What is the economic value of data?
We ask this question because the UK’s Treasury is asking it, to inform the development of a National Strategy for Data. If we get the answers wrong, we could spend many years in the doldrums. But if we get them right, huge new opportunities could be opened up.
Given all the kerfuffle there is about the importance of data in modern economies, its economic value, data as ‘the new oil’ and so on, you might think we would have a clear answer to this question by now. But we don’t, one of the reasons being confusion about terms.
Misleading assumptions
One common mistake is to confuse exchange value (how much money you can make by selling something) with use value (what it enables you to do). They’re very different things.
Take electricity. If I asked you ‘what is the economic value of electricity?’ one way of answering the question would be to look at how much money households and businesses spend on electricity each year. The resulting sum of money, you could say, is ‘the economic value’ of electricity because that’s how much people spend on it. But this money measure would miss all the economically important things electricity enables.
If we then looked at the myriad of activities and processes that electricity enables (heating, lighting, driving machinery and equipment and so on) we might end up saying: “electricity has such an enormous economic value that it’s virtually impossible to put a number on it”. After all where would we all be if electricity suddenly disappeared off the face of the earth?
Cost plus vs cost out
Confusion between use value and exchange value is easily compounded by the assumption the bigger the value of ‘the market’ for something is in monetary terms the more valuable it is. In fact, the opposite might be true. Take electricity again. In the UK, total consumption of electricity has fallen by around 9% over the last few years, with total spend on electricity falling with it.
But that’s a good thing, not a bad thing. Total consumption/spend on electricity is falling because energy efficiency is improving, so we need less energy to do the same things. For example, LED lighting uses around 75% less energy than traditional light bulbs. The fact that the amount of money spent on electricity has fallen doesn’t make electricity any less economically valuable. To the contrary, it was the falling cost of energy during the industrial revolution that made all the wonderful things it did possible: the lower the cost (and price) of an essential input becomes, the more it can be used to do a growing variety of things.
We’ve already seen this confusion between ‘market size’ and true economic value cause lasting damage in the so-called ‘identity market’. For decades big multinationals, start-ups and governments alike assumed that the ‘market’ for identity services would be big in monetary terms because the efficient, effective provision of such services is economically very important. In reality, as we’ve shown, the smaller ‘the market’ for identity services is (in pure monetary terms) the more valuable it becomes, because the more transactions it enables. In the identity sphere, verified attributes are the LED lighting of identity, enabling 75% or more cost reductions, to great benefit of all concerned (except those hoping to monetise it).
Key drivers of data value
Bearing these points in mind, what is the economic value of data?
If assessments of value that rely solely on monetary measures are both limited and potentially misleading, we need to focus on what data enables us to do; the possibilities it opens up.
Surprisingly, there’s very little common understanding or agreement on this. For example, in its Paper on the Economic Value of Data the Treasury focuses almost entirely on the potential value of ‘insight’ and ‘analytics’. But that’s less than half the story. Much less.
The economic value of data lies in four key areas of data intensive activity:
1) measuring and monitoring things. For example, if you want to lose weight you need to monitor key variables such as calorie intake and exercise. If you want good financial advice you need to know how much money you spend on what
2) insight and analytics — using the data that has been collected to identify patterns, spot trends and thereby deepen our understanding of cause and effect
3) using the above to help us make better decisions, which is economically vital because it helps us allocate available resources more efficiently and effectively
4) operations: Doing all the planning, organising, coordinating and aligning (e.g, resources to activities, supply to demand, etc), administering, applying, checking etc that we need to get stuff done; implementing the decisions we make more efficiently and effectively.
This fourth point — operational efficiency and effectiveness — is the Cinderella of the debate. Hardly anyone talks about it (perhaps because it is deemed mundane and boring and not sexy like ‘analytics’) but most of the costs incurred in the economy and the opportunities of improvement derive from it. It’s where value is finally delivered, where people actually ‘do stuff’ rather than just gaining ‘insights’. And it applies across the board: in every industry and every sector of the economy whether public, private or third/social sectors.
It’s what Mydex is currently doing in public services working on local clusters: focusing on the use value, not the exchange or market value of data, solving data logistics challenges in a flexible cost-out way that improves outcomes and reduces friction, effort, risk and cost for all stakeholders.
Unleashing the economic value of data
The Treasury’s assessment of the economic potential of data is a curate’s eggish.
On the plus side, it identifies one of the biggest areas of untapped potential. Under today’s status quo, most of the data that’s collected remains trapped and controlled by the organisations that originally collected it — who guard it jealously, seeing it as one of their most important assets, keys to competitive edge, and so on. But as long as this data is trapped in these organisational silos, the uses to which it can be put will be tightly constrained by the purposes and priorities of the particular organisation that controls it. For the true economic value of data to be unleashed we need far-reaching data portability.
On the negative side, it misses out on two key areas.
First, it focuses almost entirely on the economic potential of insight and analytics while almost completely ignoring opportunities for improved operational efficiencies, where order-of-magnitude cost reductions are now within our reach. Second, it takes today’s organisation-centric status quo as a given and assumes its continuation, thereby completely ignoring the possibility that individuals could collect, store and use their own data for their own purposes.
In doing so, it risks making two major policy errors.
First, by framing the economic opportunities of data in a highly particular way that effectively endorses and promotes the unique and special interests of just a tiny handful of US companies (e.g. Google, Facebook, Amazon, IBM, Microsoft etc), while overlooking the needs and interests of the rest of the UK economy (including the public and third sectors and citizens, participating directly in their own right, as well as most private sector firms large and small).
Second, by overlooking the potential for personal data empowerment it risks continuing a status quo were citizens are effectively excluded from participation in the data economy while blocking the development of a completely new industry sector and driver of economic growth: services that help individuals (as well as organisations) collect and use data to make better decisions and manage their lives better.
The practical policy implication of this oversight is to overlook one of the biggest immediate, practical opportunity that is just waiting to be seized: the development of a new personal data logistics infrastructure that actively includes citizens in the workings of the data economy.
Conclusion
The debate about the economic value of data is important. If a narrow, monetary perspective on value is allowed to prevail, policy decisions will focus on creating ‘markets’ where a tiny handful organisations prioritise monetisation of data assets whose potential is actually stifled. But if we take a broader view of data’s use value (improving all those processes of monitoring, insight, decision making and operational delivery for both organisations and individuals) vital new policy priorities emerge.
In personal data, data portability needs to be actively encouraged so that the full potential of data can be unleashed. A new infrastructure of data collection, storage and sharing (via personal data stores) needs to be built so that citizens are included rather excluded from the workings of the data economy. Only then policies will the full economic value of data be realised.
