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Big Data: Lessons from Earlier Revolutions
David Meer, partner, Booz & Company / Strategy + Business / September 18, 2013
Imagine for a moment the following scenario: You and your fellow members of the senior leadership team are gathering in the conference room for a regular meeting with the CEO. She starts talking about a recent global economic forum where there was a lot of buzz about a technology that promises—or threatens—to turn the business world upside down. She turns to the group, with particular glances toward the chief marketing and chief technology officers, and asks, “What’s our strategy for dealing with this? Are we ready?” During the next few weeks and months, task forces form, new people are hired, and the organization strains to digest the implications of this technology revolution and make the most of it.
The year: 1993. Because for the moment, I’m not talking about “big data”; I’m talking about the Internet. In 1997, digital advertising in the United States cracked the US$500 million mark. A decade and a half later, it was more than $10 billion.
Here’s another example: When point-of-sale transaction data first became available in the late 1980s, marketers could finally know with some certainty their market shares, the prices consumers were paying, and what percent of sales were on deal—all things we take for granted today. And going back to the Mad Men era, think of the revolution in marketing triggered by pioneers like Robert K. Merton and Daniel Yankelovich when they invented, respectively, focus groups and consumer segmentation.
My point is that big data is just the latest in a series of technology revolutions that have changed the nature of business, in particular customer-facing activities such as innovation and marketing. Our reaction to it should be informed by all we’ve learned from past revolutions, which for me boils down to two main points: Don’t miss the boat, and stay focused on solving core business issues.
The reason I emphasize not missing the boat is that big data isn’t just a matter of more information and better analytics, but a true paradigm shift toward more data-driven decision making. This means extracting insight from the full range of available data. My belief is that the emergence of big data as a major topic is causing increased attention to all kinds of data—including old-fashioned, created data and experiments; digital data; transaction data; and unstructured data. And that’s a good thing. The ability to collect, harmonize, process, interpret, and act on all your data, big and small, will become a core enterprise capability. But it has to live at the center of business decision making—it won’t (and can’t) be relegated to the periphery, performed by insights specialists and third-party vendors. Missing the boat means delaying this inevitable journey.