Ten Mistakes to Avoid In Predictive Analytics Efforts

August 5, 2015

By Fern Halper

Predictive analytics—a statistical or data mining solution consisting of algorithms and techniques used on both structured and unstructured data to determine outcomes—is becoming a mainstream analytics technology, and organizations are realizing its competitive value.

Business intelligence (BI) is typically reactive and can’t estimate targets (called outcomes) of interest, such as: Who will respond to a promotion? Who will drop my service? When will a piece of equipment fail? Predictive analytics, however, is proactive.

TDWI research indicates that predictive analytics is one of the most popular kinds of advanced analytics. If users stick to their plans, a majority of organizations will be using it to predict customer behavior, cope with risk, and improve operations, along with many other use cases. Although many companies are excited about the possibility of utilizing predictive analytics, there are a number of interrelated themes about what not to do when it comes to predictive analytics projects.

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