Where’s the Data Profit, Lebowski? Episode One Materials (Category #Data)

I've collected the materials from the first episode of “Where's the Data Profit, Lebowski?” The livestream took place on September 7, 2026. Andrey Tsybin, Nikolay Golov, and I discussed turning data into money, starting with a familiar request: we have accumulated plenty of data, so let's earn something from it. The size of a warehouse, however, tells us little about who would pay for its contents. By the way, the project has its own dedicated site, prodata.tech, and the next episode should arrive in about a week.

We approached the question through product analytics and experiments, data-platform architecture, and engineering leadership. We also disagreed about how much value survives when you remove detail.

We discussed:

• Three paths to revenue or savings. Sell data externally, improve internal decisions, or build a product around it. We introduced all three, then spent most of this first episode on external sales.
• The buyer and the task. Who needs this particular dataset, and what could they do with it? Andrey emphasizes coverage, representativeness, and stable collection: a rising metric may reflect broader observation rather than a growing market.
• Copies, rights, and de-identification. Nikolay examines collection purposes and subsequent use; we also discuss re-identification through events and routes. The legal examples raise questions to examine for an actual transaction. They do not establish permission to sell data simply by removing names.
• Aggregated analytics. Strava and X5 supplier analytics prompted a debate about the price of detail. Nikolay and I discussed how losing detail can reduce value; Andrey argued that a reliable answer to a buyer's question can be a product in its own right.
• Costs beyond the export. Preparation, support, protection, copying risk, and sharing a competitive advantage with the buyer. An invoice for the first delivery does not tell us whether the whole undertaking benefits the company.

Episode materials: 📌 Episode page with timestamps 📖 Three paths from data to money — a separate treatment of the topic with cases and diagrams that I prepared for the episode 🎬 YouTube recording 📝 Edited conversation recap

If you have tried monetizing data, which part proved hardest: finding a buyer, preparing a useful product, or figuring out what remained after all the costs?

#Data #Product #Analytics #Architecture #Management