they operate.
"There's no lack of data around us, but there is a lack of clarity around what questions to ask and why that matters," Mr. Buluswar said. "With all this data that is being transmitted and created every day, the winners…will be firms that can understand that information and connect it to problems they are trying to solve."
Chartis' aggressive investment in a central analytics capability may be exemplary, but it also may be out of reach for many insurers, suggests Mark Gorman, CEO and founder of The Gorman Group, an analytics-focused consultancy based in St. Paul, Minn. "There are some organizations that have had the resources to invest broadly in functional and line-of-business strategic imperatives, and moving to an enterprise-wide approach is a very natural evolution," Gorman says. "It's more problematic for organizations that haven't had that experience because they don't see how all the pieces apply."
[For more on insurers groundbreaking application of analytics, see Insurers Hit the Underwriting Mark With Big Data .]
Many insurers see their competitive differentiation in terms of comparative operational efficiency, Gorman notes. They will need to recognize the physical limits of that strategy and prepare to compete on areas such as product innovation or customer experience, he advises. "Only when a company has made that shift does the capability to leverage information as a competitive asset emerge," he comments.
That intellectual shift appears to have happened at Chartis, Gorman opines, but so has a more basic sense of the historic moment in business competition. "Understanding about the potential of analytics to provide a growth opportunity has to surpass merely intellectual conviction," he says. "At Chartis they didn't sit down with the numbers and just do a cost/benefit analysis; they also agreed that the world is changing and they needed to do something about it — they understood it at a gut level, and not everyone has crossed that barrier.
Nor is such a conviction a sign of arriving, but rather only a sign of beginning the journey to analytic competition, stresses John Lucker, a Hartford-based principal in Deloitte's global advanced analytics practice. Acknowledging the importance of analytics by appointing a lead corporate evangelist/executive is only the first step toward creating a more data centric, metric-oriented and fact-based decisioning corporate culture, he says.
To establish analytics as a strategic priority, companies must successfully execute on what Lucker calls six harmonized primary components:
1. Analytic strategy.
2. Analytic results that are either or both pragmatic and innovative.
3. Business and operational implementation of analytic solutions.
4. Technology integration of analytic solutions.
5. Organizational change management to embrace analytic solutions.
6. Performance management and measurement of analytic solutions.
"All of these project components must be harmonized to achieve end-to-end success in any analytic journey and then be supported and held accountable by executives at the very top of a company," Lucker insists.
The synchronization of these steps in execution are hard enough but they are often confounded by the inertia of many corporate cultures, the cognitive biases of key executives and their subordinates, and other behavioral economic factors, Lucker adds.
"Many find that the 'soft stuff; in analytic projects are often what makes success a reality," Lucker notes. "So a watchful eye, accountability to achieve predetermined metrics, and very careful project leadership on the six previously mentioned focus areas are imperatives for any company seeking to seriously make analytics a strategic approach and competitive differentiator."