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What is Analytics?

That’s one of those questions that should have a simple and straightforward answer, but in fact has dozens of answers (not all of which are either simple or straightforward.)

Addressing a closely related question, Thomas Wailgum at CIO is wondering just exactly what we mean by the term “BI.” These two questions are linked because the two areas of expertise are closely linked, and because another question that often gets raised is “What is the difference between BI and analytics?” (or vice-versa.)

In fact, Tom Davenport,  one of Wailgum’s panel of experts (along with Merv Adrian and others)  offers the following:

I view BI as all the things that organizations do to make sense of data for purposes of managing an organization better. To me it includes reporting and analytics, though there is much more of the former than the latter. However, I sense that the BI term is rapidly being supplanted by “business analytics.”

Tom is absolutely correct in setting out analytics as a subset of Business Intelligence. Analytics is one of the “things organizations do” to transform data into effective business decisions, but it isn’t the only one. At Sybase, when distinguishing between the two categories that we have labeled Reporting and Advanced Analytics, we first look at whether data is simply being formatted and summarized, or whether deeper patterns within the data are being sought after and explored. Advanced analytics generally requires extensive ad-hoc analysis as well as consideration of many more variables than are likely to be incorporated in a simple report. The goal of an advanced analytics environment is often forecasting — providing a sufficiently detailed analysis of what has come before in order to make plausible predictions of what will come next.  The term “predictive analytics” is often used to describe such applications, but it’s important to note that any sufficiently complex analysis can be considered advanced analytics even if it does not provide a prediction or forecast.  

For example, any time that human behavior is being analyzed, that’s most likely advanced analytics.  To be sure, businesses often analyze behavior in order to make a prediction about an individual will do next — Buy more of the same? Buy something else? Default on payment? — but they can also use behavioral analyses to determine how effectively their systems and processes meet customer expectations, how well they have done at holding the target market’s attention, and related questions. 

The vital distinction is this: advanced analytics involves more than just slicing and dicing of the data. The data has to be evaluated for key trends and patterns, which are abstracted into a model. When new data is gathered it is run against the model. To give a very simple example, analysis of customer purchasing trends may reveal correlations between a customer’s past buying behavior, geographic location, income, and likelihood to purchase one of several new products. When a customer carries out a transaction, his or her behavior and profile are compared with, or scored against, the model in order to predict the likelihood that a particular offer will succeed.

Ultimately, it’s this reliance on models that sets advanced analytics apart from other types of BI analysis. When a business takes a look at data to try to improve decisions and performance, that’s business intelligence. When a business compares incoming data with a model in order to achieve deeper understanding, deal with human behavior in real time, or predict what’s going to happen next, that’s advanced analytics.

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  1. Timo Elliott says:

    Here’s how I see as the (ongoing, recurrent, chronic?) BI “nomenclature debate”:

    First, people often mix up two different things: the business issue that people are trying to solve, and the technology that is being used to solve it.

    The underlying business problem of using information more effectively to improve performance (whatever you want to call it) has existed at least since the dawn of computing (and probably since the dawn of time) — and hasn’t substantially changed, despite the increasing complexity of the modern world.

    So any discussion that tries to pick apart, say, BI from Analytics on the basis of the BUSINESS USE of the data is doomed to failure. For example, some people try to make a distinction between “backward-looking BI” and “forward-looking analytics”, but people have always used “backward looking data” to make predictions for the future (otherwise, what would be the point of the analysis in the first place?) — what has changed is what people are trying to do, but how well the technology is able to help them do it.

    There have been massive changes in BI technology over the last 20 years, and is the area that discussions of what is “BI” vs “analytics” should be sensibly confined to (as you mostly do in this article). But any separations that people make are inevitably artificial, since BI technology has been a slowly-moving spectrum of different incremental features, rather than big steps forward (except, maybe, for the notion of a “semantic layer”?).

    So different people are going to draw the lines in different places (sometimes using usage differences rather than technology differences e.g. “if human behavior is involved, it’s likely to be advanced analytics”)… And what’s the point of comparing arbitrary lines drawn along a spectrum anyway?

    Ultimately, what drives most name changes is boredom with existing terms (I’m regularly told I should mention BI to finance people, because it’s too passé), a need to show that new technology is available, and a need (particularly for new entrants, and analysts) to differentiate from competitors (“we do BI 2.0!”).

    My advice to anybody caught up in name wars is to forget about it. It’s essentially pointless. It’s only analysts and vendors that care — businesses themselves have the “luxury” of actual business problems to solve. They can look at the new technologies available and match them to what they can achieve — without having to care what the category is called.

    Thanks for letting me rant! :-)

  2. Muthu Ranganathan says:

    Good to bring, yes I also agree that the business user does not care, they are basically looking at how reporting and analytics will help them interpret data and make meaningful, fast and predictable decisions. Another important aspect from a technological difference is the tool vs. Application analogy I often use. BI is a tool vs. Business analytics is purpose built application
    Muthu Ranganathan (@muthukr27)

  3. Jerry says:

    Thanks for the article. I think that it is reasonable to distinguish between “BI” and “Advanced analytics” when we speak about Sybase IQ as IQ offers many functions for AA (time series analysis functions, Fuzzy logix library…) not typical for other databases. I would appreciate to read something more on this topic.

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