Methods to determine the right price for your dataset

This article is the second in a two-part series on data pricing. You can find Part I here.

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Prep your data for sale on the Marketplace. Photo by Benjamin Wong on Unsplash

Following up on our core ideas from the previous article, let’s start by looking at simple techniques to estimate the price for a data asset. The more advanced economic mechanisms, which will be introduced in the next (and final) article, can open up new possibilities for data markets and can be very useful to let the market signal the price an asset should have — but they cannot apply in every scenario.

Considering we are in a nascent period of data markets…

An introduction to the complex problem of pricing information goods.

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Photo by Alexander Mils on Unsplash


Imagine you own Dataset X, and you’re trying to sell it.

Party A needs some of this data as a crucial component to launching a new business operation, so they’re willing to pay $10,000 to access to it. However, it turns out that they only need data from the past 3 years, so you offer them a discount at $8,000.

Party B comes along needing this same data, but it’s only something that will offer them a slight competitive advantage. They’re willing to buy access to the full data for $1,000. …

A story of prices and social scalability

Few topics in the cryptocurrency world are as passionately debated as the question of value capture and price. Which tokens will appreciate in value, and which will be worthless? Who will be the winners? How and why will this happen? These questions drive investment theses, public attention, crypto-valuation theories, and even determine which projects people will want to get involved with. Fat protocols, hyperbitcoinization, staking, work tokens, on-chain cash flows — you name it.

Yet, one aspect has been overlooked so far: why does value capture matter at all? The answer may seem blindingly obvious at first, but I believe…

Erwin Kuhn

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