When More Data Is a Waste of Time
Product teams rely on data. Still, many decisions remain unchanged after all that learning. The real reason for this isn’t laziness or a dislike of research.
In every product role, someone eventually says, “Let’s do more research to be safe.”
It sounds responsible and mature, like something a good product manager should say. Sometimes, it’s the right move. But just as often, this is how decisions quietly die, buried under interviews, dashboards, and polished slides that change nothing.
This is where the idea of Expected Value of Information (EVI) helps, even if you never use the term in a meeting.
EVI is a simple idea. Information isn’t valuable by default; it only matters if it improves a decision. More specifically, it’s valuable if the expected improvement in outcomes is greater than the cost of getting that information, including time, money, and delays.
In product management, this matters because learning always has a cost. User interviews take time. Prototypes slow down delivery. Experiments use up engineering resources. Waiting for more data often means not shipping, not committing, and not learning from real-world feedback. EVI asks a tough question: what decision will actually change if we get this new information?
If the honest answer is “none,” then the expected value of that information is almost zero, no matter how interesting it seems.
Good product managers use EVI all the time, often without calling it that. Imagine you’re thinking about a feature that will take three months to build, but you’re not sure if users really need it. A week of interviews and a quick prototype might show the problem isn’t that big. In this case, that information could save months of work. There’s high uncertainty, a high cost if you’re wrong, and a low cost to learn — so EVI is very high. Here, research isn’t just justified; it would be irresponsible not to do it.
Now, consider a different situation. A third-party API you use is being shut down, so you have to migrate. There’s no other option. Running discovery workshops or user interviews won’t change the decision — the work has to be done. Any extra research might feel useful, but from an EVI perspective, it’s just for show. In this case, getting the work done matters more than being curious.
Prioritization is another area where EVI quietly sets strong product leaders apart from busy ones. Teams often debate whether to improve onboarding or build advanced features for power users. Both seem important. A small piece of information, like learning that 40 percent of users never finish onboarding, can completely change the roadmap. In that moment, a simple funnel analysis is extremely valuable because it changes the order of work, not just the team’s confidence.
EVI also explains why product strategy often feels slower and heavier than feature work. Strategic decisions are expensive, long-lasting, and hard to undo. Entering a new market, choosing a pricing model, or picking a core platform locks you in. The cost of being wrong is measured in years, not sprints. In these cases, even costly information can be worth it compared to the risks. A legal review, a pilot launch, or a partner conversation might seem slow, but their expected value is often very high.
Pricing decisions are a good example. Launching a new premium tier affects revenue, positioning, and trust. A fake-door test or a few tough conversations with real customers can prevent a very public mistake. When pricing goes wrong, it almost never fails quietly.
On the other end, some decisions lead teams to over-invest in learning. For example, a copy change on a low-traffic internal tool doesn’t need an experiment plan, research brief, and stakeholder review. In these cases, shipping and watching what happens is usually the smart move. The decision can be reversed, the risk is small, and the EVI of more information is close to zero.
EVI helps product teams avoid two extremes. One is analysis paralysis, where learning replaces decision-making. The other is reckless execution, where teams mistake speed for courage. EVI offers a better way: learn actively when it can change the outcome, and act quickly when it can’t.
The most useful EVI question isn’t “do we need more data?” but “what would we do differently if this turns out to be false?” If there’s no clear answer, it’s often smartest to stop researching and start shipping.
Information isn’t a virtue. Making decisions is.