Looking Outward with Big Data: A Q&A with Tom Davenport
The management scholar provides an incisive look at the true potential of big data and the many challenges to unleashing it.(originally published by Booz & Company)
This article was originally published by Booz & Company.
For some, “big data” is a revolution poised to transform everything from how we gather intelligence on terrorists, to how we manage our supply chains, to how we brush our teeth. For others, it’s an evolution, little more than the latest twist in the decades-old notion of business intelligence. Sorting out its true potential is no easy task.
Fortunately, Tom Davenport, the President’s Distinguished Professor in Management and Information Technology at Babson College and a long-standing expert on business analytics, has turned his attention to the issue. His newest book, Big Data at Work: Dispelling the Myths, Uncovering the Opportunities (Harvard Business Press, 2014), avoids the common focus on the technological marvels of big data. Instead, it examines the present and future potential for solving real business problems, and the management and strategic challenges that big data raises as companies strive to put it to use.
Davenport recently spoke with strategy+business about big data’s potential for transforming not just how companies gather and analyze information, but how they use it to learn about their external business environments, make decisions, and develop new strategies.
S+B: In previous books, you’ve written extensively about business analytics, and now you’ve turned your attention to big data. What’s the difference between the two?
DAVENPORT: It’s interesting. Initially, I didn’t see much of a distinction, and I thought that I could kind of rest on my laurels and not write a book about big data—because the fact is that the analytical tools and approaches used are not all that different for big data. But when I started talking to companies and data scientists, I realized that there really were some fairly substantial differences—some that have yet to be fully articulated and some that are already in evidence.
First, much of the data used in big data analysis is unstructured. It’s amazing that we can now analyze text and voice and video. But you need to figure out how to structure all that data and get it in a format where you can analyze it, and it takes a lot of time and effort to get the data into useful rows and columns. Even once it’s structured, it’s not easy to analyze, for example, the meaning of people’s comments on social media or in blogs or customer reviews. I was talking to a professor at USC who studies political discourse in social media, who pointed out that it’s tough in that realm because computers are totally incapable of detecting sarcasm.
Second, the sheer speed of the flow of data is now like a river, very fast moving and big, that just keeps on coming. This brings up a real management challenge: We simply don’t have the continuous decision-making methods that can make effective use of the continuous data stream we have at our disposal. So we need to shift our approaches to both how we monitor data and how we use it in decision making.
We’re already seeing this issue arise in the sentiment-analysis applications where people are looking at what their customers are saying about them on the Internet via social media and other channels. The technology allows them to monitor the data as it comes in, and they say, “Oh, look—opinion is up, it’s down, it’s up, it’s down.” But they don’t really know what to do with the results, and they never set up any criteria for when and how they should take action.
To make matters worse, data moves and changes so quickly that in order to make decisions about it, you have to create thousands of new models a week—a speed that people just aren’t capable of. In fact, as humans, we can no longer understand a lot of the reasons that one marketing effort, for instance, might succeed more than another. I think the whole realm of managerial decision making, at least in marketing—and probably in some other areas as well—is changing. The best we can do is to take a high-level overview of how these systems are working and hope that they don’t get off track. It’s not that different from what has already happened in financial services, with flash crashes and the like. And the companies that can develop these high-speed decision-making capabilities in response to the sheer speed of big data will be taking a big step forward.
We need to shift our approaches to both how we monitor data and how we use it in decision making.
S+B: Unstructured data that’s coming in at light speed—that sounds pretty daunting. How have the sources of all this information affected the ways in which it is managed?
DAVENPORT: Substantially, and that’s the third big difference. In my new book, I quote Peter Drucker, who noted almost 20 years ago that one of the risks of corporate IT lay in the fact that because it relies only on internal data, it encourages a “degenerative tendency…to focus inward on costs and efforts, rather than outward on opportunities, changes, and threats.” One of the great advances of big data is that so much of the data now comes from external sources: from social media, or macroeconomic data, or weather data, for instance. The fact that companies are becoming able to incorporate it into their planning and decision-making processes is a healthy development. If companies can include this kind of data in their models, they’ll have a much better idea of how successful they might be with a particular product or marketing campaign.
S+B: What kinds of opportunities do you think all this new access to external data will open up? Will companies really become more outward looking?
DAVENPORT: Yes, eventually. There’s a company I talk about in the book called Recorded Future, which indexes huge swaths of the Internet, then analyzes the resulting data to forecast future events. It’s used by intelligence agencies to predict the potential for terrorism, for instance, and by private companies to conduct market analysis and competitive intelligence. Procter & Gamble is one of its customers, and I’m always impressed by how the company tries to get the best external information.
P&G uses Recorded Future to assess whether there are changes in customers, suppliers, or competitors that might affect its success—even details like whether there might be a strike in one of the ports from which an important supplier ships raw materials. They also work with other external information companies, including Signals Intelligence Group in Israel, on competitive and strategic intelligence. P&G embeds the data in its “decision cockpits,” which go out to 50,000 employees, and its “business spheres,” which are group analysis and decision-making rooms for senior executives.
It still strikes me how few companies pay attention to such data in terms of looking at their markets and their customers and their competitors. I think the external focus is a great idea, and it’s the direction in which a lot of big data analysis is going. The ability to analyze the external environment is going to be a real point of differentiation from one company to another.
S+B: And yet, in many of the cases you cite in the book, companies, and especially large ones, appear to be using big data in the service of rather traditional business goals.
DAVENPORT: Yes, that’s true. A lot of them aren’t really thinking differently about what to do with big data. Instead, they’re simply asking, “How can we use these technologies to save money?” It’s a fine thing to do, but it’s not a very interesting or inspiring way to use this technology. GE, for example, is now using sensors to collect and analyze data on the performance of its gas turbines, and the company estimates that improving performance by just 1 percent could lead to US$55 billion in fuel savings over 15 years. That’s a lot of money. Yes, the technology is amazing, and it’s a great business goal, but it’s not really a breakthrough.
But at some point, I think, GE will have so much information on what makes a turbine run efficiently that the company will be able to monetize that information by creating new products and services around it. And even since I wrote the book, I’ve seen more and more examples of large corporations using big data to create new products and services in hopes of generating growth. Even financial-services companies like JPMorgan Chase & Company and Barclays have started new business units to develop new offerings around data. I think it’s a really helpful new development. Of all the things you can do with big data, developing new products and services is, I think, the most interesting and valuable.
Of all you can do with big data, developing new products and services is the most valuable.
The key, however, is still the ability to analyze all the information that’s being collected. The so-called “Internet of Things” is a great example. The sheer amount of data being collected is incredible, but studies suggest that just one half of 1 percent of all the data in the world gets analyzed in any way at all. To really get value, we have to have the analytics and the managerial capabilities and skills to use all that data to make better decisions. It’s not how much you have, but what you do with it.
- Edward H. Baker is a longtime business journalist and a contributing editor at strategy+business.