What executives don't know about big data
How much more profitable would your business be if you had free access to 100 times more data about your customers? That’s the question I posed to the senior executives in attendance at a recent big-data workshop in London.
But not a single member of that tech-savvy crowd would hazard a guess. One of the CEOs actually declared that the surge of new data might even lead to losses because his firm’s management and business processes wouldn’t be able to manage it in a cost-effective way.
Big data doesn’t inherently lead to better results.
Although big data already is – and will continue to be – a relentless driver of revolutionary business change (just ask Amazon’s Jeff Bezos, Google’s Larry Page or LinkedIn’s Reid Hoffman), too many organisations don’t quite grasp that being “big data-driven” requires qualified human judgment, and lots of it.
Web 2.0 juggernauts such as Google and Amazon have the advantage of being built around big-data architectures and cultures. Their future success is contingent upon becoming disproportionately more valuable as more people use them. Big data is both an enabler and a byproduct of “network effects”. The algorithms that make these companies run need big data to survive and thrive. Ambitious algorithms love big data and vice versa.
Similarly, breakthrough big-data systems such as IBM’s Watson – the Ken Jennings-defeating Jeopardy champion – are designed with a clarity and specificity that makes their many, many terabytes of data intrinsically indispensable.
By contrast, the overwhelming majority of enterprise information technology systems can’t quite make up their digital minds. Is big data there to feed the algorithms or inform the humans? Is big data being used to run a business process or create situational awareness for top management? Is big data there to help the company innovate or maintain the status quo? “All of the above” is exactly the wrong answer.
What works best is not a C-suite commitment to “bigger data,” ambitious algorithms or sophisticated analytics. A commitment to a desired business outcome is the critical success factor. The reason why my London executives evinced little enthusiasm for 100 times more customer data was that they couldn’t envision or align it with a desirable business outcome. Would offering 1,000 or 10,000 times more data have been more persuasive? Hardly. Neither the quantity nor quality of data was the issue. What matters is how – and why – vastly more data leads to vastly greater value creation. And designing and determining those links is the province of top management.
Instead of asking, “How can we get far more value from far more data?” successful big data overseers seek to answer, “What value matters most, and what marriage of data and algorithms gets us there?” The most effective big-data implementations are based on desired business outcomes, not massive data sets.