People keep saying: “Data is the new gold” and “Every company is sitting on a goldmine of data.”

There is some truth in this. There is huge potential value in using data better: improving decisions, automating manual work, optimizing processes, building better products, and sometimes even creating new business models. There is also potential in sharing data, both internally between teams and externally with partners.

But potential value is not the same as actual value. The “data is gold” story often sounds more like wishful thinking or a sales pitch than a guarantee. It can be a way to point at something else: selling tools, consulting hours, or platforms.

If you listen to how data projects are actually sold and run, another pattern appears. To “dig” for the supposed gold in your data, you usually have to pay someone up-front. Consultants, vendors and service providers want fees, licenses, or long projects before anything valuable is delivered. The logic is: “You’re sitting on a goldmine, just pay us to dig.”

If the data really is gold, why does almost all the financial risk sit with the company that owns the data, and so little with the people doing the digging? If there is so much certain value, why isn’t more of the digging offered on a shared-risk or outcome-based basis?

Part of the answer is that data is not like gold. Gold is valuable on its own and easy to price. Data is only valuable in a specific context, combined with specific processes and decisions. Gold, once mined, doesn’t change. Data gets stale, systems change, and models drift. Gold mining companies accept risk because they believe in the upside. In many data projects, the only guaranteed upside is for whoever gets paid to “explore” your data.

On top of that, getting value from data involves a lot more than just “digging.” You need to clean it, integrate it, understand the business context, build pipelines, respect governance and privacy, and deliver something that is actually usable in daily work. Then you have to maintain it as things change. This is ongoing work, not a one-time extraction.

So instead of accepting “data is gold” as a fact, it is more honest and useful to treat data work as a risky investment. Each initiative is a bet: it costs time and money, and the outcome is uncertain. That doesn’t mean you shouldn’t do it. It means you should manage it like an investment, not like a guaranteed treasure hunt.

A more practical approach is to start from specific decisions or processes you want to improve, not from the abstract idea that “we need to use our data.” Define what better looks like and how you will measure it: fewer errors, less manual work, higher conversion, lower churn, faster response times. Then run small, focused projects with clear goals and limits on time and cost. If something works, you can scale it. If it doesn’t, you stop and learn from it.

When working with partners, try to align incentives. Ask how much of their compensation depends on success. Prefer phased work with concrete deliverables and go/no-go points over open-ended exploration. If nobody is willing to share any risk, be careful. You might be paying for digging where there is little or no gold.

The same thinking applies to sharing data. Inside the organization, share data when there is a clear, shared use case, not “just in case.” Agree on ownership and quality expectations so you don’t spread bad data around. Outside the organization, only share data if you understand what the other party will do with it, how value will be created, and how that value will be shared. If you can’t answer who benefits, how you measure it, and what happens if it doesn’t work, pause.

There is real value in using and sharing data. But data is not automatically gold, and repeating that slogan does not make it true. If you always have to pay someone else to dig, and they always get paid whether or not you find anything, then the gold may not be in the data—it may be in the selling of the digging.

Instead of asking how to unlock the gold in your data, ask where, concretely, data can help you make better decisions or run better processes, and how you will know if it worked. That question is less glamorous, but it is much closer to creating real value.