Intelligent Enterprise Magazine - Decision Support: From the Lab

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                    October 26, 1999, Volume 2 Number 15
                                      
                            The Privacy Backlash
                                      
          Know your customers well... but don't make them paranoid
                                      
   I make my living as a data miner, and I can't help noticing that it
   makes a lot of people really nervous.
   
   An old college friend I hadn't seen in years, an artist and
   anthropologist, had only a fuzzy idea of what I do from seeing the Web
   site of Data Miners, the consulting company I founded, and was afraid
   I might be into Orwellian "1984 stuff."
   
   But later in the same conversation, she expressed the hope, only
   partly tongue-in-cheek, that my work will save her time at the video
   store; as soon as she enters the store, someone can simply hand her
   the tape that data mining predicts she will want, and she can go home
   happy.
   
   My friend's reaction was a typical mix of fear, misunderstanding, and
   willingness to see a bright side. I wish that clearing up the
   misunderstandings could alleviate the fears. Unfortunately, a clearer
   understanding of the power inherent in the com- bination of powerful
   data-mining tools and widely available personal data from many sources
   is likely to make people (and the people's representatives in
   Congress) more worried rather than less.
   
   Data mining can modestly improve our lives by helping our vendors and
   service providers better understand our needs. It can also be abused
   to invade our privacy. It won't take many examples of such abuse to
   raise an uproar leading to restrictive legislation likely to treat
   "good data mining" and "bad data mining" alike.
   
   The only way to avoid such a backlash is to educate industry and
   consumers alike how to determine what data mining applications are
   legitimate, and most important, to give people some control over how
   their personal data is used. After all, most people are happy to trade
   away some of their privacy for some other benefit. We accept having
   our picture taken by the ATM, because it makes us feel safer knowing
   that a robber would also be on camera. We accept having our luggage
   X-rayed, bomb-scanned, and ransacked at the airport, because we are
   glad a terrorist's luggage would get the same treatment.
   
   The public's acceptance of data mining requires that two conditions be
   met:
   
   o An explicit and well-understood covenant exists between those mining
   the data and the subjects of their analysis
   
   o Individuals feel they control the data they provide and how data
   miners can use it.
   
Benign Data Mining

   Most data-mining applications in marketing are benign. Despite all the
   rhetoric about "relationship marketing," no marketers are interested
   in you as a human being; they are interested in you as a potential
   customer. Information about you that sheds no light on your propensity
   to buy a certain product is simply not interesting to the vendor. Your
   age, income, sexual orientation, political affiliations, number of
   credit cards, and fondness for lima beans may all be things you choose
   not to share with your neighbors. But even personal information that
   would set the neighbors' tongues wagging for weeks on end does not
   interest the commercial data miner unless it can help predict whether
   you will order from a catalog or default on a loan, for example.
   
   Anyone really out to get you -- an ex-spouse or a collection agency,
   perhaps -- doesn't need data mining. The information that finds its
   way into marketing databases has always been available to those
   willing to look hard enough. In Massachusetts, where I live, I am free
   to check the registry of motor vehicles for an inconsiderately parked
   car's owner name and address. I can then walk over to the registry of
   deeds and see what his house is worth. And I can look up his number in
   the phone book if I want to call him to give him a piece of my mind.
   What is new with data mining is not the ability to determine who owns
   a particular car, but the ability to scan thousands of automobile
   registrations looking for patterns.
   
   Informed Consent. As consumers begin to realize the value of
   information about themselves and their habits, they will start
   charging for it. Many supermarkets already pay for this information.
   The ability to tie purchases to an individual is valuable enough that
   the store is willing to pay for it in the form of additional discounts
   offered to people who identify themselves each time they make a
   purchase. As long as the data is used only to figure out what coupons
   to offer which customers, and not to figure out who is eating too much
   fat, most people don't mind supplying the information and taking the
   discount. As consumers become more savvy, they will start expecting to
   be paid for their information in other situations as well.
   
   The Customer Rules. As long as companies are collecting data on you
   only because they want to sell you things, you are in control. If they
   misuse that information in ways that disturb you, you will be less
   likely to buy their products and services. That explains why MCI asks
   you who your friends and family are instead of just figuring it out
   for themselves and calling them up. Marketers' fear of upsetting you
   is the strongest protection you have against their misuse of your
   personal data.
   
Malign Data Mining

   Fear of offending consumers may stop some abuses, but not where real
   money is at stake. Recently, a supermarket defendant in a
   slip-and-fall suit used loyalty-card data to show that the plaintiff
   was a heavy drinker (or at least purchased a lot of alcohol). The suit
   was dropped. All of a sudden, many people who never gave much thought
   to what use their loyalty-card data might be put have started worrying
   about it.
   
   Similar worrying news stories are reported nearly every day. There was
   the wife who opened a telephone company's discount offer for calls to
   a frequently dialed number that her (now former) husband shouldn't
   have been calling. There were the drugstore customers alarmed to
   discover the reason they were getting direct-mail solicitations
   relevant to their illnesses was that a drugstore chain had sold its
   prescription data. The stories are numerous.
   
   It is easy to imagine worse scenarios. The analysis techniques that
   transform catalog order data into mailing-campaign targets could
   easily serve nefarious purposes. For instance, a "big brother"
   government might find data-mining techniques handy for compiling an
   enemy list. If we classify data-mining applications on a continuum on
   which a direct mailer deciding not to send you a sweepstakes entry is
   at one end, and a repressive state identifying you as a target for
   special persecution is at the other, many of them fall somewhere in
   the middle. How can we draw a line between the applications that ought
   to be tolerated or even welcomed and those that should be feared and
   outlawed? My answer is to evaluate each application on two scales:
   
   1. How close is the alignment between the people doing the data mining
   and the people whose data is being mined?
   
   2. What is the balance of power between the miners and the mined?
   
   Let's look at a few potential applications of data mining, keeping
   these two scales in mind.
   
   In the case of a consumer direct-marketing organization trying to
   reach the right customers, the interests of the miners and their
   targets are actually very closely aligned. Consumers do not want to
   get junk mail advertising products and services in which they have no
   interest. Similarly, the mailer has no interest in wasting postage on
   people who are unlikely to respond. Conversely, if the offer is one
   the consumer considers valuable, both the vendor and the consumer are
   pleased. As for power, it is all in the hands of the consumer, who is
   free to decide whether to respond to the offer.
   
   A more troubling prospect is the mining of medical records, credit
   card transactions, supermarket purchase records, or lifestyle data in
   order to assign risks for various ailments to individuals or
   subpopulations. How well the miner's interests align with those of the
   mined depends greatly on the nature of the healthcare system. Most of
   the developed world accepts that society as a whole benefits from, and
   is responsible for, maintaining a healthy population.
   
   In most wealthy countries, this understanding has led to the creation
   of single-payer healthcare systems in which every citizen is
   automatically covered. The interests of such a system and of the
   individual are reasonably compatible. The healthcare system saves
   money by preventing people from becoming ill and by getting them early
   treatment when they are in need. Because people tend to prefer being
   healthy to being sick, they have no particular reason to withhold
   information that may help in their diagnosis or treatment. Power is
   balanced between the miner and the mined. The healthcare system has
   the power to decide which treatments to pursue, but it does not have
   the power to refuse coverage.
   
   In the United States, the situation is quite different. Healthcare is
   usually financed through myriad for-profit insurance companies. These
   companies can save money and increase their profitability by refusing
   to cover people who are at greater risk of becoming ill. Here, the
   interests of the individual and the provider are at odds. The sicker I
   am, the more I want healthcare and the less inclined the insurer is to
   provide it. Furthermore, in the U.S. system, the power resides totally
   with the insurer, which can approve or deny coverage. Thus, while I
   might look with indifference on a project to use data mining for
   medical risk assessment in Canada or Europe, I would regard a similar
   United States program with alarm.
   
   In fact, medical records are already accorded a higher level of
   protection than most data. But what if non-medical data were used for
   the same purpose? Although I do not object to the supermarket using my
   purchasing patterns to determine which coupons to issue me, I would
   feel very differently about the supermarket data being used by an
   insurance company to determine my risk for heart disease. And yet,
   premiums are already higher for cigarette smokers, so why not for
   people who purchase a lot of beef and sour cream?
   
   Similar questions about data misuse come up with automatic
   toll-payment systems (who is interested in where you are and when?),
   telephone records (why do they want to know who your friends and
   family are?), and even magazine subscriptions, catalog orders, or Web
   site visits.
   
   Data mining is a powerful tool. Like any tool, it can be used for ill.
   As our information society matures, we will have to develop new laws
   and conventions to cope with the new methods of manipulating
   information. We should not rush to regulate harmless data mining, but
   the best regulations, where necessary, will be those that best balance
   power and support mutuality of goals between data miners and
   individuals.
   
   Michael J. A. Berry, founder and principal of Data Miners, is
   co-creator of the Decision-Support Systems Laboratory (www.dsslab.com)
   in Cambridge, Mass. You can reach him at mjab@dsslab.com.
   
   
   
   
   
   Copyright © 1999 CMP Media Inc. ALL RIGHTS RESERVED
   No Reproduction without permission
   
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