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Revolution Analytics recently announced the winners of its “Applications of R in Business” contest. Revolution Analytics has built a business around supporting R, an open source statistical software package, and extending it with features it licenses to customers. I served as a judge in the contest. Since I was in the midst of analyzing the data for our predictive analytics benchmark research, I was interested to see how the contestants applied predictive analytics techniques to specific business problems.

As I’ve written previously, predictive analytics is on the rise, despite the fact that the term is a bit of a misnomer. What software vendors, industry analysts and others call predictive analytics may be described more accurately as data mining. Data mining includes both predictive and descriptive analytics. Predictive analytics is used to predict future occurrences, such as the likelihood of an individual customer purchasing a particular product. Descriptive analytics is used to classify things (say, customers or products) into groups. These descriptive characteristics can be used in conjunction with predictive analytics to help produce more accurate predictions. For example, single male customers in group A may be more likely to purchase a particular product than single male customers in group B.

Nevertheless, the market seems to have adopted “predictive analytics” to encompass the broader category of data mining. I’m comfortable with that use of the term, so here and elsewhere in my research, unless specified otherwise, I consider predictive analytics and data mining to be interchangeable labels. (See: “Technology Terminology: What’s in a Name?”)

R, as a statistical package, includes many algorithms for predictive analytics, including regression, clustering, classification, text mining and other techniques. The contest submissions supported a variety of business cases, including, among others, predicting order amounts to optimize manufacturing processespredicting marketing campaign effectiveness to optimize marketing spending, predicting liquid steel temperatures to optimize steel plant processes and performing sentiment analysis of Twitter data.

The entries served to reinforce the notion that using predictive analytics requires specialized skills. Take a look at the entries above and ask how many people in your organization could perform those types of analyses. Despite the requirement for these additional skills, the demand for predictive analytics continues to rise. The entries show one of the reasons for this rise: the value that predictive analytics can provide. Imagine if you could redirect marketing spending from ineffective campaigns to more productive campaigns, or squeeze costs out of your manufacturing processes.

The entries also demonstrated a best practice: close alignment between the analyst and the underlying business objectives. Predictive analytics is not magic. It requires an understanding of business processes and an understanding of statistical techniques. The judging criteria reflected this requirement as well. One of the three categories we were asked to score was applicability of the submission to business. I think it’s clear how the analyses in the winning entries could provide significant business value.

If you are not yet applying predictive analytics, check out the submissions for examples of where you might apply them to your business; you can skip the implementation details. If you are using predictive analytics and understand the statistics involved, check out the submissions for suggestions of how you might enhance your own applications. In either case, keep an eye out for the results of our predictive analytics benchmark research to learn more about best practices and how others are using predictive analytics.


David Menninger – VP & Research Director

When it comes to technology, debates about whether a particular name suits its category are rampant. Here is a link to one such argument about the term “big data” from Curt Monash, an analyst whom I respect a great deal. This debate rages in the Twittersphere also, as in this comment from Neil Raden, another analyst I respect, suggesting that “big data is a marketing term … imprecise by design.”  Another term I’ve encountered resistance to recently is “predictive analytics.” See: (“Revolution Analytics Hosts Contest on Business Predicting the Future“).

Having spent a large portion of my career developing and marketing software products, I am probably biased, but I see value in broad, easily understood – even if imprecise – terms. Such terms are inclusive rather than exclusive, and that allows more vendors to participate in the markets, prompting more competition, more debate and ultimately better products and more variety of products. Broad terms such as “big data” and “predictive analytics” are also easily understood, so  potential buyers can get an immediate idea of how such categories might fit in their organization. The result is a bigger market for products, which in turn leads to more investment in the category.

On the other hand, broad terms do create some confusion for buyers. Worse, some vendors corrupt the meanings of these terms, and too many jump on the bandwagon. So buyers have to do some homework, research the subject and understand what vendors have to offer. Yes, our business as analysts benefits from some of this confusion because we are hired to help with research and education, but organizations making a large technology purchase should be doing this anyway.

The alternative – very precise and exclusive terms – might eliminate one type of confusion but would add another: an even greater proliferation of technology categories. A more precise approach would place a burden on buyers. In such a world, they would need to know which specific types of solutions to consider early in the buying cycle, and they might not find other categories that provide similar capabilities. The result would be lower overall adoption rates, smaller markets and less investment to help solve business problems with those technologies.

So I like broader terms as the lesser evil. Besides, the market has spoken. While specific terms may be promoted by certain vendors or analysts, traction comes from widespread adoption. So go ahead, debate what these widely adopted terms mean. It will create a better understanding for all. Vendors, find a way to make your products relevant to a particular category. Venture capitalists, invest in a new startup that extends or alters or even replaces a category. In the end, we’ll all have to do a little work, but we’ll have wider selection of better products as a result.


David Menninger – VP & Research Director

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