Data as Currency in Media Transactions – A Note from the Field
Based on personal experience, I can tell you that the best way to rile up a privacy/data protection lawyer is to talk about the best ways to use and trade personal data as a commodity. To be fair, our (top-notch, award-winning, industry-leading) Privacy, Security and Information team do exactly what they are meant to do, which is to ensure that clients follow privacy law and what that law takes as its higher, near-inviolable purpose: the right of individuals to keep their private information just that – private.
But because our laws promote this ideal doesn't make the use of personal data evil, yet the focus on protecting personal data as a higher moral imperative leads most people to talk about using personal data (even when permitted by law) in hushed tones. But this isn't right – and it denies the fact that consumers are usually willing to share data in exchange for an enhanced entertainment experience. So what we hear every day is the simple question – how can I use the data consumers have allowed me to use, and what do I do when I don't have enough information to use that data to its fullest potential?
Let's start with AI…
Artificial intelligence and machine learning, like all media research tools, create models using data from which inferences can be drawn to predict future viewing patterns, appealing story lines, demographic trends and the like. Traditional methods of research and analysis, whether human or automated, took input data and applied algorithms to that data to create predictions. AI and machine learning take this one step further by allowing the algorithms themselves to be modified and improved upon based on prior results and feedback as to their relevance and accuracy.
Being able to make enhanced conclusions using AI allows media outlets to provide viewers with a more personal experience and is of course a growing trend. But, it relies on 'buy in' from consumers – it relies on them providing you with the data you require to be able to create that personalised experience. In this way consumers are the gatekeepers and you can only rely on data they are willing to provide. Whilst certain information can likely be inferred, the more information I, as a consumer, provide the more accurate and tailored experience I will receive and with a decreased need for prediction.
For companies that have partners who have consent from consumers to share their data, this relationship will prove invaluable for building on a company's own collected data, as the wider the pool of information to 'input', the more accurate the outcome.
Using data as a commodity
Data has been a commodity for as long as there has been data – certainly for much longer than there have been computers to automate its collection – and I think that we as transactional media lawyers fail our clients if we don't ask how data fits into a commercial deal done in 2017. For example, I've said for a long time that in the data economy (see what I did there?), a good cross-promotional deal is one in which it's hard to tell which party paid cash to the other, because there are so many other currencies, including data, at play - and that aren't traded for as often as they should be.
In the media, entertainment and sports industries that I work in, research and analysis has always been critical to ensuring that the right content – and the most relevant advertisements – are provided to each viewer or fan. It's safe to say that the use of data as a commodity has grown despite the concomitant growth of privacy regulation – and there is no suggestion that the legal use of data will slow any time soon or be any more harmful to consumers.
And, as importantly, consumers see data as a commodity to be used for advantage just as much as the companies that use data do. In a study published by Salesforce earlier this year, over 60% of UK consumers said that personalized offers have a direct influence on brand loyalty, and 55% of UK consumers felt that sharing personal information was a reasonable price for getting those personalized offers.
As mentioned above, the more data that is collected either directly from consumers or through partnership relationships, the wider the pool of information to input into the process, the more connections can be made and the more personal the result.
So the media industry stands to miss a trick if it doesn't go one step further and create and embrace a new concept: "data trusts."
The UK Government's new report "Growing the Artificial Intelligence Industry in the UK" provides 18 recommendations, the most important of which is for industry and government to work together to create "data trusts". They would work as a network of contractual relationships that allow for data to be shared in a "fair, safe and equitable way."
This could in fact reflect the model of open source software which relies on access to the software via and in accordance with a licence. For data trusts, different pots of data could have different licence rules. For example one data trust may only permit use in a certain territory, others could limit use to a specific sector. In this way individual industries such as the media industry could have their own set of data trusts to allow for efficient and effective use of data.
If a company needed access to a particular data set in order to complete its analysis for a customer, by participating in a data trust it would know the terms applicable to that use and would not be delayed by the need to negotiate those terms with the source or provider of the data set in question. Resources otherwise expended in administering those relationships could, instead, be deployed to develop and enhance the quality and relevance of available data.
And the better the data, the more accurate the results – which means better research, and better storylines, and ultimately, better entertainment.