That’s because the “sexiness” is a function of the competitive edge that data analytics gives to companies that master the discipline. And you can’t master it if it’s not an enterprise priority. The HBR and Buluswar agree about the nature of the challenge business face. Sure, it’s about the technology, but ultimately it’s about the creativity, as Murli noted to me this morning:
Analytics and data will be critical tools, but companies often struggle with a lack of creativity in how to apply them. And that’s where the rubber meets the road: not in the power of the tools themselves, but creativity in using them. The tools are not ends in themselves. The key is to start with the question.”
The HBR article — coauthored by DJ Patil and Tom Davenport of “Competing on Analytics" fame — gives the example of Jonathan Goldman, a Stanford physics PhD, who arrived at LinkedIn in 2006. After studying member profiles and being struck by the richness of their data, Goldman became fascinated with the possibilities they presented and started developing theories and strategies to grow the membership. However, the engineering team at LinkedIn found Goldman’s work an irrelevant distraction to the real work of scaling up the site. That chanced after Goldman’s insights proved enormously significant:
Through one such module, Goldman started to test what would happen if you presented users with names of people they hadn’t yet connected with but seemed likely to know…Within days it was obvious that something remarkable was taking place. The click-through rate on those ads was the highest ever seen….It didn’t take long for LinkedIn’s top managers to recognize a good idea and make it a standard feature. That’s when things really took off. “People You May Know” ads achieved a click-through rate 30% higher than the rate obtained by other prompts to visit more pages on the site. They generated millions of new page views. Thanks to this one feature, LinkedIn’s growth trajectory shifted significantly upward.
Similar coups will be occurring at insurance companies at an increasing rate, enabling those with the breakthroughs to eat their competitors’ lunch. The industry has already seen success stories great and small. In fact, the personal lines auto market — one of the largest in the industry —was reshaped by analytic insight. A modest company named Progressive became an industry titan through analytics-driven underwriting, leaving other companies scrambling to keep up. We’re bound to see other such industry transformations, along with smaller success stories, in the burgeoning age of Big Data.
The new chief science or data officer role will transform the competitive battlefield, particularly in the P&C industry, asserts Roger Burkhardt, President and CEO, EagleEye Analytics. "The battleground is expanding from personal lines into commercial lines," he notes. "And the focus for insurers has expanded and from a narrow base in pricing, to broad coverage of the full insurance lifecycle from acquisition, through retention, cross-selling and claims."
These new chiefs will focus on real business issues through identifying new ideas, rather than spending months on manual iterative analyses to get the answers, according to Burkhardt. "They are able to handle big data, including large and complex data sets, such as telematics," he adds. "They leverage cloud-based tools for maximum advantage including machine learning, text mining, and social media analytics."
[For more on Chartis' new data chief, see
Anthony O'Donnell has covered technology in the insurance industry since 2000, when he joined the editorial staff of Insurance & Technology. As an editor and reporter for I&T and the InformationWeek Financial Services of TechWeb he has written on all areas of information ... View Full Bio