I don’t think there’s been a word more used and abused in English over the past decade than data. At least in the business world. It has become almost mythically valuable there. An end in itself. And there’s been a gold rush of businesses, not to the California hills, but to their digital experiences, trying to get those experiences to receive more than they give. In fact, it’s been such an out-of-control tumble to Hungry-Hungry Hippo personal data that countries and states and major platforms are cracking down on this infestation of data prospectors.

And the desire for data is mostly…misplaced.

Because while businesses aren’t literally running to California to mine data, they are following California. It’s the socials that made it massive on people’s data. It was kind of like gold for them. And they set an example that a lot of businesses salivated over.

But user data is probably not your business. Not your side business. Not a diversification of your business. Not even a rainy-day plan for your business. In fact, collecting too much and the wrong kind of data is bad for your business.

Why Do You Want that Data?

I know why Facebook wants my data. And Twitter. Google. They give me free stuff and then sell ads to me. Or, more accurately, sell me to advertisers. Or, you know, a rainbow of other nefarious reasons. 

But you are selling something else. You have an actual product or a service that you want to sell. You’re not productizing customers. So you don’t need all the data that a digital experience can Hoover up from people on a minute-by-minute basis. You just need enough to sell your thing.

Because I’m not saying that personal data can’t help you sell. It can. I’m saying don’t stockpile and squat on it like Smaug. You just need the data you need.

You just need the data you need.

For instance, you need data to better an experience for your users. Or to personalize one. Or to access for business decision-making. These are legit reasons to want that data. But which type of data? And how much? And how frequent? And how permanent? And at what point does the data load outweigh the data benefits?

Those answers should come from your strategy. And “Maybe it’ll be useful in the future” isn’t a legit strategy. It’s how people end up with garages full of yard sale stuff they never really wanted. And your garage full of yard sale stuff is a bunch of unactionable data lying around without even a potential use (because again, you’re not going to sell it, right? RIGHT?). But it’s more than just useless data. It’s terrifying data.

Can You Accept the Cost and Risk?

Data is dangerous. To your bottom line and to your bottom. First, it costs money to architect, design, and build solutions to gather that data. It costs money to store that data. And it costs money to interpret it. And even if you have the money for indefinite access, collection, and husbandry of large amounts of data, you’re also radically increasing your risk. Security breaches. Lawsuits. Running afoul of legal regulations. You’re going to get Smaug’d sitting on top of that data hoard.

Data is dangerous. To your bottom line and to your bottom.

And here’s the other risk. Data goes bad. In businesses, at least.

This data isn’t scientific data. It’s personal data. If you’re a scientist analyzing gecko reproductive cycles (which does certainly sound personal), you can spend years, a lifetime gathering data that one day may be accessed by another scientist and save the planet. Just the act of gathering data is a scientific career well spent. 

But that’s not how business works. Your customer’s data is not valuable just for being gathered and it becomes less valuable as a business asset the older it is.

Do You Have Data Experts at Your Elbow?

Let’s say you’re okay with not being strategic with the data you gather. Let’s say you’re okay shouldering the cost and risk of it, too. You still need to do something with it at some point. And that means you need data scientists on staff. Because you can’t merely rely on SAAS dashboards to help you parse it all (which is one of those costs of data).

Data is complex (you’ve been waiting for me to say that, right?). It’s hard to interpret. Guessing sometimes has a better chance of being right. Talking to a psychic can beat data analysis. Because data is full of variables on top of variables and rapidly changing contexts and omissions, and can be completely subjective. You need an expert to even have a chance of navigating data.

But I’m of two minds on this one. You do need an expert. But you also need a pinch of salt to go with that expert.

Here. This was my favorite tweet from Election Day, from the former executive editor of Cracked.com, who spent twelve years getting beat up by data:

See, in business, data scientists are constrained by, well, business. For instance, the speed of business is often incompatible with the thorough and accurate analysis of data. These data scientists have brains for brains, but they’re only so much they can do when gains need to be made every quarter. Even throwing our rudimentary AIs and algorithms at the problem doesn’t solve it. Not yet. And if it did, your position would probably not be needed anyway.

In the end, data is greata. And I wrote this entire article just so I could write those three words. But, as with everything in business, you have to be strategic with it. Develop a plan for every particle of data you want to access, hire experts (but realize their business limitations), and make sure you have the resources and knowledge to access, store, interpret, and protect it. 

And don’t be a Smaug.