Digital Futures - Monetisation of Data

This case looks at data through the lens of competition law and asks what is the value of data and who is the data valuable to?

Lloyd v Google established that data is a valuable asset, but the Facebook case is more complicated as it has a two-sided market – the value of the data to the person using Facebook and the commercial value of that data to Facebook. They don’t necessarily hold the same value.

Data in the public domain

While many people are happy for their data, even personal data, to be in the public domain, the issue arises when a company is going to make a profit from that data. Are those same people happy to provide their data for free or should the company that is going to benefit from that data pay for it? This is the question that this case is trying to answer.

And that question is underpinned by a number of other questions. Can a Facebook user retrospectively claim value when their data is monetised? What value do you attribute to that data? Does a user’s perception of the value of their data affect its monetisation?

Taking four scenarios, at the top and the bottom you have polar opposites.

• A user thinks that their data is valuable but doesn’t feel that it equates to the return value they get from being on the site. The perceived data value here is very high.

• A user thinks that their data is a valuable asset but what they're getting in return for providing that data is the use of the Facebook platform. You pay with your data instead of a subscription fee. The perceived data value here is much lower.

And then in the middle you have users who don’t derive any value from their data or from Facebook or users who get some value from the Facebook platform, but don’t believe that equates to the same value that would be derived from their data.

How do you strike a balance between these different scenarios and create a methodology for valuing data which works across the board?

The economic modelling

With the case focusing on unfair terms & conditions and unfair trading conditions, which allow Facebook to abuse its dominant market position and extract excessive data without telling people what they are going to use that data for, the economic modelling looks to triangulate use perception evaluation with excessive profit.

This is very complicated due to the different value perceptions people place on data. A recent study in Germany showed that this could be as high as €150.00 per month, and there are new business models coming to the market that recognise and pay for people to contribute their data, but at the moment there are still issues around setting the price and the value people place on their own data. It is hoped that a judgement in this case will answer the question.

When considering the issue from a privacy perspective, the issue should be about protecting the fundamental rights of individuals rather than facilitate super profits, and this is where GDPR can support the Facebook case.

Regulation, privacy and protection

The regulators are very focused on this issue as the decision issued by the Irish State Data Protection Commissioner in respect of Facebook and Instagram’s privacy compliance demonstrates. The same underlying themes apply to the Facebook case and to every level of the supply chain across the digital ecosystem, from retailers using software to track consumers' spending habits and then translating that into personal digital advertising, through to the data that is harvested from EV charging infrastructure. The amount and level of data which is being captured emphasises that data has a value, whether that is commercial, economic or indeed both.

The acquisition of data is a significant driver and a theme we've seen in practice a lot over the last 10 years. In the context of mergers and acquisitions in the retail sector, the value of the businesses is not stock or physical assets but the businesses’ IP and customer database. A judgement in the Facebook case would help to attribute value to that data.

However, that means an increased level of protection is required. Take for example, the Bank of England who is looking at launching a new digital currency which would fill a current void, stopping other tech providers from launching their own digital currencies and in turn prevent data from being harvested for commercial value and sold on as business intelligence to retailers and others.

The monetisation of data is also linked to that very subjective view an individual has of their data. Striking a balance between users who are happy for their data to be monetised but expect a return, and those who see the return as the value in the service they are getting, will be difficult, and the Facebook case has a key role to play in achieving this.

Data models

Research is also taking place into data rights as a concept as compared to data as an asset. How can data be better commoditised in a way which is not just lawful but also ethical? Could a data trust model – which comprises of two key elements, legal agreements and a technology platform to collect, aggregate, protect and manage the data - be effective and if so, could this apply to data as an asset? The data trust model could enable the value of the asset i.e. the data and the data rights to be realised and then distributed to the beneficiaries of the trust at an appropriate time.

These are just some of the different perspectives being considered - alongside the commercial valuation there is compliance and the ethical question as well. And while the Facebook case still has a long way to go, regardless of the outcome, it is going to set a market precedent for future regulation.

The case raises three important areas for consideration. The question of public versus private enforcement and the modification that represents as part of a recent trend towards collective class actions, how competition law seems to be stepping in where consumer law has failed, and how the law vales data.

With cuts in funding diminishing the effectiveness of public sector enforcement bodies, there has been a move towards private enforcement and recognition. Class actions have emerged as the route to achieving the same underlying social goals that were originally achieved by strong public enforcement. The case against Facebook is part of this recent trend.

This is happening within competition law rather than consumer law. While there is the capacity under the Civil Procedure rules to bring class actions where claimants have the same interest, there is specific allowance made under Section 47B of the Competition Act to allow for collective proceedings which has enabled these cases to be brought.

One of the interesting things about the Facebook case is that this could lead to the Supreme Court ruling on the question of how do we assign value to someone's data which would set a new precedent and could led to new regulation in this area.

Data is valuable and this is demonstrated in the way it's used by a number of companies. For example, companies such as Google have put data at the core of their business model and they have been able to monetise it while still offering good services to users.

While putting a value on data is a complex subject, there are two characteristics that have a significant impact on the perceived value to the data owner and the value of that data to the company holding that data. While data is an intangible asset, it can still be considered an asset. The questions then are:

• What is the value of that asset?
• Who is at the negotiating table?
• What is going to be done with that data?

The methodologies

There are three traditional methods that economists usually use to value assets. The first is a cost-based method which looks at the cost to create something and what does it cost to replace it. The second is a market-based valuation which looks at data from previous transactions for similar asset classes. The third is a value-based methodology which considers data usage, the approval that each party has given during the negotiation for that data usage and associated revenues from that usage.

How to apply the methodology

In order to apply this methodology, you need to define two pieces of information. The first is the outcome. Is the outcome the revenue and profits that the company using the data could benefit from (or miss out on) or do additional third-party revenue streams need to be accounted for? Does a value need to be placed on the services the data owner is already receiving from the company who wants to use the data? These are questions that don’t have clear answers yet, hence the importance of the Facebook case.

The second piece of information is what is the fair share that the data giver should receive because their information has been used to create something? Does an associated increase in sales mean that they should receive a percentage of profits? Should the company get to keep 100% of those profits as they have created the algorithms? The answer to these questions could be a similar model to the one used by the music industry where artists have tracks sampled for media use. While this is something which needs further consideration it is likely that boundaries will become clearer as the Facebook case progresses.