Turning Data into Money: What It Means and Which Options Exist
Episode hosts
What we discussed on the recording
The first episode starts with a familiar request: a company has accumulated a lot of data and wants to earn money by selling it. Andrey Tsybin, Nikolay Golov, and Alexander Polomodov distinguish three routes: external sales, better internal decisions, and products built around data. Volume alone says little about price; buyers need information that serves a specific use case.
Most of the conversation examines external sales. Nikolay separates possession of a copy from rights to use it, drawing attention to collection purposes, user consent, and chains of processors. The hosts discuss re-identification: removing names or addresses does not necessarily sever the connection between behavioral events and a person. Their legal examples illustrate participants’ concerns about risk, rather than providing universal rules for a transaction.
Andrey proposes aggregated analytics as an alternative to raw records. Strava and X5 supplier analytics move the discussion toward product value: which question the buyer needs answered, whether the data represents the relevant market, and whether collection remains stable. Nikolay and Alexander argue that losing detail can reduce the price; Andrey counters that quality, coverage, and trust can make an analytical product valuable.
The final discussion assembles the economics of a sale: preparation, protection, maintenance, copying risk, and the opportunity cost of sharing a competitive advantage. An existing platform helps technically, although large organizations may be slow to approve a new offering. The next conversation is intended to examine internal use: which decisions change after someone reads a report, and how to demonstrate their financial impact.