The value in data

Over the coming weeks, Bristows will be posting a series of articles looking at the value in data – different types of ‘value’, some monetary, some not, and different types of ‘data’ – and by no means only ‘personal data’, though this is certainly an important category. In our series, we’ll be trying to stay as broad and consider as many different aspects as possible.

What do we mean by ‘value’?

Even the concept of ‘value’ takes many forms, depending on the context. Take, for example, the work of the Zoological Society of London and its partnering with Google’s Cloud AutoML platform on a project to protect wild animals. By applying novel machine learning techniques to an existing ZSL data set, consisting of images automatically captured by camera traps in the wild, a picture of animal movement was developed, enabling conservation plans to be based on up-to-date migration information. Clearly, the data set enabling this work is valuable, but how should one ‘value’ trying to preserve an endangered species? In monetary terms, perhaps it could be based on the cost savings from being able to target interventions more effectively, perhaps. In non-monetary and environmental terms, however, which may be the better way to look at it, perhaps the value could be priceless.

The importance of context 

Research provides further examples of the importance of context to valuing data. Think for a moment of the enormous potential value ‘locked up’ in several of the large public sector data sets, say, those of the NHS or those produced as a byproduct of our transport ecosystem. Using this data for public good can raise some of the hardest questions regarding valuation, and it’s often necessary to include ethical and societal considerations in the mix too, as well as considerations of efficiency and cost effectiveness. Sometimes, even the same data used for the same purpose can attract different valuations, depending on the context and whose hands the data is in. Health data used by the public sector for therapeutic and research purposes may be valued differently to the same data in the private sector, where further opportunities to exploit the data commercially may also exist.

What does ‘data’ mean? 

We will consider this too, using various examples. At a high level though, we’re taking it to mean information recorded in any form about almost anything; personal data, of course, but also data about machines, companies, buildings, almost anything, including metadata (data about data) and inferred data (data produced by analysing other data). With so much computing power and storage capability available these days at relatively low cost, the scope of data that’s of interest has grown too, with the prevailing attitude sometimes becoming: surely there must be value in there somewhere, often leading to an approach of collecting everything ‘just in case’ and seeing what insights can be found later.

Monetising data

Where a monetary approach to valuing data is appropriate, the factors to be considered are fairly well-understood, though not always easy to apply. How ‘unique’ or ‘exclusive’ is the data in question. Is it easily recreated and at what cost? How current is the data? Does its value also depend on how up to date it is? Data can have a short shelf-life too as is often the case with, say, real-time stock market information. What can the data be used for? Are there restrictions on its utility, either legally (e.g. a licence or other contractual restrictions) or practically, for example, as a result of limitations in its accuracy. There are many other factors too.

Legal considerations that could impact the value in data

And just as several factors can contribute to value, many can reduce the value in data too. Of particular interest to us as lawyers are those that arise from regulatory or other legal considerations. Data privacy laws, competition laws, export control requirements may all impact the value in data. This could be by imposing specific restrictions on its use (e.g. data privacy) or impacting a company’s market power and the way it may use or share data (e.g. competition law). On top of this, there’s the possibility of contractual or licence restrictions, confidentiality obligations and intellectual property issues. In short, anything that can impact data utility or exclusivity can impact its value. At their most extreme, regulatory requirements, and the potential for liability arising from data (e.g. for non-compliance with regulatory obligations), can even turn data from a valuable asset into a toxic asset, where the more data that is held, the greater the potential liability.

By the end of this series, we hope you share our view that, while there are many things to think about, the legal challenges are not only interesting but set to become more important, particularly as the importance of data continues to grow in almost every sector of the economy.