Any port in a storm. All press is good press. When life gives you lemons, make lemonade. Humans are obsessed with metaphors. We use them constantly. I even used one in the title “set the record straight.”

Honestly, who can resist a juicy opportunity to use a metaphor? The tech industry definitely cannot.

For many years, the Internet was likened to the California Gold Rush. Hopeful men and women heading west in search of a massive payload (most of them coming home empty-handed). But that’s about as deep as that analogy went.

Jeff Bezos once did a TED Talk entirely about setting the record straight on this prominent analogy. Bezos made a far more compelling case that the Internet was like the invention of electricity:

As a learning aid, metaphors are extremely useful. They help us understand something that’s new and unfamiliar by using something that’s familiar and better understood. But metaphors can also backfire. When they over-simply [what] they intend to describe, they can lead to the wrong conclusion.
Itamar Golminz, Medium

Capturing data and using it to improve your business is something that every decision-maker is thinking about. And when we generally address data, we use the analogy: Data is the New Oil.

But is this analogy misleading? Does this mode of thinking mislead our strategy?

If you type that term into a Google Search query, you’ll be a bit dumbfounded. Half of the titles will say, “Data is the New Oil” while the other half say “Why Data is Not The New Oil.”

It’s actually quite comical. So what are the arguments for both sides?

Data is the New Oil

The comparison of data and oil stems from these ideas:

Data is the new oil. [Oil is] valuable, but if unrefined it cannot really be used. It [oil] has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value. – Clive Humby
Oil fires the engines of the industrial economy. Data fires the engines of the information economy.
Most of the world’s oil is controlled by a few entities. Most of the world’s data is controlled by a few entities.
Oil is the fuel for transportation today. Data is the fuel for transportation tomorrow (self-driving cars).
Oil fields are out in nature, waiting to be harvested. Data exists in the ether, waiting to be harvested with technology.

Data is Not the New Oil

Mostly concerned that this analogy will mislead policy-makers to regulate data like a commodity (oil), Alec Stapp makes a coherent argument against the analogy:

Oil is rivalrous; data is non-rivalrous – If someone uses a barrel of oil, it can’t be consumed again. When consumers ‘pay with data’ to access a website, they still have the same amount of data after the transaction as before.
Oil is excludable; data is non-excludable – Oil is physical, so it can be stored in ways that competition cannot access it. While databases are proprietary, the underlying data usually is not. Firms struggle to prevent competitors from generating or collecting the same data.
Oil is fungible; data is non-fungible – One barrel of oil is equal in value to another barrel of the same grade oil. The data of one person’s profile (say, shopping habits) can be far more valuable to a company than another person’s.
Oil has positive marginal costs; data has zero marginal costs – There is a significant expense to producing and distributing an additional barrel of oil. Data is merely encoded information (bits of 1s and 0s), so gathering, storing, and transferring it is nearly costless.
Oil is a search good; data is an experience good – Oil’s value can be assessed prior to purchasing. By contrast, companies don’t know how much a new dataset is worth until it has been combined with pre-existing datasets and deployed using algorithms.
Oil has constant returns to scale; data has rapidly diminishing returns – Oil provides the same energy output to fuel a machine. The initial training data is valuable, while the subsequent data falls in effect.
Oil is valuable; data is worthless – The average barrel of oil is worth $58, while the average dataset is worthless. It’s only once the data is combined with skilled labor, a purpose, and machine learning (or some other technology) that the data gains value.

The Conclusion

There are truths to both sides. Where the Pro side mostly deals in parables and generalities, the Con side gets down to specifics.

Is data the new oil? Is data the new air? Or should we stop trying to put an analogy on this altogether?

I’m in no position to say for sure. But I’m leaning toward data not being the new oil. The metaphor is useful for rallying people’s excitement and conveying that data is important and a valuable pursuit. However, past that, the metaphor does us no good.

In the end, that’s what metaphors are for – simplifying the complex. And I think that’s what this metaphor accurately does. How you decide to implement data into the improvement of your organization, though, should have a bit more strategy involved than, “We need to suck as much oil out of the ground as possible.”