Publications

DM Review Magazine, July 2007, By Jeff Kaplan

Why were there so many failed enterprise customer relationship management (CRM) implementations? Everyone from executive management teams to database administrators have their own point of view on where the failure occurred: upper management didn't buy in; the software promised to do more than it could; the implementation took too long; the hidden costs were too high; and countless other reasons.

As with everything in life, we try to learn from our past mistakes, and we hope that by doing so, history will not repeat itself. The same is true with the recent surge of investment in data mining (also known as predictive analytics). The good news is that many companies are getting it right this time around - they have learned from their CRM software purchase mistakes and are investing in predictive analytics as a service. Unfortunately, for every company that is getting it right, there are many companies continuing to look for the easy, quick fix by purchasing out-of-the-box software with hopes that it's going to magically save the day.

he key challenge that packaged predictive analytics software has not been able to crack is how to extract knowledge from data quickly and put it into the hands of marketers to make better, more informed decisions. Although significant progress has been made over the last three decades by academic researchers in database technology, statistics and machine learning to improve the techniques for detecting patterns in large data sets, the use of predictive analytics still requires an incredible amount of expertise. This expertise is sorely needed to deliver accurate, understandable and actionable analysis.

Read more.

What's this?