Business Analytics: Building a Smarter Game Plan
Wes Simonds 120000EFD6 email@example.com | | Tags:  netezza spss demand algorithmics on analytics business data warehousing information clarity patterns predictive pages bigfix analysis trend open management
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The following contribution is by guest blogger Wes Simonds. Over the next few months, Wes will share with you his perspective on the role of software in transforming business and building a smarter planet. Wes worked in IT for seven years before becoming a technology writer on topics including virtualization, cloud computing and service management. He lives in sunny Austin, Texas and believes Mexican food should always be served with queso.College football is my favorite sport, but for reasons unclear to me, it's not played every day of the year.
Analytics help you compete better in the business arena
So I find myself giving other sports a chance. This is often a learning experience.
For instance: the recently completed US Open tennis tournament. Did you know it has an Official Technology Partner?
I didn't. Neither did the sportscasters. I was surprised to hear that even jaded, seen-it-all John McEnroe was impressed with the detailed insight provided, which sounded much like this:
To beat Djokovic, Federer will need to land more than 62 percent of his first serves from the ad court, and in that scenario, Djokovic is least likely to return serves right down the middle.
To beat Federer, Djokovic will need to concentrate on Federer's relatively weak backhand, targeting it 43 percent or more of the time, especially in volleys at the net.
Wow, I thought. "This goes way beyond sabermetrics in baseball, which is mainly about individual players' relative strength. This is nothing less than a tailored, prioritized game plan driven by deep analysis of hard data. It tells players how well they're doing, where they're weak and strong, what kinds of risks are coming up, and what they need to do to achieve their goals. And it does that in as much or as little detail as they need.
Then I thought: What if businesses could leverage this kind of software to do much the same things?
Then I thought: They can. That's what business analytics is all about.
Don’t be an analytics have-not
Mychelle Mollot, IBM Vice President of Worldwide Marketing for Business Analytics, makes a very similar case.
"For our customers, analytics is really a tool to help them compete," Mollot said. "People have to discover new ways to differentiate, be competitive, and find new areas for growth. Many organizations are turning to IBM for analytics to help them make sense of their data in order to drive better business outcomes."
Think of data as a stockpile of valuable, but hidden, insights. Discovering those insights requires analytics tools capable of sifting through the stockpile and detecting trends and patterns. Then, based on the insights, business leaders can create strategies to help the business grow.
Practically every organization, in every industry, can benefit from quantified analysis of the available data. This is particularly true if little or no analysis along those lines is being performed right now.
"We see that not just in our own experience with our customers," said Mollot, "but the data [in general] shows it as well. In the studies we've done, [there is a clear] divide between the Analytic Haves and the Analytic Have-Nots.
"And the more that organizations fall behind in terms of their analytic usage and their analytic capabilities, the more their performance is going to be impeded by it."
Recognize trends and patterns faster and more accurately
As just one specific example, consider the business context of insurance providers. The entire insurance business is, at its heart, driven by statistical analysis -- an attempt to assess various forms of risk on a mass scale in order to provide financial protection for clients against undesirable events.
But going beyond that form of analysis, there is also the issue of claim evaluation. Insurance providers sometimes receive fraudulent claims; the faster and more accurately such claims can be identified and dealt with, the better the business outcome for the insurance provider. And that, in turn, will translate into value for policyholders in the form of lower premiums.
Detecting just which claims are fraudulent, though, is a complex matter. It's also an opportunity for analytics tools to shine.
Such was the recent experience of Infinity Property and Casualty Corporation, an Alabama-based automobile insurance provider that covers drivers identified as higher-than-normal risks. This organization provides 24x7 service, handling between 25,000 and 35,000 claims per month -- a vast data pool of ever-increasing size, and one in which a certain percentage of claims are going to be fraudulent. Being able to pinpoint those claims rapidly and correctly is thus a crucial aspect of Infinity's business model.
Thanks to a new set of analytics solutions and modeling techniques, the organization has managed to achieve exceptional results. Via sophisticated predictive models, claims can now be flagged as suspicious and referred to a special investigative unit in one to three days instead of a month. And they are now much more likely to involve actual fraud once they're investigated.
Furthermore, this approach pays a second dividend in the case of routine, legitimate claims. These can now typically be paid in one day, instead of a week or more.
The business result for Infinity? Twice the accuracy in identifying fraud and swifter claim processing in all cases, leading to a 403 percent return on investment.
That's an impressive result by anybody's standards.
Cases like that also illustrate just why leading IT providers are focusing more and more on analytics solutions: there is an increasing market demand for them.
"In terms of a growth strategy, IBM is investing in analytics, as we have seen from the recent acquisition announcements over the last year: SPSS, OpenPages, Clarity, Netezza, BigFix and now recently Algorithmics," said Mollot. "It is a core strategy for IBM because it's core to the success of our customers. We really believe that analytic-driven organizations are going to outperform those that are not analytic-driven."
Pursue analytics via a tailored strategy that reflects your specific context
If Mollot is right about that -- and I think she is -- then the question is not whether organizations need to deploy analytics tools, but how.
For best results, they should think through not just what their challenges and goals are, but also how to implement and integrate new analytics capabilities over time. The idea should not be just to buy and install analytics solutions, but also to drive positive change via an analytics strategy.
Some points to consider:
1. Find out how mature your analytics strategy is right now -- and what you should do next.
You can accomplish this by taking a quick analytics quotient (AQ) test. Given the results, determining the next logical steps should be much more straightforward.
2. Consider analytics capabilities from both tactical and strategic perspectives.
Often, a balanced approach is best. One way to go about that: think strategically (in terms of designing the system), but act tactically (in terms of creating pilot projects).
For instance, you might begin with analytics-driven direct marketing, but over time expand into much more specific analyses of customer data, such as the probability they will buy any given product or service.
3. Evolve your strategy and capabilities over time.
As your business changes, so will your data, your customers, and your strategies. You'll need to grow and refine your analytics capabilities in parallel.
In many cases, analytics can also help organizations understand change better, revealing not just new possibilities, but also false conclusions or unexpected gaps in their market awareness.
"Customers often find that the more they know, the more they realize they don't know," said Mollot. "That's what drives the next set of projects: the opportunity to learn more. Analytics is an ongoing process that [empowers] people at the point of impact with the ability to make decisions. So it becomes a cultural change as well as a technology and transformational journey."
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About the author
Guest blogger Wes Simonds worked in IT for seven years before becoming a technology writer on topics including virtualization, cloud computing and service management. He lives in sunny Austin, Texas and believes Mexican food should always be served with queso.