The Economics of Data Businesses

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.

Adding to the problem is the fact that almost all data products have a ‘minimum viable corpus’ — a size below which the data simply isn't useful. This parallels the concept of a minimum viable product in software, but an MVC is usually much harder to build than an MVP.

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.)

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.

An expanding corpus also expands your audience: for example, there are many more buyers for data covering 50 US states or 10,000 public stocks than for data covering 10 states or 200 stocks.

Dun & Bradstreet (NYSE:DNB) is 180 years old. No business survives that long (two world wars and a civil war, recessions and depressions, market booms and busts, multiple technological revolutions, good and bad management, you name it) without an amazing structural moat. That’s the power of data businesses.

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...  The classic mistake people make is to see this and jump to the conclusion that early-stage data businesses don't work and will never work. But that's a... See more

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.