Find data sources with untapped value. Public records, fragmented APIs, manual workflows ripe for automation.
Build reliable pipelines that pull data from the source. Handle authentication, pagination, rate limits, and edge cases.
Raw data is rarely useful alone. Cross-reference, validate, clean, and enhance. Add context that increases value.
Package into formats customers can use. Automate delivery. Build portals for self-serve. Collect recurring payments.
Manual first. Get someone to pay for a spreadsheet before you automate anything. Most data product ideas die at first contact with customers.
Stale data is a commodity. Fresh data is valuable. The faster you can get information from source to customer, the more you can charge.
Anyone can scrape. The value is in what you add — cross-referencing, validation, derived insights. That's what makes your data defensible.
Your time should go into finding opportunities and talking to customers — not copying data between spreadsheets. Automate everything repeatable.
One data type. One geography. One customer segment. Nail it, then expand. Broad products are hard to sell and harder to maintain.
Subscriptions compound. One-time sales don't. Design for recurring delivery and recurring revenue from day one.
Discovery to delivery in 2-4 weeks for most data products. We move fast because we validate before we build and use tools that don't require months of setup.
Ongoing maintenance is minimal once the pipeline is running — mostly monitoring and occasional source changes.