Using Analytics for Better Decisions

Ann All

Forget your Palm Pilot, your BlackBerry and your laptop. The most valuable tool for today's manager is a crystal ball.


What, you don't have one? In the absence of said ball, the next best thing may be sophisticated analytics software that uses statistical algorithms to crunch through data and offer insights that can help you predict business events before they happen and take appropriate action.


Yeah, we know. Shouldn't your data mining, online analytical processing (OLAP) and other fancy-schmancy business intelligence tools be enough to get the job done? Not necessarily. Those tools locate and organize data that can aid in decision making, but without the crucial predictive element.


The success stories are pretty compelling. Magazine distributor Time/Warner Retail Sales and Marketing says it has achieved a return of 282 percent over five years with a predictive analytics application, largely by reducing the number of unsold magazines returned by retailers.


An ARC Advisory Group analyst was quite taken with a demonstration of an app that helps retailers predict future out-of-stock events -- including those caused by factors outside the normal purview of supply chain organizations. Not only that, but the app features a decision support capability that helps users determine the best proactive strategies.


A Babson College study shows that high-performing companies tend to use three key characteristics of predictive analytics: capturing and maintaining a large amount of transactional data, combining it with data found in the public domain, and fostering a culture of fact-based decision making.


As always, the latter (and most human) element may be the toughest nut for companies to crack. But vendors are increasingly willing to provide solutions that incorporate the first two capabilities.

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