It’s not just a cliche; data really is like oil.
There’s so much value in oil in fact that today it is frequently the difference between long-term profitability and failure for thousands of businesses around the world.
But monetizing such a precious resource as data or oil is more complex than it may at first seem. It requires strategic execution and there are hazards along the way.
Here are three steps your company can follow, to turn your data from a raw resource into a thriving business:
Edwin L. Drake, the man who discovered oil in America, had no presumptions that the oil business would be easy. He methodically drilled deeper into the earth than anyone before, inventing entirely new technologies to make that possible. He blew through his company’s coffers, then gambled by taking out a loan in his own name. Famously, townsfolk near his drilling sites mocked and jeered the seemingly hapless prospector.
Then when he finally struck, it triggered a new Gold Rush. Opportunistic Americans flocked to Pennsylvania to make a fortune, as if the oil was just there for the taking.
Everybody wants a free lunch.
Perhaps you’re aware, in some vague sense, that your data could be valuable. So you collect lots of it–without much discretion, as much as you can get away with–on the basis that it might be worth something.
It’s an understandable instinct, but then what? You’re liable to end up with a big morass of data. To do anything productive, you’ll first need to sort it out–to organize and identify what data you actually have.
It’s at this point that you may realise: a lot of what we have isn’t actually worth very much at all. It will might be outdated, incomplete, inconsistent or inaccurate. More commonly still you’ll often find it’s simply irrelevant. Data such as invoicing data, inventory and employee data etc helpful for improving your own products or operations (and this is a form of data monetization) but you need to seriously ask “what would anyone else do with it?” Figuring out what data is externally remunerative, rather than merely internally useful, requires a degree of self-reflection and honesty that eludes many ardent data evangelists .
At this point, if you’ve done it right, you’ll have specific sets of actionable data that might be worth something on the market. Onto step two.
The defining image of the oil rush in America was the blowout: a tall, latticed derrick spewing petroleum high in the sky like a geyser. Ordinary folk hitching a ride to Pennsylvania dreamed of striking a vein so rich that it would explode black gold.
It’s human nature that, when we have dollar signs in our eyes, we overlook considerations like cost and risk. Blowouts were, of course, quite wasteful, and they tended to kill people. But who was worried about that?
“People think [data is] some kind of magic pixie dust that just automatically resolves itself into money. And the reality is, it doesn’t.”
Data monetization seems so obvious from the outside. But there are technical costs and perhaps more importantly risks that are legal and even moral in nature that might prevent you from utilizing otherwise valuable information, or cause trouble if you try. It’s necessary to assess all risks to determine what use cases are actually worth pursuing.
Imagine, for example, imagine you run a social media company. You have the ability to track what users are looking at and for how long, whom they’re interacting with and what they’re saying, where they’re located, and so on. There are untold ways to profit off of such data, in theory, but what’s actually worth doing?
Aside from the technical challenges in deep data mining, and the costs of building out such capabilities, a lot of it is illegal. Regulations like EU’s GDPR and California’s CCPA severely limit how technology companies can manage user data. It’s not just about what you can and cannot collect, either–one rule common to these laws is that companies must provide users with the ability to download and permanently delete all of their personal information. Thus, the more data you collect, the more resources you’ll have to invest in order to abide by these laws.
Then there are moral considerations. What kinds of monetization would cross the line? Hyper-personalized advertisements? Selling data directly to third parties? To government intelligence agencies?
After sorting out the undesirables, if you’ve done it right, you’ll have a subset of use cases for your data that are technically attainable, law-abiding and morally sound.
You’ve got your data and a use case, so you visit an IT consulting firm.
You ask them: what’s it going to cost me to do X and Y with my data?
They reply: 15 million dollars.
Is that investment worth the payoff? 15 million dollars seems like quite a lot. It depends, you suppose, on whether you’re going to make 10 million dollars’ profit, or 100 million.
This is why it’s so crucial to develop your data monetisation business model before you start the monetization process . How are you going to package what you’re offering? What will you charge for it, and by what means? How many customers do you expect, and how soon? From there, what’s the expected revenue, margin, and ROI? Without evidence-based projections your project is likely to peter out, like all those would-be oilmen with nary a clue as to how to extract, store, transport and sell petroleum.
Like Edwin Drake. Drake could find oil but lacked the business skills to actually profit off of it. He didn’t patent the unique inventions that allowed him to drill so effectively, and squandered his savings on speculation. The man who discovered the most valuable natural resource in America died in poverty.
Today, petroleum is no longer the world’s most valuable resource. As of a half decade ago, that honor belongs to data. Data is far better than oil, too, because any company–your company–can profit from it. As long as you follow the necessary steps along the way.
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