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The economics of the Digital Transformation – II

There is not such a thing as “value of data”. This value depends on who is looking for those data, what they will be used, what is the context of those data, the timeliness of the data and so on. Most likely, data are most valuable when they are used for illegal purposes. In the graphic the estimated value of data in cybercrime. Image credit: VPN Mentor

The Digital Transformation moves activities and processes to the cyberspace, hence it shifts the focus to data. Data are being used in the cyberspace assembly line to create value and these data are the raw material that will be converted into services or products using a variety of transducers (like CAM – Computer Aided Manufacturing).

Because of their association to “raw material” one hears people saying that “data is the oil of the XXI century”. From a perceptual point of view that is fine, however it is very wrong and most importantly it is misleading, since it conveys the idea that as in the last century economy revolved around oil now it revolves around data applying the same rules of the game. This is not the case.

  1. Oil is rooted in the “atoms” economy, the economy of scarcity. If I have a barrel of oil and I give it to you, you got it but I no longer have it. On the contrary, if I have a GB of data and I give them to you, you got them, but I still have them. We are in the economy of abundance.
  2. You cannot distinguish (in economic terms) a barrel of oil from another barrel of oil -of the same grade- (economists would say it is fungible). However, data have different values depending on what they are, what is their specific use at a specific time….
  3. Producing one barrel more of oil has a cost (positive marginal cost), producing one extra GB of data (basically) has no extra cost. In what economists call the “perfect competition” the value of a product is equal to its marginal cost (that is because competition shrinks the margin, a perfect competition will reduce the margin to zero, hence the product cost will be its marginal cost). A barrel of petrol will cost you its marginal cost (somewhere between 6$ to 22$, extraction cost varies), so will data: you can get data for free, because their marginal cost is zero!
  4. When you use a barrel of oil you get a return. That return stays the same as you use a second barrel of oil and so on: it has constant return on scale. On the contrary, data tend to have a diminished return, the more data you are funnelling the less valuable the additional data becomes (this is becoming even more so as data are used to train AI algorithms. The more data you provide the more accurate the result but as you provide more data the accuracy increment decreases). If you provide 1,000 movies they have a certain value X, if you provide 10,000 movies the value does not scale, it increases by a small percentage that gets smaller and smaller as more and more content is made available.
  5. Moving oil around cost, hence you tend to distribute the stock to decrease transport. On the contrary moving data around doesn’t cost anything (at least the cost is not borne by those accessing the data, you don’t even know where data are actually stored). This fosters consolidation and aggregation in the business of data, and indeed there are a few huge data aggregators.
  6. Oil has a price (it oscillates depending on market condition), data is basically worthless, unless it is considered in an envelope of usage. It is the usage that makes the difference, that is not the case of oil. So how much data value can be depending on the use? In the graphic one can see the value attributed to data when retrieved illegally (and most likely for illegal use). A stolen Netflix password may have a 3$ value (dirty cheap!), a Spotify password just 2.8$. The codes to access your bank account have a value that depends on the amount of the account, a 2,000$ account access code may be worth 100$, a 15,000$ account access code may fetch 1,000$.

There is much more to the economy of data, and it will be explored in the following posts. What should be clear is that one cannot compare data to oil in terms of economic value and moreover that the value measuring stick for one does not work for the other.

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the Industry Advisory Board within the Future Directions Committee and co-chairs the Digital Reality Initiative. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.