45 years ago, IBM engineer Irvin Miller penned an article for The Harvard Business Review touting the beauty and power of the IBM System/360. IBM essentially put the keys of business analytics into the hands of Nixon-era executives via a machine that used elegant equations to solve business dilemmas. The era of (Big) Data was born.
IBM is still reaping value from Big Data, even though they now sell services to analyze it, no longer building the machines that do the crunching. Chairman and CEO Ginni Rometty told CNBC recently that "data is the world's natural resource." Her statement is poetic and profound. Big Data, if interpreted as information about human preferences, predilections and activity, is practically inexhaustible. And like any other natural resource, it needs to be protected, refined and preserved if it is to hold any true value.
Big Data is quickly becoming an integral part of how businesses solve problems and set strategy. When used properly, it is quite potent. When used without balance, things can go very wrong. Think about the overuse of data modeling that fueled the financial meltdowns in 1998 and 2008: Data, models and more to the point, the specific behavioral assumptions behind them, were trusted more than experience and intuition. These catastrophes showed the folly of believing that a model can account for every permutation of human/market behavior, placing unquestioned probabilities to activity that has proven to be at times grossly reactive. Such belief represents a dangerous imbalance, a faith that analytics is superior to wisdom or "knowing."
What is data, really? The word's Latin origin -- datum -- means "something given." Something given, but not necessarily a complete answer. Big Data is that "something given" on steroids; reserves of information so vast and complex they require new and powerful methods to process them. Which leads to the question of what happens after all the processing has occurred and the analysis is complete? If Big Data is muscle, where do the softer elements of intuition and wisdom enter the equation? Because what we do with the data and analysis is more important than the data itself.
What we do with it rests on bringing proper care to Big Data's yield. Care in terms of balancing information and insight, certainty and ambiguity, masculine analysis and feminine reflection. Without such balance, we run the risk of hiding behind mountains of information with scant regard for our active role in mindfully deciding what comes next.
With the idea of applying balance to Big Data, below is a short list of suggestions to get the most from any info-rich analytical pursuit:
1. Be open to where the data takes you: Part of the fun of Big Data lies in the surprise element. Stacking up scores of information, one against the other, can create unforeseen and valuable insights.
2. Don't become enamored with the obvious solution: Big Data is sexy, and this sexiness can create a kind of irrational desire to run after the first big idea. Data may be neutral but human ambition is not.
3. Bring intuition, wisdom and "gut feeling" to bear: Oftentimes, these terms are equated with a more feminine approach to problem solving. Exactly the point if you want to balance Big Data's masculine vibe.
Analysis of consumer habits, social media impact, industry competition or dangerous non-governmental actors is becoming easier and cleaner to do, thanks to Big Data. With time, we'll figure out whether our management of such computational insight moves us forward or leaves us merely chasing data.