The question is tricky: it appears that most of us already surrender our personal data in exchange for services that we access online. Whether you are a user of Facebook, Gmail, Pinterest, CouchSurfing, Foursquare or any social network, the deal is straightforward: you can use a service in exchange for your data. The relation between the actual revenue that rises from the selling of mined datasets and an individual’s data seems not to be so directly correlated (arguably, the sum of all parts does not account for the total of individuals’ data aggregated and mined.)
That being said, a few attempts have been made to try and broadly assess the value of an individual’s dataset. But before getting into these attempts in greater detail, let’s figure out what kind of data we are really talking about here.
Most of the online service providers are not interested in your own attributable data. They are interested in the ways they can infer future users’ preferences and behaviour from correlations drawn from big datasets.
Your personal data are made of numerous data sources that somehow constitute your “online identity”. Whether these data are “volunteered”, “observed” or “inferred”, they mainly gather: identifying data (e.g. name, email address), sensitive identifying data (e.g. social security number), demographic data ( e.g. age, race), court and public record data (e.g. voting registration, marriage licence), home and neighbourhood data (e.g. home listing price), social media and technology data (e.g. friends connection, type of media posted), vehicle data (soon to be connected objects data), health data (e.g. over the counter subscription, tobacco usage), financial data (e.g. credit worthiness, investment interests), travel data (e.g. frequent flyer information), general interest data (e.g. charitable giving, gambling history), purchase behaviour data (e.g. amount spent on goods).
The power lies in the correlations that can be drawn from these different data coming from different sources. Most of the online service providers are not interested in your own attributable data. They are interested in the ways they can infer future users’ preferences and behaviour from correlations drawn from big datasets. Through powerful algorithms, data-brokers, tech firms have the capacity to enrich the knowledge that they have on their customers and/or users’ behaviour. This information can be used as a commodity, therefore, priced and sold.
The pricing strategies are quite opaque as most are run through trade deals and their attached privacy.
Some efforts are made, sometimes in surprising ways, to try and assess an individual’s data worth. What is shown in this video is that the sum of all the information gathered by Facebook amount to $12 per user. Another way of seeing it is by dividing the revenue of Facebook by its user base. Alternatively, the Guardian reported that Datacoup was remunerating personal data sharing for about £8 a month, that Federico Zannier managed to make $2 733 on Kickstarter to share its keystroke, mouse movements and screenshots of his activities. Luth Research’s “ZQ Intelligence” service also tracks smartphone and tablet activity for a payment of $100 a month to 25 000 opt-in users. While looking at the FT “personal data calculator” it seems that, except if you are a public figure, $0.080 is the rough cost of an individual’s data as one set in a bundle of 1,000.
All in all, the pricing of datasets has yet to stabilized and questions about the remuneration of individual producers is yet to be fully investigated. What seems to be obvious today is that there is a tension between the value that an individual gives to his own personal data (privacy is an example) and the still volatile market price attached to it. However, it seems that we will most probably not be able to rely on our data production to finance our retirement.
Morgane is working with a mobile app design agency AbsolutLabs. See more on www.absolutlabs.com.