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Data can offer a peek into business conditions weeks or months out, if you have the right predictive tools. That's the goal of Procter & Gamble's 2-year-old Business Sufficiency program, which gives executives a glimpse of performance indicators six to 12 months ahead. Using SAS tools for statistical analysis, P&G developed dozens of analytic models to assess production, shipments, and market share; sales trends by country, territory, product line, chain, and store; media and advertising activities; and regional and national economic conditions. Because P&G's analytic models are predictive and exception-oriented, they help execs address looming problems with production, sales, distribution, marketing, and merchandising performance--before they lead to real financial shortfalls.
And P&G analysts don't just offer one prediction for each model; they scope out the range of performance possibilities so managers can devise backup plans, in case expectations don't hold up.
Talk about an edge.
Our InformationWeek 2012 Business Intelligence, Analytics, and Information Management Survey shows that the old practice of following the money--using lagging financial indicators to guide a company's decisions--is giving way to the forward-looking approach of following the data. Respondents' companies are following P&G's lead--gathering, managing, and analyzing not only more information, but also more types of information, using advanced predictive and statistical analytics to improve internal operations, get closer to customers, sell and market products more effectively across channels, and outperform competitors.
Other top trends: The 542 respondents to our survey say business intelligence/analytics software as a service is making its mark, despite the fact that, as the de facto stewards of enterprise data, BI and information management professionals have reservations about the cloud--63% worry about data security, and 47% foresee integration problems. Mobile interfaces for BI and analytics are in high demand, with 44% of respondents planning to add such options for smartphones and tablets. Compared with our survey last year, the number of respondents who cite data-quality problems as a barrier to adopting BI and analytics products enterprise-wide fell nine points, to 46%--an accomplishment in the big data era. But if there's one uber trend in BI and information management, it's the meteoric rise of analytics, particularly advanced statistical and predictive analytics. For the third year in a row, survey respondents rate advanced analytics as the most compelling among a dozen leading-edge technologies.
Once an elite niche, analytics has become the proverbial tail wagging the dog, with vendors and practitioners alike making analytic capabilities and initiatives their top priority. The trend goes hand in hand with rising interest in using big data sets to mitigate risk, anticipate customer demand, and formulate more successful product and service offerings. Name a business scenario, and you can likely apply advanced analytic techniques to make better, more pre-emptive decisions rather than react to failures later.
That's the key contrast with what's now disparagingly dubbed "rearview mirror BI." Business intelligence has long been associated with activities that explore and extrapolate on historical data. Summary statistics, queries, reports, and even threshold-triggered alerts and low-latency dashboards based on historical information are rearview mirror. They provide a picture of where you've been. Advanced analytics applies statistical and predictive algorithms to come up with calculated, predictive measures, scores, and models. It shows where you're headed.