Here are several excerpts from an article by Stephen Swoyer, published in Enterprise Systems, Predictive Analytics: Slow Adoption Despite Big Benefits:
Few technologies provide as much ROI bang for the buck as predictive analytics. Despite its impressive benefits, only 21 percent of respondents to a recent TDWI survey had fully or partially implemented predictive analytic solutions in their organizations, while another 19 percent were in the process of developing them.
Why does adoption continue to lag?…
Predictive analytics is a deceptively simple way of describing a set of business intelligence (BI) technologies that help uncover relationships and patterns within large volumes of data. That’s the first part of the predictive analytic value proposition.The second part (and probably the most important part from the perspective of business decision-makers) is that predictive analytic tools uncover actionable insights. The relationships they discover can, in turn, predict behaviors or events. It’s in this respect that TDWI Research contrasts the forward-looking view afforded by predictive analytic solutions with the historical perspective (that of a “rearview mirror,” according to many predictive analytic vendors) afforded by BI tools, which are typically employed less as predictive and more as deductive technologies.
“Other BI technologies—such as query and reporting tools, OLAP tools, dashboards, and scorecards—examine what happened in the past,” writes Wayne Eckerson, director of TDWI Research, in a recent report.
These tools are deductive, Eckerson argues, because “business users must have some sense of the patterns and relationships that exist within the data based on their personal experience.”
Rearview mirrors or no, conventional BI tools enjoy much greater adoption than their forward-looking counterparts, at least right now.
The good news is that 44 percent of TDWI’s survey respondents were still exploring their options with respect to predictive analytic tools. That means about 84 percent of respondents were implementing, investigating, or at least nominally open to predictive analytic deployments; just 16 percent had no plans to deploy the technology at all. That’s still a puzzling adoption rate for a technology that—if case studies, ROI success stories, and marketing anecdotes are correct—has an enviable track record.
“Predictive analytics can yield a substantial ROI. Predictive analytics can help companies optimize existing processes, better understand customer behavior, identify unexpected opportunities, and anticipate problems before they happen,” Eckerson writes. For six years running, he points out, a majority of TDWI’s annual Leadership Award winners have used predictive analytic solutions to achieve noteworthy business results.
Which begs a particularly insistent question: Why is a high-value technology like predictive analytics so paradoxically under-represented in the enterprise?
For a number of reasons, thought leaders say, starting with the technology’s esoteric roots in statistical analysis.
“Predictive analytics is also an arcane set of techniques and technologies that bewilder many business and IT managers,” Eckerson points out. “It stirs together statistics, advanced mathematics, and artificial intelligence and adds a heavy dose of data management to create a potent brew that many would rather not drink!”
Along with rival SAS, SPSS controls a sizeable slice of the analytics market and a disproportionate share—given its comparatively small BI market footprint—of the predictive analytics space. According to market watcher International Data Corp. (IDC), SPSS derives almost 90 percent of its revenues from sales of advanced analytics software, for good reason: both SPSS and SAS started out as developers of statistical analysis software; both remain highly respected analytics vendors to this day.
According to TDWI’s Eckerson, one of the principal concerns of business managers and other decision-makers isn’t so much the efficacy or the usability of predictive analytic solutions, but their appropriate (or most effective) initial inflection point: namely, where should they start?
“Most have only a vague notion about the business areas or applications that can benefit from predictive analytics,” he writes. “Most don’t know how to get started: whom to hire, how to organize the product, or how to architect the environment.”
The most common inflection point is in marketing, Eckerson and others say. Other important applications include budgeting and forecasting, fraud detection, demand planning, customer service, quality improvement, surveying, and supply chain management, according to TDWI’s research.
“Most experts agree that predictive analytics requires great skill—and some go so far as to suggest that there is an artistic and highly creative side to creating models—most would never venture forth without a clear methodology to guide their work,” Eckerson explains.
