Everyone knows data is a good thing. After all, as management expert Peter Drucker famously said: “If you can’t measure it, you can’t improve it.”

But there’s a catch. Data doesn’t necessarily provide clarity or real insights. Too much data, or irrelevant data, can work against you, so decision making becomes more difficult. It can be hard to find the signal in the noise.

To tell a clear story, achieve a goal or effectively solve a problem, you have to be able to prioritize what data is actually important. Or you may end up measuring what doesn’t matter, just because you can.

Identifying the Right Data

Which data you pay attention to should depend on what you’re trying to achieve. What’s your end goal? And what are your targets — how will you know you’ve succeeded? For example, generating brand awareness would require paying attention to different metrics (or ‘Key Performance Indicators’) than generating conversions.

And what’s the true value of a metric? What’s its overall impact on the bottom line? Does it impact immediate goals, or long-term goals?

Different goals and timelines determine which data is more or less valuable to you today.

Data and Decision Making

Analyzing just a few of the most meaningful metrics provides clarity and prevents data overload. If you’re trying to solve a particular problem, what information is actually important? What metrics are actually useful for decision makers? If you want to make everyone’s lives easier, (and maybe win some points with coworkers), isolate only the data they need.

For instance, the person managing a campaign may need to understand more tactical metrics, while the CMO needs to see the bigger picture. One useful way to think about it: If the data changed dramatically, would it have a significant impact on the next big decision? If the answer’s no, leave it out.

Relying Too Much on Data

Data alone doesn’t always tell the whole story. Interpretations can be skewed by biases or assumptions, leading to bad decisions or limiting your options.

Take this common scenario: A website visitor spends a lot of time on a particular page. Maybe they find that content relevant and helpful. Or maybe they’re struggling to find the information they need.

How do you know? Assumptions about data should be tempered with other factors, like past experience, market research, or road testing your hypothesis in the real world. Qualitative and quantitative data go hand in hand, giving you a clearer, more nuanced understanding of the situation.

Data is an important tool, when used wisely. No doubt about it. But too much data, or the wrong data, can cloud decision making and hide actionable insights. Take some time to think about what’s important, why, and for whom. When it comes to data, the simpler it is, the better.