Data you can’t use is data you can’t afford

Your organization invested heavily in analytics infrastructure. You have dashboards. You have KPIs. You have data flowing everywhere. Yet when a critical decision lands on your desk—whether to pivot a product line, shift resources, or restructure a team—that expensive data infrastructure suddenly feels useless.

This is the gap between having data and having evidence.

Most organizations confuse the two. Data is abundant. Evidence is purposeful. Data is what you collect. Evidence is what tells you whether your work actually moved the needle on something that matters.
The trap is expensive. Companies spend thousands on tools, platforms, and systems to capture more data. They build elaborate dashboards. They hire analysts. But here’s what happens: they measure what’s easy to measure, not what’s important to measure. They track activity metrics—how many training sessions were completed, how many meetings occurred, how many initiatives are “in progress.” These feel like progress. They’re not.

Meanwhile, the real question goes unanswered: Did this work actually change what we needed it to change?

Consider this scenario. A company launches an expensive leadership development program. Completion rates are tracked meticulously. The data shows 95% participation. Success, right? Except two years later, retention of high performers has declined. The program didn’t prevent the problem it was designed to solve. But by the time that outcome became visible, the decision to invest was already justified by the activity metrics.

The cost of this misalignment isn’t just wasted spending on the program itself. It’s the compounding cost of making decisions based on the wrong evidence. Resources continue flowing to initiatives that don’t deliver. Priorities stay muddled. Execution fragments. Leaders feel out of control because they are.
PuMP’s discipline starts here: distinguishing between what you do and what actually changes because of what you do. Before you measure anything, ask yourself this question: if this metric improved, would the organization be genuinely better off? If you can’t answer that clearly, you’re measuring activity, not outcome.

The practical path forward is simpler than most people think. Stop asking “what should we measure?” Start asking “what needs to change?” Articulate that result clearly—in plain language, observable and measurable. Then design just enough measurement to track whether it’s actually changing. Dismantle everything else.

This costs nothing. It saves everything. Because the evidence you need is usually already available somewhere in your organization. You’re just looking for it in the wrong place, or looking at it through the wrong lens.

The organizations that win aren’t the ones with the most data. They’re the ones disciplined enough to use only the data that matters.