How Charts Lie: Reading Visuals Like a Leader (Not Just a Viewer)

In a modern game or software studio, charts are everywhere. They’re in sprint reviews, planning decks, postmortems, telemetry dashboards, revenue updates, and quarterly roadmaps. But here’s the uncomfortable truth: just because a chart is clean, on-brand, and data-driven… doesn’t mean it’s telling the truth. Or even that it’s being read correctly.

That’s the warning—and the opportunity—at the heart of Alberto Cairo’s How Charts Lie. His central message is deceptively simple: charts don’t speak for themselves. People interpret them. And when we interpret them poorly, we make bad decisions.

Cairo isn’t out to scare us off charts. He’s out to make us smarter with them. And for those of us leading teams where every metric matters, that’s a leadership skill we can’t afford to skip.

Here’s a breakdown of Cairo’s most important—and most actionable—ideas, reframed for leaders in software and game development.

Charts Aren’t Truth. They’re Arguments.

The first big mindset shift is this: a chart isn’t just a picture of data—it’s a visual argument.

Every chart involves a choice:

• What data to include or exclude
• What type of graph to use
• What axis scales to set
• What colors, shapes, or labels to apply

These choices aren’t neutral. They influence how your audience feels, what they notice, and what they think the takeaway is.

Dev Application: When reviewing a chart—yours or someone else’s—ask: “What argument is this making?” Is it meant to show growth? Decline? Urgency? Stability? Then ask, “Does the design support that message—or distort it?”

Context Is Everything

Cairo is relentless on this point: a number without context is meaningless. A chart without a baseline is misleading.

If your studio’s DAU dropped by 10%, is that a red flag—or is it seasonal variation? If your build stability rose from 91% to 94%, is that impressive—or just regression to the mean?

Charts that show trends without reference points, or comparisons without a consistent scale, lie by omission—even if the data is technically correct.

Dev Application: Every time you share a chart, ask: “What is this being compared to?” Make sure to include historical benchmarks, prior releases, or peer segments. Anchor the insight.

The Axes Are a Weapon (Use Them Carefully)

One of the most common (and sneaky) ways charts lie is through axis manipulation. Truncate the Y-axis? A tiny change looks dramatic. Stretch the scale? A serious issue looks trivial.

This isn’t just a technical mistake—it’s a storytelling choice. And if you make it unconsciously, you risk misleading your audience or losing their trust.

Dev Application: In every product review or team dashboard, check the axes. Start them at zero when possible. Highlight big changes only when they’re actually big. And annotate what the viewer should notice.

Design Should Clarify, Not Impress

Cairo loves good design—but he hates when design overwhelms message. He warns against overuse of 3D charts, gradients, animations, and flashy visuals that look slick but hide meaning.

The goal isn’t to impress stakeholders with a pretty slide. It’s to get them to understand the insight in seconds.

Dev Application: Build charts for clarity, not showmanship. Use flat design. Stick to 1–2 colors. Label key data points directly. Use bold only where you want the eye to go. If you need to explain how to read the chart, it’s too complex.

Correlation ≠ Causation (But People Will Think It Does)

This is an old one—but Cairo’s warning is fresh: just because two lines move together doesn’t mean one causes the other.

In our world, that matters a lot. Player churn might correlate with ad exposure, but that doesn’t prove ads caused the churn. Crash rates might drop after a patch, but it could be coincidental.

And here’s the kicker: even if you know the limits, your audience might not. If you present a correlation without a caveat, they’ll draw their own (often wrong) conclusion.

Dev Application: When you show two trends side by side, say explicitly: “This is correlation, not causation. Our working theory is X, and we’re testing it with Y.” Frame uncertainty as part of the process—not a flaw.

The Viewer Brings Their Own Bias

Even the most honest, well-designed chart can still lie—if the audience misreads it.

Cairo emphasizes that our brains are wired to spot patterns, make assumptions, and fill in blanks. We see causality where there is none. We fixate on outliers. We believe charts that confirm what we already think.

That means part of our job as storytellers isn’t just to design better charts—it’s to guide interpretation.

Dev Application: Don’t just show the chart. Talk through it. Frame it. Label it. Use chart titles that reflect the takeaway, not the topic (“Retention dips after Day 3 spike” instead of “Retention Metrics”). Lead the audience to the meaning.

Don’t Trust Viral Charts Without Vetting

Cairo spends a whole chapter breaking down infamous viral charts—some political, some corporate, some accidental—that went global before anyone questioned them.

His point? If a chart feels powerful, it might be wrong.

Leaders are often shown “wow” charts in Slack threads, executive decks, or third-party reports. But the most compelling visuals are also the most likely to be oversimplified, stripped of nuance, or framed with an agenda.

Dev Application: When someone brings you a viral stat or shocking graph, ask:

• What’s the source?
• What’s missing?
• What’s the framing?
• What might a different chart say?

It’s not cynicism. It’s diligence.

Teach Your Team to Be Chart-Literate

One of Cairo’s best points is that data storytelling is a shared responsibility. It’s not just on the analyst or the PM to get it right. Everyone—designers, engineers, producers, QA—needs to be able to read, question, and interpret visuals critically.

That means chart literacy needs to be part of the culture—not just the tooling.

Dev Application: Add a 10-minute segment to sprint reviews: “How to Read This Chart.” Host a team session on visual framing. Call out good examples. Gently question bad ones. Raise the floor on data fluency.

Final Thought: The Chart Is Just the Beginning

At its core, How Charts Lie is not about visuals. It’s about how people make meaning from numbers—and how that process is fragile, emotional, and easily distorted.

As leaders in software and game development, we can’t afford to just “show the chart” and hope people get it. We have to be curators of meaning. Guides through the noise. Translators of signal.

Cairo gives us the mindset—and the responsibility—to do that well.

So the next time you’re reviewing your metrics deck or updating your roadmap visuals, ask yourself:

“Does this chart tell the truth?” “Does it help people understand?” “Does it lead us to a better decision?”

If it doesn’t, it’s not just a design problem. It’s a leadership opportunity. One you can fix.