Those intent on mining unstructured "big data" for business intelligence will do well to familiarize themselves with the inherent and human limitations of that approach.
Strategist Daniel W. Rasmus has a long and thought-provoking piece in Fast Company titled Why Big Data Won't Make You Smart, Rich, Or Pretty. It spells out some of the factors data modelers need to be aware of when trying to make sense of vast swaths of data that may come from multiple sources around the company or around the Internet.
We have extensively covered the promise, and the nuts and bolts, of the coming wave of big-data analytics (see the related posts below). Cassimir Medford wrote about two obstacles in the road to the big-data future -- a shortage of mathematically trained, analytically inclined workers, and the difficult of locating the right hardware and software tools. These are problems of a different order than the fundamental (some might say existential) ones with which Rasmus concerns himself. Let's look at a few of these.
Should we take human psychology more into account and apply hyperbolic discounting? Or does the declining discount rate capture the reality better? No one knows, for a particular problem or market, but some may think they know, and may choose a model that agrees with their biases.
No model constructed at the time could have captured the effect of the rise of Walmart on the retail industry. Later, Amazon's rise presented a similar change of context that would have invalidated key assumptions in any model of retail. The model has to change with the world. Feedback loops can help implement incremental change, but fundamental context change will defeat them. Ongoing agility is required; that and a willingness to throw out and rebuild models that have gone out of touch with the world.
Rasmus outlines a handful of other existential difficulties in the business of big-data analytics, including the modelers' motives, feedback loops, complexity, and the unpredictable effects on the model itself of acting on computed intelligence. Go read his article. You'll be glad you did.
Few doubt that big-data analytics will be a large and growing trend from here forward. It's important to recognize what kinds of questions for which the technology will provide reliable answers, and where its limitations lie. "In the end," Rasmus writes, "we may not be equipped to apply the technology in a meaningful and safe way at scales that outstrip our ability to represent, understand, and validate the models and their data."