Sunday, December 06, 2009

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.


Paul B. Felix said...

Great post, Dean.

My colleagues and I have been struggling with this as well. The firm I work for, LiveLogic, is a BI consulting firm, and we are steering the business toward predictive analytics - specificaly around customer retention. We are calling the customer analytics business "Customer Intelligence" for now. In any case, we talked about this last week and concluded that we thought BI is a term that covers the entire industry including etl, dw, oltp, olap, dashboard, anlaytics, etc... Not sure if this is right or not, but the point is that I completely agree. There are no clear definitions.

Thanks again for the great SAP workshop at PAW!

Paul B. Felix

Mark Johnston said...

For a long time, data mining companies decided they did not want anything to do with business intelligence and data visualization, probably because it didn't involve any complicated algorithm.

I think this is changing rapidly. Analysts are now looking for integrated solutions which offer BI + data mining + data visualization. For example, here is a fascinating video showing this type of integration (it also appears merges that with search and collaboration):

Dean Abbott said...

I laughed at "probably because it didn't involve any complicated algorithm"! And I think there is an element of that in the data mining world. I like "Everything should be made as simple as possible, but not simpler". Of course, this is usually done on the context of data mining solutions, where "simple" means regression and the more complex means NNets or SVMs.

We should talk sometime about your product offerings--very interesting.

Fern Halper said...

I tend to think of business intelligence as a subset of business analytics because I view business analytics as a broad category of technologies that help people make business decisions. Whereas BI might just include the analysis of structured data and it is more about slice and dicing data, BA would also include text analytics/mining as well as predictive modeling.

But in reality, it's a matter of semantics.

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