9 Truths Lead To Big Data's Future

6. "The same meaning can be expressed in many different ways, and the same expression can express many different meanings."

So say Googlers Alon Halevy, Peter Norvig and Fernando Pereira in their touchstone March 2009 IEEE Intelligent Systems article, "The Unreasonable Effectiveness of Data." How is data's unreasonable effectiveness revealed? Via semantic interpretation of "imprecise and ambiguous" natural languages and by tackling the scientific problem of interpreting massive, aggregated content by inferring relationships via machine learning.

7. "Big data is not about the data! The value in big data [is in] the analytics."

Harvard Prof. Gary King said this, in effect spinning out the Googlers' (see quote number six) thoughts. Yet I can't completely agree with King. There is value in the business process of determining data needs and devising a smart approach to collecting and structuring the data for analysis. Analytics helps you discover that value, so my preferred formulation would be, "the value of Big Data is discovered via analytics."

My thinking isn't original. See, for instance, "Big Data, Analytics, and the Path from Insight to Value," by Steve LaValle, Eric Lesser, Rebecca Shockley, Michael S. Hopkins and Nina Kruschwitz in the December 2010 issue of MIT Sloan Management Review.

8. "Intuition is as important as ever."

So says Phil Simon, who wrote Too Big to Ignore: The Business Case for Big Data," published earlier this year. (I contributed material on text analytics and sentiment analysis.)

Simon explains, "Big data has not, at least not yet, replaced intuition; the latter merely complements the former. The relationship between the two is a continuum, not a binary." Tim Leberecht explores this same point in a June article for CNN, "Why Big Data will never beat business intuition."

Finally, these eight points lead to a future truth, an appraisal that I believe isn't yet widely understood:

9. The future of big data is synthesis and sensemaking.

The missing element from most solutions is the ability to integrate information across sources, in situationally appropriate ways, to generate contextually relevant, usable insights. I'll pull some defining quotations from an illuminating paper by design strategist Jon Kolko (admittedly applying them out of context). First, Kolko cites cognitive psychologists who have been studying the connections between problem solving and intuition, who "reference sensemaking as a way of understanding connections between people, places and events that are occurring now or have occurred in the past, in order to anticipate future trajectories and act accordingly."

(Kolko's source is "Making Sense of Sensemaking 1: Alternative Perspectives" (2006) by Gary Klein, Brian Moon, and Robert R. Hoffman in IEEE Intelligent Systems. See also their "Making Sense of Sensemaking 2: A Macrocognitive Model.")

Kolko sees [design] synthesis as a key element, a "sensemaking process of manipulating, organizing, pruning and filtering data in an effort to produce information and knowledge." What capabilities are afforded? IBM Fellow Jeff Jonas says "general-purpose" sensemaking systems will colocate diverse data in the same data space. Such an approach enables massively scalable, real-time, novel discovery over an ever changing observational space."

Isn't that our big data goal, to advance from pattern detection to actionable conclusions? I hope my nine truths have helped you understand the path.

Seth Grimes is the leading industry analyst covering text analytics and sentiment analysis. He founded Washington-based Alta Plana Corporation, an IT strategy consultancy, in 1997. Follow him on Twitter at @SethGrimes.

Making decisions based on flashy macro trends while ignoring "little data" fundamentals is a recipe for failure. Also in the new, all-digital Blinded By Big Data issue of InformationWeek: How Coke Bottling's CIO manages mobile strategy. (Free registration required.)