You can charge recurring revenue; after all, nobody wants to work with obsolete data. (This is harder in the early days, not because of lack of buyer appetite, but because your update cadence probably isn’t good enough.)
The second fundamental truth of data business models is this: whoever controls the data, captures the value. Intermediaries get squeezed. A common failure mode is to build a business on top of somebody else’s data. If you depend on a single upstream source for your data inputs, they can simply raise prices until they capture all of the econom... See more
Despite the fact that many of the largest and most dominant tech firms in the world are data businesses, there are not many resources on the what, how and why of this business model.
Successful data businesses are all built around a unique or proprietary data asset. There are a few ways to build such an asset:- Brute force License and transformCore business outputPayment in kindInbound network effectsGive to get Data exhaust
Everything starts slower on the data side. Building a valuable data asset takes time. Building the supporting infrastructure to actually deliver that data takes time. Sales cycles take time.
Google, Bloomberg, Yelp, and ZoomInfo are all data businesses. They acquire their data in different ways, and they generate revenue from that data in different ways. But for all these companies, data is the fundamental unit of value creation.
These tactics interact. Sometimes the very act of merging multiple datasets adds substantial value. Joining data correctly is hard! Other non-glamorous ways to add value include quality control, labelling and mapping, deduping, provenancing, and imposing data hygiene.
A common failure mode is to build a business on top of somebody else’s data. If you depend on a single upstream source for your data inputs, they can simply raise prices until they capture all of the economics of your product. That’s a losing proposition.