Over the next few weeks, Bristows will publish a series of articles on the value of data – different types of “value”, some monetary, some not, and different types of “data” – and by no means just “data”. personal”. , although this is certainly an important category. In our series we will try to keep it 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 partnership with Google’s AutoML cloud platform on a wildlife protection project. By applying new machine learning techniques to an existing ZSL dataset, consisting of images automatically captured by camera traps in the wild, an image of animal movement was developed, allowing conservation plans to be based on up-to-date migration information. Obviously, the dataset enabling this work is valuable, but how should one “value” trying to preserve an endangered species? In monetary terms, this could perhaps be based on the cost savings of being able to target interventions more effectively, perhaps. In non-monetary and environmental terms, however, which may be the best way to look at it, perhaps the value could be priceless.
The importance of context
Research provides other examples of the importance of context in evaluating data. Think for a moment of the huge potential value ‘locked up’ in many of the large public sector datasets, for example those of the NHS or those produced as a by-product of our transport ecosystem. Using this data for the public good can raise some of the most difficult questions about evaluation, and it is often necessary to also include ethical and societal considerations in the mix, as well as considerations of efficacy and profitability. Sometimes even the same data used for the same purpose can be assessed differently, depending on the context and the person in the hands of the data. Health data used by the public sector for therapeutic and research purposes may be valued differently from the same data in the private sector, where other opportunities for commercial exploitation of data may also exist.
What does “data” mean?
We will also examine it with the help of various examples. However, at a high level, we mean recorded information in any form about almost anything; personal data, of course, but also data about machines, companies, buildings, almost everything, including metadata (data about data) and inferred data (data produced by analyzing other data). With so much computing power and storage capacity available these days at relatively low cost, the scope of interesting data has also widened, with the prevailing attitude sometimes becoming: surely there must be value in it somewhereoften leading to an approach of collecting everything “just in case” and seeing what information can be found later.
When a monetary approach to valuing data is appropriate, the factors to consider are fairly well understood, but not always easy to apply. How “unique” or “proprietary” is the data in question. Is it easy to recreate and at what cost? How current is the data? Does its value also depend on its topicality? Data can also have a short lifespan, as is often the case with, for example, real-time stock market information. What can the data be used for? Are there any restrictions on its usefulness, either legally (eg license or other contractual restrictions) or practically, for example, due to limitations in its accuracy. There are also many other factors.
Legal considerations that may affect the value of data
And just as many factors can contribute to value, many can also reduce the value of data. As lawyers, we are particularly interested in those that arise from regulatory or other legal considerations. Data privacy laws, competition laws, export control requirements can all impact the value of data. This can be done by imposing specific restrictions on its use (for example, data privacy) or by affecting a company’s market power and how it can use or share data (for example, the right to competition). In addition to this, there is the possibility of contractual or licensing restrictions, confidentiality obligations and intellectual property issues. In short, anything that can impact the usefulness or exclusivity of data can impact its value. At their extreme, regulatory requirements and the potential for liability arising from data (for example, in the event of non-compliance with regulatory obligations) can even turn data from a valuable asset to a toxic asset, where the more data held, the greater the potential liability.
By the end of this series, we hope you’ll share our view that while there’s a lot to think about, legal challenges are not only interesting but bound to become more important, especially more so as the importance of data continues to grow in almost every sector of the economy.