The Economics of Data Businesses

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.

The effort required to go from zero-to-one in data businesses is one reason they are so formidably defensible. It's also why ‘brute force’ remains one of the most popular strategies used by players in this game. A data product that can be built easily is a data product that can be replicated easily.

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

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.

In fact, typically, a successful new data product is not a variation on the existing data; it’s a brand new data asset from a completely different source, exploiting a completely different set of loops.

The marginal cost of acquiring data begins to decline. You begin to see economies of scale on the infrastructure side.

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