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SAP's recent posting 10 Myths About Predictive Analytics reminded me there's something I've been meaning to mention for a while. (SAP's 10 myths are pretty much on the money by the way, so that post is worth a quick read...).
So much marketing seems to imply that predictive analytics is somehow superior to business intelligence. The suggestion is that if you already have business intelligence you should be looking to upgrade it by adopting predictive technologies. Or, to use Tom Davenport's model, companies should be on a continual migration from descriptive analytics, to predictive, and then to prescriptive. That migration is a marketing myth. True enough, many companies should be considering predictive analytics - but as a complement to business intelligence, not a replacement.
BI will always have it's place - the CFO will always need financial statements, the distribution manager will always need a shipping list, and call center managers will always needs a dashboard. No need to fix what isn't broken. However, predictive analytics comes into it's own when there are both large amounts of data and when cause and effect relationships are complex. BI does a poor job with those use cases. I'll talk more about that as I build out my description of different analytic types in the next few weeks.