The concept of a data-value exchange between individuals and organisations is surprisingly well established.
Every time a customer uses a retailer’s loyalty card, for instance, that exchange is taking place: that customer is being rewarded with cash (or more accurately with discounts) for sharing their data. The implicit understanding being that the data is used to target offers or derive other insights – and that the benefit of that activity is shared between the retailer and the customer.
Similarly, Facebook and Google offer free services, the use of which generate huge amounts of monetisable data.
However, if the concept is well established, its full potential is only beginning to be realised. Indeed, there’s every possibility an entirely new industry will emerge in the next year or two.
People are more aware than ever of the value of their data – as the digital generation grows up and data breach scandals break in mainstream media on an almost daily basis, there is now a populace informed and educated enough to understand the value of their personal information.
Secondly, there is simply more data: it’s an oft-repeated statement, but it is really worth revisiting. Streaming of media (audio, video), health assistants (Fitbit, Apple Health), networked vehicles and associated telematics data, and physical social networking groups (meetup.com) are all generating data based on either new behaviours or layering a digital blanket over a physical, chaotic world.
Thirdly, public pressure and likely emerging legislation means that this data is now more accessible than ever before, and will continue to open up further. Application Programming Interfaces (APIs) – the tools and routines for building software applications – allow permitted access to Spotify playlists, and to LinkedIn professional profiles.In Brazil, it is even possible for individuals to see their entire transaction behaviour for the whole year at stock keeping unit (SKU) level through a state-funded site and associated API.
The collision of these effects means that there is the possibility of making real money by acting as a broker to individuals’ aggregate data. Tangibly if a company can join or access Facebook feeds, IoT data, transactional behaviour and bank account data at an individual and permitted level – and harness this data for insight or targeting purposes – they can reward the individual with cash.
So, what are the barriers?
Firstly, there are mechanical ones. Recruiting a base of users large enough to be meaningful and useful is no easy ask – you are asking individuals to both trust you and to do some work for you (create an account etc.)
There are also conceptual barriers – why would an organisation (say: Sam’s Club) open data it could be monetising itself to a third party which could sell this to competitors? It’s a tough question that legislation may yet sort out (organisations will be forced to open data to individuals).
It is reasonable to believe that one of the companies will come to succeed – the idea is too good for it not to. And there are some polished players already out there: datacoup, people.io, Powr of You, CitizenMe (launching soon).
However, even as these companies form, there are questions they will need to solve if they are ever going to be as much of a staple of digital life as, say, Twitter.
Firstly, is the value exchange the same for everyone – do some individuals want cash back or privilege? Or recognition? The current "cash back" model is effectively transparent and simple but it does mean a skew to less affluent participants.
Secondly, there is a more difficult problem in evaluating the data. Current models are flat: consumers receive the same payment and businesses pay the same.
That raises tricky questions: if the data is being brokered for insight purposes, is the 1,000,001st individual worth as much as the 5,001st? Almost certainly not. And if the data is being brokered to allow the individual to be addressed with marketing media, is a lowly paid worker worth the same as a highly paid professional?
Again, almost certainly not.
Ultimately the so-called data locker companies will need to think about these questions as they scale – the flat model is too crude for the nuanced world they could be creating.
Jason Nathan is the group managing director for data at Dunnhumby.