At XL Group, a global insurance and reinsurance company, analytics is as much a part of the business as underwriting and risk management.
Kimberly Holmes, XL Group
In fact, analytics has been part of the company from the get-go, Kimberly Holmes, head of strategic analytics at XL, told me in a recent interview. The company, headquartered in Dublin, Ireland, launched in the mid 1980s with a new model for risk transfer, addressing a "big problem and disruption in the commercial casualty insurance space with a solution that has spanned the test of time."
Now, with the insurance industry at a crossroads, XL stands at the ready with an arsenal of advanced analytics tools. They're the result of work Holmes has overseen since joining the company two years ago, charged, she said, with the mandate of improving decision making at XL via the development of advanced analytical decision support tools.
"We've seen a major shift in the risk paradigm over the last few years: Risk is growing exponentially, and there are big changes in the information available, how customers operate, and technology. XL is responding by embracing advanced analytics."
Holmes' strategic analytics group has developed 12 multivariate predictive models as well as nontraditional analytics in areas where it doesn't have enough data to support a traditional multivariate predictive model solution, she described. Next up is enabling sophisticated and interactive data visualizations via SAS Visual Analytics.
Introduced last March, Visual Analytics is a next-generation analytics solution comprising an administrative tool for managing users, security, and data; an exploration tool for ad hoc data discovery and visualization; design capability for standard and advanced reporting and dashboards; and Mobile BI, for rendering the data visualizations natively on mobile platforms. These come together in what SAS calls "The Hub," which provides role-based, secure access for IT, business, and analytics professionals. Behind the scenes, Visual Analytics runs on the highly scalable SAS LASR Analytic Server, an in-memory analytics engine that uses Hadoop as local storage for fault tolerance. (See SAS Visual Analytics Provides Wow Factor for more details.)
When we talked last week, XL and SAS (this site's sponsor) were in the final stages of working out a hosting arrangement for Visual Analytics, Holmes said. Holmes said she decided using Visual Analytics in a hosted environment would be best given the hardware requirements and the expertise required to maintain the hardware and software.
"My hope is that we'll be up and running with it any day now. I really can't wait to get my hands on it," she said.
Holmes' expectations for how Visual Analytics will benefit XL are high. "Most people don't have mastery or even rudimentary understanding of statistics. Through the use of Visual Analytics, we can make the story in the data come to life before our eyes."
In its traditional management information environment, the data analytics can tell XL what happened but not the why. It can't go deep enough into the datasets to give the company the whole story, she explained:
With Visual Analytics, we're not going to be getting the abridged version, the CliffsNotes. We're going to be getting the whole story, and it's a big story, telling us the why. That's the most important thing. Knowing what happened is important but if you don't know why things happened, you don't know what to do to make things better going forward.
The benefits of Visual Analytics "will go straight to the bottom line," Holmes said.
Decision-making effectiveness determines success at XL, as it does at most other companies, Holmes noted. So if it can improve its decisions, it'll improve the bottom line. "How we improve our decisions is through more information and better analysis."
But implementing that is harder than developing it, added Holmes, noting the difficulties in getting people to change their behavior. Visual Analytics will help XL get to change management tenet: Speak to the heart, she said. "And it will help us ask new questions that we didn't ask before. We would not get the insights from the data if we did not have these questions. This will improve how we analyze risk and how we implement the findings and the tools we develop."
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