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One Transaction Generates Data to Feed Multitudes

Last week I jumped from the starting point of the newest U.S. anti-trust action against Google into a discussion about the legal and economic status of data. I would like to carry the discussion of data further.

To briefly recap: data is history (it describes someone at a given time or it describes something that happened at a given time); history is not subject to ownership by anyone; and while we have some laws restricting use or financial exploitation of the information, generally anyone who can figure out how to use data legally will be allowed to use it productively. Data is considered to be a commodity by many, including the states AGs who just sued Google. Is this more like harnessing the wind, drilling for oil, issuing securities, or cultivating crops? We are still figuring out the appropriate analogies, and the correct analogy may depend on the type of data collected and how it is used.

What do I mean when I say anyone can use data productively? In the U.S., except for limited restrictions, many parties can claim control and use of the descriptions of a transaction. For example, if Anita purchased a pair of slippers from a local store to be shipped to her house, and she paid with the store’s online application accessed from her smartphone, a dozen parties have claims on some or all of that information. Under current law, all of these parties could use the information to effectuate the transaction, and likely for internal purposes, and some could transfer that data to others in most circumstances.

Anita thinks this data is hers because it describes her transaction, and California and the EU give her some rights to limit how the information is shared. But there are many more “first parties” who feel they were part of the transaction and will keep/use records of it. Who would collect data from this transaction?

The store she purchased the slippers from, of course, maintains this data as its own sales record.  But also that store’s merchant bank, it’s payment processing company and probably the shipping company used to deliver the goods.  By the way, the shipping company could actually be an entire series of primary shippers, fulfillment coordinators, warehouse operators, and trucking or delivery contractors, all of whom now have Anita’s name, address and probably know what was shipped to her house. They may work for one company or they may represent several separate entities. The store may have a special purchase points discount program with an outside marketing firm managing this program and keeping Anita’s information in their databases.

The store’s online presence is likely monitored by Google Analytics or a similar data company. If Anita came to the store through an online advertisement, then the site hosting the ad, the company managing the ad buy, and the ad placement network would likely have detailed information on Anita’s purchase and may receive funds from the clickthrough and/or the purchase. There could be several variations of this set up which could include other parties receiving Anita’s information.

But Anita’s side of the transaction also creates interested parties. Since she found and purchased the slippers over her phone, the company that operates the application she used will capture all of the transaction data. So may the company that provides the core software for the phone and allowed the app to be downloaded – likely Apple, Google, or Samsung. The phone company that connected the transaction – Verizon, T-Mobile, or AT&T – may collect information about the transaction too.  All of these companies can include location data of when and where the transaction was actually completed and may charge to pass this data to either the company’s mentioned on the retailer’s side of the transaction, third parties interested in this transaction or to data aggregators.

And, of course, Anita needs to pay for the slippers, so her bank will keep the data, and so will the company sponsoring the payment application she used – Venmo, PayPal, MasterCard, Visa, AmEx. All of these financial companies think of themselves as data companies now and make significant money packaging up the data about all of our transactions, analyzing them with machine learning programs, and selling the information – aggregated or otherwise – to anyone who might be interested. Some of these companies will package up the names of everyone who bought slippers or footwear over the past month and sell contact with these people to other retailers who want to find live slipper-buyers.  Maybe a retailer’s analytics show that people who just purchased slippers will soon purchase sweatpants or a robe, or even cocoa mix and marshmallows, so they want to send out a coupon when they know a buyer is ready. And, as with the shipping companies, there are lots of business structures to serve these markets, with financial processors and marketing consultants and data analytic specialists, so the number of companies in the chain is likely higher than you might think.

Nearly all of the companies I just mentioned have a first-degree relationship to the transaction – the company performed a service that most people would recognize as part of that transaction. As you move further out along the chain to second and third-degree relationships, or companies that were not involved at all in making the introduction, the sale, the payment, or the delivery, you still find people making their living off of the data generated by Anita’s purchase of slippers.

I describe this transaction and the data it generates to help explain why the attention economy is complex and why it is difficult for Anita to say “the information about my purchase belongs to me, and not to anyone else.” Not only is history, not something that one person can own, but a dozen parties have a legitimate claim to that same sliver of history, and dozens more are likely making use of it in an intricate data-focused economy.

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