Numbers Don't Speak for Themselves
Six questions for the people explaining the numbers – and the people making decisions with them.
Imagine you’re looking at the results of a blood test. One result is highlighted:
22%.
Is that good?
You probably have a few questions:
What does this measure?
What’s a normal range?
Should I be concerned?
What would cause it to change?
Do I need to do anything about it?
Your doctor doesn’t just tell you the number. They explain what it means, how it compares to what’s typical, and whether it requires action.
The same thing happens in business all the time. Whether the number comes from a fundraising report, a marketing campaign, a financial statement, or a website dashboard, the number itself is only part of the story.
Most of us spend at least part of our careers translating numbers for people who aren’t close to the data behind them. Over time, I’ve realized I rely on the same set of questions almost every time.
Whether I’m explaining a metric to leadership, reviewing results with a team, or helping someone understand a report they didn’t create, these questions fill in the parts of the story a number can’t tell you by itself. And when I’m on the receiving end, they’re the same questions I want answered before I decide what to do next.
Q1: How do we make it safe not to know?
Before you even talk numbers, make it clear that nobody is expected to know all of this already. Remember that the person receiving this information didn’t build the report, and they may not even use the system it came from. They shouldn’t be expected to instantly understand every metric, assumption, and calculation behind it.
If you’re explaining:
“Before we dig in, let me give you some context on where this comes from. Some of this might be new.”
If you’re asking:
“I want to make sure I’m reading this correctly. Can you walk me through it?”
Good conversations happen when people stop pretending they already understand and the people doing the explaining avoid making others feel foolish simply because they have different expertise.
For example, a development director presents fundraising results to board members who don’t work with donor metrics day-to-day. Before discussing donor retention, campaign performance, or year-over-year giving, the director spends a minute defining the metrics and providing context so board members can focus on the discussion instead of wondering whether they’re missing something.
Two good things happen that turn this into a productive conversation. First, board members gain enough context to participate confidently. Second, everyone stays focused on the problem to solve rather than getting stuck on terminology or debating what a metric means. The discussion moves more quickly from “What are we looking at?” to “What should we do about it?”
Q2: What does this number tell me? What’s missing?
Every metric captures part of reality and ignores the rest. It’s useful to know where a number comes from, but you also need to understand what activity or action the number represents (and what it does not).
A website pageview tells us that someone visited a page. But it doesn’t tell us whether they found what they were looking for, stayed to read, or left immediately.
A customer satisfaction score isn’t just a number. It tells us how a group of customers responded to a survey. But it doesn’t tell us how the customers who didn’t respond felt about their experience.
If you’re explaining:
“This score comes from our post-purchase survey. It reflects the opinions of customers who chose to respond, but not those who ignored the survey entirely. It’s useful feedback, but it may not represent every customer’s experience.”
If you’re asking:
“What does this number measure, and what part is not included here?”
Q3: Why do we look at this number?
The easiest way to waste time in a data conversation is to spend twenty minutes discussing a metric that isn’t connected to the decision you’re trying to make.
If you can’t explain why you’re looking at a number, it’s hard to know what to do with it. And a number can be perfectly accurate and still be irrelevant to the question at hand.
If you’re explaining:
“We’re looking at email open rate because we’re trying to understand whether our subject lines are getting attention. This metric tells us whether people are interested enough to open.”
If you’re asking:
If this number went up, down, or stayed the same, what would we actually do differently?
For example, a retail store sees more visitors this month than last month, but fewer visitors are actually making purchases. Foot traffic and purchase conversion rate answer different questions. Until you know what the business is trying to achieve, you can’t know which number matters most.
Q4: What’s “normal?”
A number needs context, or it’s just trivia. Whether something is good, bad, or unremarkable depends entirely on what you’re comparing it to. This is where you need a reference point such as benchmarks, historical performance or industry trends.
If you’re explaining:
“For an email list like ours, a 20–25% open rate is typical. We’re currently at 22%, which puts us in the normal range.”
If you’re asking:
“Compared to what?”
For example, a nonprofit reports that 48% of last year’s donors gave again this year. Is that good? It depends. If donor retention is typically 40%, that’s very encouraging. If it has historically been 60%, it may signal a problem. The number hasn’t changed. The context has.
Q5: What makes the number move?
Knowing why a number moves helps you respond.
Numbers don’t just decide to be different. Something happened. Usually several somethings. Some of those factors are within our control. Others aren’t. The more clearly we understand the difference, the more likely we are to take productive action instead of reacting to noise.
For example, a nonprofit sees a decline in donations during the summer. Before sounding the alarm, the development team looks for possible drivers. Is this a seasonal pattern that happens every year? Did they do less outreach than normal? Did a major grant end? The answers matter because each explanation suggests a different response.
If you’re explaining:
“Donation totals are influenced by the number of outreach campaigns, donor retention, grant funding, seasonal giving patterns, and broader economic conditions. Some of those are within our control, and some aren’t.”
If you’re asking:
“What would have to change for this number to improve?”
Q6: What should we pay attention to?
Not every movement in the data deserves a reaction, and not every metric has the same clock. A weekly change might be meaningful for website traffic during a marketing campaign, but largely irrelevant for annual donor retention or employee turnover.
Want to create anxiety? Watch a metric more closely than it was designed to be watched. In some situations, moderate fluctuations in the numbers are normal. Some changes may be expected at certain points or only become meaningful when they persist over time.
For example, a Customer Support team sees a spike in tickets the week after launching a new product feature. On its own, that increase isn’t necessarily bad news. More customers may be using the feature, asking questions, and learning how it works. The more important question is whether ticket volume returns to normal after the initial adjustment period.
If you’re explaining:
“Support tickets increased after the launch, which was expected. We’ll continue monitoring for a few weeks, but a short-term spike isn’t unusual.”
If you’re asking:
“What amount of change would actually cause us to act?”
Why these questions matter
Every good data conversation touches on these six questions, whether they get said out loud or not.
When the questions are skipped, people fill in the gaps themselves. They make assumptions about what a number means, whether it’s good or bad, what caused it, and what should happen next.
When those questions are answered, people spend less time talking past each other and more time solving the problem in front of them. Decisions get made with a shared understanding of what the number represents, what it doesn’t, and why it matters.
Just as importantly, everyone gets better at these conversations. The person explaining learns to communicate more clearly. The person asking learns what to look for. Over time, both sides become more confident working with data they didn’t collect and metrics they don’t use every day.
Most questionable decisions aren’t caused by a lack of data. They’re caused by confusion about what the data means, what it doesn’t mean, or why it’s being discussed at all.
Ask better questions, and the numbers become a lot more useful - and the conversation does too.
This should be a mandatory read for anybody working with data analytics.
Thank you for sharing this 🙏