The Economics of Data Businesses

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.

You should also try to add proprietary value of your own, lest either your suppliers or your customers encroach and disintermediate you. A sufficiently large transformation of your source data is tantamount to creating a new data product of your own.

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

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.

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.

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

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 use your data to power your customer acquisition, most notably with a data content loop. Data content loops can be simultaneously cheaper, more scalable and more defensible than most other go-to-market channels, as shown by Expedia, GlassDoor and Zillow.

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.

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.