Data Is Capital, Not Money

Capital and money might seem like the same thing, but they’re not. A lot of executives I talk to about data capital confuse the two — even MBAs! So, let’s clarify the difference between capital and money, and why it matters when it comes to data.

Take capital first. Capital, along with labor and land, is an economic factor of production in a good or service. If you don’t have enough of these basic inputs, you can’t make the thing or deliver the service you have in mind.

Greg Mankiw, professor of economics at Harvard, uses an apple-producing firm to illustrate these factors in his Principle of Economics, the gold standard for Econ 101 textbooks.

apples

Land is pretty easy to picture. It’s the apple orchard. The same for labor. It’s all the work that goes into tending the orchard, picking the apples, packaging them for sale, and so on. But capital is a bit harder to see. The capital of an apple farm includes ladders, tractors, and warehouses used in growing, harvesting, and packaging apples for sale.

In other words, capital is any produced good which is a necessary input for creating another good or service.

Financial capital is also a produced good. It’s not a natural resource. It has to be made somehow. Any you make it by selling your apples at a price above your costs. You can also increase your financial capital beyond what you can make yourself by borrowing it from someone who already has a whole lot of it, like a bank.

So, yes, a firm’s capital can include money. Money is a necessary input into most production processes. But money is different from all other kinds of capital, including capital equipment or data.

In order for something to be money, it must be both a store of value and a means of exchange. The Benjamin in your wallet (soon Tubman) is good at being money because 1) its value tends to stay pretty stable (a twenty will buy tomorrow pretty much what it buys today), and 2) you can exchange it for things you want more than twenty bucks in your pocket.

Anything with these characteristics can be money. There’s a story, made famous among economists by Milton Friedman, about the islands of Yap whose inhabitants used limestone discs for money. Some of the discs were huge, as big as 12 feet in diameter, and they were cut from the limestone on a nearby island. This is difficult to do, so the number of discs in circulation grew slowly which helped existing discs keep their value.

Yap

The discs were the recognized form of currency in the community, so you could buy things with them. But since they were so big, when you paid someone, the community simply recognized the change in ownership, and the disc stayed in your front yard or wherever you dropped it when you brought it home. The discs may not have been convenient, but they were money.

Data is different. To see how, consider a specific data set. Let’s say you have web browsing data on everyone in the richest zip code in the US (which is 10104, according to Experian) for the last year. What’s the value of this data? What’s it worth?

The fact that you immediately want to define its worth in terms of dollars, euros, or renminbi is the first tell. While the data may be valuable, it is not in itself a store of value. Its worth is what the market is willing to bear. It goes up or down depending on what potential buyers are willing to pay, like a house or a Van Gogh.

In addition to being a poor store of value, the data itself is not a unit of exchange. You can’t walk into a Starbucks and buy a latte with a megabyte of your one-percenter browsing data. You can’t pay for things with it.

One objection to this last point is that online we do, in fact, pay with data. We use Google and Facebook without making traditional payments. We get these services in exchange for our data. True, but that’s barter. Which is how you trade when you don’t have money.

The reason this distinction between data-as-capital and data-as-money matters is because of the harsh competitive reality of data.

Contrary to popular belief, data is not abundant. Data consists of countless scarce, even unique observations. If the competition digitizes and datafies interactions with your customers before you do, they get that data and you don’t. They can then create algorithms and analytical services you can’t. To fix this, you’d have to go back in time. And no amount of money can make that happen.

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