A lire sur: http://www.technologyreview.com/business/40320/?nlid=nlbus&nld=2012-05-04
Many entrepreneurs foresee vast profits in mining data
from online activity and mobile devices. One Wharton business school
professor strongly disagrees.
- Thursday, May 3, 2012, By Lee Gomes
Cut the nonsense: Peter Fader says a flood of consumer data collected from mobile devices may not help marketers as much as they think.
Wharton/Peter Olson
Wharton/Peter Olson
Few ideas hold more sway among entrepreneurs and investors these
days than "Big Data." The idea is that we are now collecting so much
information about people from their online behavior and, especially,
through their mobile phones that we can make increasingly specific
predictions about how they will behave and what they will buy.
But are those assumptions really true? One doubter is Peter Fader, codirector of the Wharton Customer Analytics Initiative at the University of Pennsylvania, where he is also a professor of marketing. Fader shared some of his concerns in an interview with reporter Lee Gomes.
TR: How would you describe the prevailing idea about Big Data inside the tech community?
Fader: "More is better." If you can give me more data about a customer—if you can capture more aspects of their behavior, their connections with others, their interests, and so on—then I can pin down exactly what this person is all about. I can anticipate what they will buy, and when, and for how much, and through what channel.
So what exactly is wrong with that?
It reminds me a lot of what was going on 15 years ago with CRM (customer relationship management). Back then, the idea was "Wow, we can start collecting all these different transactions and data, and then, boy, think of all the predictions we will be able to make." But ask anyone today what comes to mind when you say "CRM," and you'll hear "frustration," "disaster," "expensive," and "out of control." It turned out to be a great big IT wild-goose chase. And I'm afraid we're heading down the same road with Big Data.
There seem to be a lot of businesses these days that promise to take a Twitter stream or a collection of Facebook comments and then make some prediction: about a stock price, about how a product will be received in the market.
That is all ridiculous. If you can get me a really granular view of data—for example, an individual's tweets and then that same individual's transactions, so I can see how they are interacting with each other—that's a whole other story. But that isn't what is happening. People are focusing on sexy social-media stuff and pushing it much further than they should be.
Some say the data fetish you're describing is especially epidemic with the many startups connected with mobile computing. Do you think that's true? And if so, wouldn't it suggest that a year or two from now, there are going to be a lot of disappointed entrepreneurs and VCs?
There is a "data fetish" with every new trackable technology, from e-mail and Web browsing in the '90s all the way through mobile communications and geolocation services today. Too many people think that mobile is a "whole new world," offering stunning insights into behaviors that were inconceivable before. But many of the basic patterns are surprisingly consistent across these platforms. That doesn't make them uninteresting or unimportant. But the basic methods we can use in the mobile world to understand and forecast these behaviors (and thus the key data needed to accomplish these tasks) are not nearly as radical as many people suspect.
But are those assumptions really true? One doubter is Peter Fader, codirector of the Wharton Customer Analytics Initiative at the University of Pennsylvania, where he is also a professor of marketing. Fader shared some of his concerns in an interview with reporter Lee Gomes.
TR: How would you describe the prevailing idea about Big Data inside the tech community?
Fader: "More is better." If you can give me more data about a customer—if you can capture more aspects of their behavior, their connections with others, their interests, and so on—then I can pin down exactly what this person is all about. I can anticipate what they will buy, and when, and for how much, and through what channel.
It reminds me a lot of what was going on 15 years ago with CRM (customer relationship management). Back then, the idea was "Wow, we can start collecting all these different transactions and data, and then, boy, think of all the predictions we will be able to make." But ask anyone today what comes to mind when you say "CRM," and you'll hear "frustration," "disaster," "expensive," and "out of control." It turned out to be a great big IT wild-goose chase. And I'm afraid we're heading down the same road with Big Data.
There seem to be a lot of businesses these days that promise to take a Twitter stream or a collection of Facebook comments and then make some prediction: about a stock price, about how a product will be received in the market.
That is all ridiculous. If you can get me a really granular view of data—for example, an individual's tweets and then that same individual's transactions, so I can see how they are interacting with each other—that's a whole other story. But that isn't what is happening. People are focusing on sexy social-media stuff and pushing it much further than they should be.
Some say the data fetish you're describing is especially epidemic with the many startups connected with mobile computing. Do you think that's true? And if so, wouldn't it suggest that a year or two from now, there are going to be a lot of disappointed entrepreneurs and VCs?
There is a "data fetish" with every new trackable technology, from e-mail and Web browsing in the '90s all the way through mobile communications and geolocation services today. Too many people think that mobile is a "whole new world," offering stunning insights into behaviors that were inconceivable before. But many of the basic patterns are surprisingly consistent across these platforms. That doesn't make them uninteresting or unimportant. But the basic methods we can use in the mobile world to understand and forecast these behaviors (and thus the key data needed to accomplish these tasks) are not nearly as radical as many people suspect.
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