The Economics 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

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 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

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.

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.

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.

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.)

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.

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