Pricing Your Value — How Should You Think About The Value of Your Data In A Marketplace?
How much are you worth? Not from a traditional personal financial net worth perspective, but the digital you? We know that many technology companies make money by aggregating and selling user data. But how much do they make exactly, what’s the value of each record they sell?
Ironically, it’s a much easier question to answer on the Dark Web. A recent Experian blog post described the price criminals generally pay for certain data — with passports and medical records being worth up to $2000 each and an individual’s social security number selling for only $1! It’s not very much at an individual level, surprisingly. That’s because it’s not the data themselves that are valuable, it’s what the data allow the buyers to do.
For example, criminals pay more for passports because they can be used to move more freely than otherwise possible. In contrast, access to a worker’s corporate login credentials may not be worth much if that worker doesn’t have access to key internal applications. Similarly, when big tech companies practice “surveillance capitalism” — tracking you and then selling that tracking data — it’s what they do with the data that matters just as much as the fact that they captured and sold it. For example, in the recent examples of using individual data to influence elections, that data must be worth significant money.
Coming back to the original question but with a twist: how much are the data points that make up the digital you worth in a decentralized and user-controlled marketplace? Despite the fact that data are sold constantly today, there still isn’t a good answer to this question. We do know there are certain factors at play that determine some answers:
- Some data are worth more than others because of non-fungibility and usability. There won’t be just one price for one type of data, even if the data could be considered the same. For example, medical data for patients with some diseases could be worth more to pharmaceutical companies than data about commonly understood diseases because the former data could be used to create a more expensive drug. Similarly purchasing data from affluent consumers would be worth more than purchasing data from less wealthy individuals.
- How often the data get refreshed. Some data like tokenized college degrees will have value for specific limited purposes — such as when a new employer is doing a background check. However, neither one’s completed college degree nor date of graduation change in one’s lifetime. Other data like purchasing patterns or travel history change frequently and therefore have more use cases and will be bought more often.
- Value increases as the entire data set increases. Even in situations where one user’s data are highly desired, most data buyers won’t see value until they have a large set of data. Here the network effect is important. Using the medical data example again, the pharmaceutical company likely has no use for only one person’s data but an aggregated set of data from thousands of people with the same condition would be highly valuable.
- Even personally identifiable data may require sharing value with others. While we don’t like to admit it, even our most personal data are often not ours alone. Driver’s licenses are issued by governments and those governments have a say in how license numbers may be used. Similarly, to keep with our medical example, medical records are created by doctors and hospital staff so if a consumer agrees to be part of the anonymized data set that’s aggregated and sold, the individual may get a portion of the amount the pharmaceutical company pays because the hospital or doctor will also get a portion.
We know our data are valuable, but how much they’re worth is unclear. We are all still discovering what individual data are worth, something we’ll have more clarity on as data marketplaces evolve and market forces can go into effect.
For more information about PikcioChain,
Visit our website,
And join our community.
The PikcioChain Team