There has been a lot of discussion in the news lately about privacy concerns associated with the collection, storage, and analysis of Big Data. The latest brouhaha resulted from the revelation that “Google and several other advertising companies are bypassing the privacy settings in Apple’s Safari browser.” [“Google’s tracking sets off another privacy debate,” by James Temple, San Francisco Chronicle, 18 February 2012] It wasn’t just the fact that Google was tracking people, it was “tracking the Web-browsing habits of people who intended for that kind of monitoring to be blocked.” [“Google’s iPhone Tracking,” by Julia Angwin and Jennifer Valentino-Devries, Wall Street Journal, 17 February 2012] Google, and like-minded advertising companies, were shooting themselves in the foot with such actions. Google’s actions were particularly foul since “the findings appeared to contradict some of Google’s own instructions to Safari users on how to avoid tracking. Until recently, one Google site told Safari users they could rely on Safari’s privacy settings to prevent tracking by Google. Google removed that language from the site.”
In another article, Julia Angwin reports, “The industry has been caught in a number of high-profile privacy slip-ups. Facebook Inc. recently agreed to settle charges by the U.S. government that some of its privacy practices had been unfair and deceptive to users.” [“Web Firms to Adopt ‘No Track’ Button,” Wall Street Journal, 23 February 2012] As Angwin’s headline states, the fallout from the Google revelation is that “a coalition of Internet giants including Google Inc. has agreed to support a do-not-track button to be embedded in most Web browsers—a move that the industry had been resisting for more than a year. The reversal is being announced as part of the White House’s call for Congress to pass a ‘privacy bill of rights,’ that will give people greater control over the personal data collected about them.” Angwin continues:
“The companies have agreed to stop using the data about people’s Web browsing habits to customize ads, and have agreed not to use the data for employment, credit, health-care or insurance purposes. But the data can still be used for some purposes such as ‘market research’ and ‘product development’ and can still be obtained by law enforcement officers. The do-not-track button also wouldn’t block companies such as Facebook Inc. from tracking their members through ‘Like’ buttons and other functions.”
The debate on this subject has only begun. As President and CEO of a company that offers Big Data services, I’m aware of the value that data analysis can provide to businesses beyond marketing purposes. Good analysis can make businesses more effective. Using the knowledge they gain from Big Data analysis, companies can use resources more wisely and eliminate needless waste while, at the same time, better serve their customers. Actions like Google’s (that will likely end up restricting Big Data availability) could limit the good that can be accomplished in the years ahead. So let’s get the cards out on the table.
Dennis K. Berman reports, “Computer systems are now becoming powerful enough, and subtle enough, to help us reduce human biases from our decision-making. And this is a key: They can do it in real-time. Inevitably, that ‘objective observer’ will be a kind of organic, evolving database.” [“So, What’s Your Algorithm?” Wall Street Journal, 4 January 2012] This is important, Berman writes, because “we are ruined by our own biases. When making decisions, we see what we want, ignore probabilities, and minimize risks that uproot our hopes.” He continues:
“What’s worse, ‘we are often confident even when we are wrong,’ writes Daniel Kahneman, in his masterful new book on psychology and economics called ‘Thinking, Fast and Slow.’ An objective observer, he writes, ‘is more likely to detect our errors than we are.'”
The objectivity that Berman believes is so critical in our lives comes from the analysis of Big Data. That’s why collecting and analyzing Big Data is so important to individuals as well as businesses. Restrict access to Big Data and some of that objectivity will inevitably be lost. Berman writes, “Call it Big Data, analytics, or decision science. Over time, this will change your world.” He continues:
“These systems can now chew through billions of bits of data, analyze them via self-learning algorithms, and package the insights for immediate use. Neither we nor the computers are perfect, but in tandem, we might neutralize our biased, intuitive failings when we price a car, prescribe a medicine, or deploy a sales force. This is playing ‘Moneyball’ at life. It means fewer hunches and more facts. … Crunching millions of data points about traffic flows, an analytics system might find that on Fridays a delivery fleet should stick to the highways — despite your devout belief in surface-road shortcuts. You probably hate the idea that human judgment can be improved or even replaced by machines, but you probably hate hurricanes and earthquakes too. The rise of machines is just as inevitable and just as indifferent to your hatred. … We should take some comfort — however difficult it may feel — that machines will help us eliminate our worst human tendencies.”
Let’s face it. It’s not the ability of computers and algorithms to help us make better decisions that is creeping people out. It’s the fact that Big Data analysis can learn an incredible amount of things about our personal behavior and lifestyle from analyzing data we have generated through our use of technology. For example, using algorithms Target was able to figure out which of its customers were pregnant even if the women hadn’t revealed that specific tidbit to the company or anyone else. [“How Companies Learn Your Secrets,” by Charles Duhigg, New York Times Magazine, 16 February 2012] Duhigg relates the following story:
“A man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation. ‘My daughter got this in the mail!’ he said. ‘She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?’ The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again. On the phone, though, the father was somewhat abashed. ‘I had a talk with my daughter,’ he said. ‘It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.'”
Duhigg points out that Target’s objective was get to shoppers to change their buying habits — something that it turns out is very hard to do. Duhigg explains:
“The process within our brains that creates habits is a three-step loop. First, there is a cue, a trigger that tells your brain to go into automatic mode and which habit to use. Then there is the routine, which can be physical or mental or emotional. Finally, there is a reward, which helps your brain figure out if this particular loop is worth remembering for the future. Over time, this loop — cue, routine, reward; cue, routine, reward — becomes more and more automatic. … Habits aren’t destiny — they can be ignored, changed or replaced. But it’s also true that once the loop is established and a habit emerges, your brain stops fully participating in decision-making. So unless you deliberately fight a habit — unless you find new cues and rewards — the old pattern will unfold automatically.”
In order for Target to get its customers to change their shopping habits (i.e., to do more shopping in Target stores), it needed to find a trigger that it could use. Duhigg explains more about the research that made Target look for pregnant women:
“In the 1980s, a team of researchers led by a U.C.L.A. professor named Alan Andreasen undertook a study of peoples’ most mundane purchases, like soap, toothpaste, trash bags and toilet paper. They learned that most shoppers paid almost no attention to how they bought these products, that the purchases occurred habitually, without any complex decision-making. Which meant it was hard for marketers, despite their displays and coupons and product promotions, to persuade shoppers to change. But when some customers were going through a major life event, like graduating from college or getting a new job or moving to a new town, their shopping habits became flexible in ways that were both predictable and potential gold mines for retailers. The study found that when someone marries, he or she is more likely to start buying a new type of coffee. When a couple move into a new house, they’re more apt to purchase a different kind of cereal. When they divorce, there’s an increased chance they’ll start buying different brands of beer. Consumers going through major life events often don’t notice, or care, that their shopping habits have shifted, but retailers notice, and they care quite a bit. At those unique moments, Andreasen wrote, customers are ‘vulnerable to intervention by marketers.’ In other words, a precisely timed advertisement, sent to a recent divorcee or new homebuyer, can change someone’s shopping patterns for years. And among life events, none are more important than the arrival of a baby. At that moment, new parents’ habits are more flexible than at almost any other time in their adult lives. If companies can identify pregnant shoppers, they can earn millions.”
I understand the privacy concerns that people have; but I also understand why retailers are relentless in wanting to know us better. As Duhigg writes:
“There is a calculus, it turns out, for mastering our subconscious urges. For companies like Target, the exhaustive rendering of our conscious and unconscious patterns into data sets and algorithms has revolutionized what they know about us and, therefore, how precisely they can sell.”
Hopefully, reasonable regulations will be worked out so that the concerns of both consumers and retailers can be addressed. Being able to target advertising saves retailers money and provides them with a much greater return on investment than other forms of advertising. Getting to know you through Big Data analysis helps more than just the retailers from which you buy. Big Data analytics can help suppliers ensure that the products you want are available at the right time, in the right location, in the right amount when you want or need them. You deserve to know what data is being collected from you. You should also know that that data is providing valuable information that can improve lives by providing new insights.
