Overlap in the Business Intelligence / Predictive Analytics Space

I've received considerable feedback on the post Business Intelligence vs. Business Analytics, which has also caused me to think more about the BI space and its overlap with data mining (DM) / predictive analytics (PA) / business analytics (BA). One place to look for this, of course, is with Gartner, how they define Business Intelligence, and which vendors overlap between these industries. (I think of this in much same way as I do DM; I look to data miners to define themselves and what they do rather than to other industries and how they define data mining).

I found the Gartner Magic Quadrant for Business Intelligence in 2009 here, and was very curious to understand (1) how they define BI, and which BI players are also big players in the data mining space. Answering the first question, data analysis in the BI world is defined here as comprising four parts: OLAP, visualization, scorecards, and data mining. So DM in this view is a subset of BI.

Second, the key players in the quadrant interestingly contains only a few vendors I would consider to be top data mining vendors: SAS, Oracle, IBM (Cognos), and Microsoft in the "Leaders" category, and Tibco in the visionaries category. Of these, only SAS (with Enterprise Miner) and Microsoft (SQL Server) showed up in the top 10 of the Rexer Analytics 2008 software tool survey, though Tibco showed up in the top 20 (with Tibco Spotfire Miner).

I think this emphasizes again that BI and DM/PA/BA approach analysis differently, even if the end result is the same (a scorecard, dashboard, report, or transactional decisioning system).

Business Analytics vs. Business Intelligence

I used to be one that thought the term "data mining" would stay as the description of the kind of analytic work I do. To a large degree it has, but there are always new spins on things, and it seems that quite often in the business world, Predictive Analytics or Business Analytics are the terms of the day.

I just came across this post from the Smart Data Collective: OLAP is Dead (Long Live Analytics), which had some fascinating graphs on hits related to the phrases OLAP and Analytics. The first shows the steady decline of OLAP as a searched term to the point where even the OLAP report has been renamed to The BI Verdict. Meanwhile, "analytics" has been increasing steadily in hits. SAS even touts themselves as leaders in "Business Analytics" now.

Which brings me to the question in the title of this post. It seems to me that Business Intelligence has taken over the role that OLAP and dashboarding used to take on (at least in the circles I worked in). Is there a difference between Business Intelligence and Business Analytics? James Taylor, someone whom I respect tremendously, doesn't think so.

As SAS talked about its business analytics framework it became clear that they envision the results of data mining and predictive analytics (where they genuinely have offerings superior to almost everyone) will be delivered in reports or dashboards. This is what I have somewhat dismissively called "predictive reporting" and while it is better than purely historical reporting, it does not do much to make every decision analytically based as it leaves out the decisions made by machines (which don't read reports) and those made by people with too little time to read a report (most call center or retail staff, for instance) or no skill at interpreting it.

I guess I just don't see the difference between BI and BA...

If all of business analytics is reduced to "predictive reporting", then I can see why some might consider it no more than business intelligence. But even so, are they the same? I don't mean are the results the same either. For that matter, the final decisions from analytics for say classification look just the same as a human decision (buy or not buy? fraud or not?). But is the process the same? I would argue "no". Much of the power of predictive analytics comes from the automation in searching for and assessing nonlinearities, interaction effects, and combinatorics relating observables to outcomes. So, rather than manually assessing these, one automates the process through the use of "decision trees", "neural networks", or some other algorithm. So the difference lies in efficiency in the process.

Now how the predictive information is used, in a report, as part of an automated system or in some other way, is a critically important question, but independent of how the decisions are generated.