Our benchmark research on business analytics finds that just 13 percent of companies overall and 11 percent of finance departments use predictive analytics. I think advanced analytics – especially predictive analytics – should play a larger role in managing organizations. Making it easier to create and consume advanced analytics would help organizations broaden their integration in business planning and execution. This was one of the points that SPSS, an IBM subsidiary that provides analytics, addressed at IBM’s recent analyst summit.
Predictive analytics are especially useful for anticipating trend divergences or spotting them earlier than one might otherwise. For example, sales may be up compared to a prior period, but is it simply month-to-month variability or the start of an upward trend? Better analytical techniques can help distinguish between normal variation and the beginning of a new trend. By using analytics, one might even discern that while the revenue numbers have been positive recently, the underlying data contains warning signs that point to diminishing volumes, lower prices or both in the future.
Predictive analytic models are created using a top-down or a bottom-up approach, or some combination of the two. SPSS offers tools to handle both. The top-down approach involves creating a statistical hypothesis based on business observations or theories and then testing that hypothesis using statistical methods. IBM SPSS Statistics enables users to build a relevant picture from a sample, as well as test assumptions and hypotheses about that picture. A bottom-up approach unleashes automated data mining techniques on data sets (typically large ones) to distill statistically significant relationships from them. SPSS Modeler is designed for use by experienced data miners but also business analysts to speed the creation and refinement of predictive models. Often, companies employ both approaches iteratively to refine and improve models.
I used to joke that the main value proposition of SPSS was that while its chief rival, SAS, required its users to have a Ph.D. in statistics, SPSS could be used by anyone with a master’s degree. Applying predictive analytics techniques is simple in concept but far from simple to integrate into day-to-day business beyond its traditional roles such as market research. This partly explains why so few companies have woven predictive analytics into their planning and review cycles. It’s possible to create relatively simple predictive models, but for many business issues, such models may be too simplistic to be useful. And they may not be reliable enough because they generate too many false positives (people spend too much time chasing non-issues) or false negatives (missing important developments or breaks in trends).
Beyond the data and technology challenges posed by advanced analytics, there are
significant people issues that companies must address to make their use practical. These can be more difficult to tackle than most business/IT issues because of the experience and skills that are needed that our benchmark in predictive analytics still finds lack of adequate resources. Automating general business processes, for instance, requires bringing together business subject-matter experts with people who understand IT. Advanced analytics, however, requires three sets of skills – business subject-matter expertise, IT and statistics – that are rarely found in any single individual. Communication among sets of individuals who have these skills often is difficult because they have a limited appreciation of the others’ domains and often have difficulty expressing the nuances of their own area of expertise.
Today there’s greater focus than ever on analytics, partly because an explosion of available data has made it possible and even necessary to make sense of it. As part of IBM, SPSS has been benefitting from the parent’s “smarter planet” marketing theme. SPSS also has taken steps to expand demand for its tools by reducing the people barriers to adopting advanced analytics. One step has been to automate data preparation for use in Statistics and Modeler. Another is an automated modeler that takes several different approaches to analyze a set of data in a single run and then compares the results. Yet despite these steps, I expect advanced analytics to require specialized skills for many years.
Therefore, I also expect adoption of advanced analytics to happen slowly. Most executives at the senior and even middle levels of corporations have limited familiarity with advanced analytics. Many may have had their last formal education with statistics as a required business school course. To spur broader adoption of predictive and other advanced analytics, IBM and others must foster a “pull” approach to marketing analytics. Business executives need to know that advanced analytics are available and of practical value, especially outside of traditional statistics-heavy realms such as consumer research and fraud detection. Sales planning, financial planning, enterprise risk management, maintenance and customer service are all areas ripe for use of predictive and other advanced analytics. We found all of these as future use of predictive analytics in our benchmark. It’s easy to convince analysts like me of the value of analytics; it’s much harder to get business executives to incorporate them into day-to-day practices. It would be helpful for its own cause if IBM SPSS were to identify promising uses of advanced analytics by function and industry and provide a canned blueprint that can serve as a starting point. Such a blueprint would incorporate a business case illustrating the problem, the suggested steps for addressing it and the scope of benefits that can be realized.
The continuing explosion of data will give rise to an increasing number of ways that business and finance executives can use information to their advantage. But first they have to know that they can.
Regards,
Robert Kugel – SVP Research
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