Aug 08, 2013
The Big Challenges of Big Data
Here is this month’s piece from Brand Velocity, an Atlanta-based consulting firm that is putting Peter Drucker’s ideas into practice at major corporations.
In 1988, Peter Drucker wrote an article for Harvard Business Review called, “The Coming of the New Organization.” Twenty-five years later, Drucker’s “new organization” is still not here.
This observation may surprise some, especially given the way that so many executives and management writers are trumpeting the “Big Data revolution” in which we now find ourselves.
But from the standpoint of knowledge-work productivity—a term that Drucker himself coined—our ability to turn endless reams of facts and figures into genuinely useful information remains very immature. This isn’t due to the limits of technology. Indeed, the biggest Big Data challenge at the moment does not stem from the science but, rather, from how we’re struggling to have art and science come together through human beings.
“Everyone in an organization should constantly be thinking through what information he or she needs to do the job and to make a contribution,” Drucker wrote in that 1988 essay. “This may well be the most radical break with the way even the most highly computerized businesses are still being run today. There, people . . . believe that information specialists know what data executives and professionals need in order to have information. But information specialists are tool makers. They can tell us what tool to use to hammer upholstery nails into a chair. We need to decide whether we should be upholstering a chair at all.”
This is a point echoed by Upendra Belhe, senior vice president and chief enterprise business analytics scientist at Chubb Insurance. “Today’s biggest struggle,” he says, “is the need for companies to productively integrate what business people know with what data scientists know, to generate insights that can help companies make a meaningful difference.”
In our work at Brand Velocity, we find three common misunderstandings with Big Data. First, many executives assume that analytics are automatically worthy, regardless of an organization’s willingness and ability to make them actionable.
Second, too many managers act as if one size fits all—that is, as if they don’t need to carefully tailor their company’s information systems to meet their particular structure and culture while also taking into account their inherent limitations and constraints.
Finally—and this speaks directly to the issue that Drucker raised—a large number of companies are mistakenly convinced that Big Data experts can add value even when they’re talking past or altogether ignoring the managers who must use the data for front-line decisions.
As Drucker taught, there is a huge difference between a piece of information that is merely thought provoking and one that can actually lead to action. Being actionable requires integration—with a clear strategy, excellent industry and company knowledge, good data quality, and business and data experts who are committed to learning from one another.