Sure ... data is big these days, but what are companies doing with all that newly-mined information? To find out, we asked three CIOs about how big data analytics pay off for them. A long time ago (or what seems like a long time ago), big data was big news. However, big isn’t the adjective business and technology leaders search for these days when they discuss data. Data isn’t a commodity to be stored for later, when you might need it. Data has become a science. Capturing and storing once unimaginable amounts of data is one thing — using it to predict behavior and outcomes is the fun part today. [ Related: CIO.com and Drexel University to honor 50 analytics innovators ] Big data isn’t a big deal if you don’t see a related payoff. To find out how IT leaders effectively quantify big data, we asked three member of the CIO Executive Council this question: SUBSCRIBE TO OUR NEWSLETTER From our editors straight to your inbox Get started by entering your email address below. Please enter a valid email address Subscribe What has been the biggest payoff or result you’ve seen from your big data and analytics efforts? The answers from tech leaders in financial services, insurance and healthcare are personal, predictive and persuasive. Carol Juel, executive vice president and CIO, Synchrony Financial. Carol Juel, executive vice president and CIO, Synchrony Financial “The latest big data efforts are an evolution of work we’ve done for years to improve our partners’ competitiveness. Technology has enabled us to work with larger and larger data sets at speeds that are ever faster. Our biggest impact is our ability to continually improve personalization of information for our merchants’ consumers as well as increase traffic for our merchants. Our big data efforts currently include many use cases across multiple functions from fraud to our call centers to marketing.” Guru Vasudeva, senior vice president and CTO, Nationwide. Guru Vasudeva, senior vice president and CTO, Nationwide “Near real-time predictive analytics have enabled Nationwide to optimize online sales and call center interactions. For example, we receive a high volume of online leads and have limited capacity within call centers for followup. Predictive analytics allow us to prioritize online leads, identify the most suitable prospects, and contact them within minutes to complete the sale. Jim Vertino, executive vice president and CIO, HealthPlan Services . “Predictive models, near real-time analytics, machine learning capabilities, and big data technologies are some of the ingredients that have enabled us to provide actionable analysis and insights. However, most critical to realizing results has been our ability to ’embed the analytics’ into the sales workflow and ensure immediate actions are taken based on the insights. In other words, ‘analysis,’ plus ‘insights,’ plus ‘action’ equals ‘results.'” Jim Vertino, executive vice president and CIO, HealthPlan Services “HealthPlan Services (HPS) is the county’s largest service provider for Obamacare enrollment and payment processing. With more that 25 percent of all Obamacare enrollments flowing through HPS systems and processes, the accumulation of data over the past three open enrollment periods has been tremendous. By analyzing this data and watching the new Affordable Care Act consumers’ behavior, HPS has been able to leverage its LoyaltyLink platform to predict consumer outcomes, [and] create and execute on strategies to effect those outcomes for the betterment of the total health care ecosystem we are all now living in. “HPS, on behalf of its health insurance company customers, has predictive models that score all 5 million members it administers each night for the follow business outcomes: customer loyalty; propensity to lapse in coverage; cross sell additional insurance products; likelihood of the healthcare consumer to pay for coverage (effectuate or bind coverage); and finally, models that predict possible fraudulent activities by consumers or insurance agents. These analytics efforts have helped HPS customers retain more members, reduce the cost of new members, and ultimately reduce the cost of coverage which is passed on to the end consumer. 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