Hi there, I hope your weekends have gone well. So about 30% of readers here discovered PlotStack through my ebook (Data Visualization: An Audience-First Approach), and I’m proud to announce that it’s received a recent major update. If curious, you can check it out below. It’s a free online interactive experience now instead of a static PDF. Truthfully, it’s a lead magnet to the rest of the world, but for you since you’re already subscribed you can access it with just the same email you use here. (Plus it’s mobile friendly)

If you’ve ever sat through a call where the focus was on information irrelevant to you and the audience, the concepts in the book dig into that experience. The data was fine. It’s just the altitude was wrong. That's the core idea behind my book.

In this week’s issue I want to revisit the concept that anchors the whole idea, because it's one of the most practical mental models I've found for thinking about visual design. It’s helped me put the idea into something practical with students, direct reports, and even myself as a reminder when I’m working on reporting.

And the idea starts with a helicopter.

📣 Looking for Beta Testers
There are still a couple slots available to beta test my upcoming Chrome Extension: VizBuddy. You can check out the tool here for details just keep in mind it’s not published yet on the Chrome Store and the landing page is a temporary placeholder. If interested, simply respond to this email with “VizBuddy”. I will send instructions out in the upcoming week for everyone who has already opted in. Thanks in advance!

The Altitude Problem Most People Get Wrong in Analytics

The scene. Picture yourself as the owner of a large outdoor shopping outlet. You're not walking the floor, you're in a helicopter, high above it. From the sky, you see the full footprint. Buildings, parking lots, the overall view of the place. You can't read store signs or count individual shoppers, but you don't need to. You're watching for whether the whole operation is running.

Now drop lower. You're the outlet manager now, hovering closer to the ground. Details sharpen. You can see which stores are pulling foot traffic and which ones look quiet. You start asking different questions like where are the bottlenecks, which areas need attention, and wondering what patterns are hiding in the flow of people?

Then you land. As the store manager, the view of the entire outlet disappears. Your store is your world. You're watching customers browse, tracking checkout lines, making decisions that live entirely at the transaction level.

Three roles. Three altitudes. Three completely different relationships to the same underlying reality.

This is exactly how data granularity works in visualization. Your audience isn't wrong for wanting different things, they're operating at different altitudes, and our job is to meet them where they are.

Where most visualizations break down. Executives get buried in detail they never asked for. A dashboard crammed with daily trend lines for every region doesn't help someone who needs to know whether the business is on track, it just adds noise at the wrong altitude.

Frontline employees get handed vague summaries that don't tell them anything actionable. Knowing that overall sales are "up 4%" doesn't help a store manager who needs to know what's moving and what isn't.

And the most common mistake: the one-size-fits-all dashboard that intends to serve everyone but actually serves no one. It forces executives to dig for the high-level read they need while leaving managers short on the operational detail that drives decisions.

Designing for the right altitude looks like this. At the executive level, lead with summaries and trends. Simple, high-impact visuals, line charts, KPI tiles, summary views, with everything else stripped away.

At the management level, bring in comparative insight. Which regions, products, or teams are pulling ahead? Heatmaps, bar charts, and rankings do this well.

At the operational level, give people the specifics. Detailed tables, drill-downs, transaction-level data, the kind of view that lets someone take action on what they're seeing.

This isn't a retail-specific framework. It applies to every industry, every client, every internal report you've ever built. The altitude shifts, but the principle holds.

If a visualization isn't landing, the data probably isn't the problem. The altitude is. You can read more about this concept here.

What year was the first iPhone released?

A. 2005
B. 2006
C. 2007
D. 2008

Answer at the bottom of this issue

How To Communicate Data To Stakeholders In A Way They Actually Understand by Team Sigma
Digs into why analysts and executives speak fundamentally different languages. Analysts focus on precision and statistical validity, executives on risk and opportunity and how that gap is the real reason dashboards fail to land. Read more →

Dashboard design best practices – 4 key principles by Sisense Team
Covers the idea of revealing details progressively, most significant insights at the top, trends in the middle, granular detail at the bottom and introduces the five-second rule as a practical test for whether your most important metric is actually findable. Read more →

Elements of Data Storytelling: How to Tell a Good Story with Data by Dave Mariani
Makes the point directly: executives need business impact, analysts want methodology, and operations teams need actionable metrics, and that the same data tells a completely different story depending on who's receiving it. Read more →

Power BI Theme Generator #data-visualization #productivity
A newer generator on the market with a live preview, light/dark mode support, accessibility-friendly color palettes, and the ability to generate palettes from an uploaded image. A solid pick for those who want their reports to look as good as they function.

ColorSlurp #productivity
ColorSlurp is the ultimate suite of color tools for designers, developers, and artists. This is the color picker I use when developing color palettes for my data visualization projects.

▶️ Data Storytelling 101 | Think Like a Data Analyst by Christine Jiang (13 mins)
The video focuses on the critical skill of data storytelling, highlighting its importance in distinguishing data analysts in competitive job markets, especially in the era of AI where generating charts is automated but communicating insights effectively remains a human advantage. Christine, a former data hiring manager and mentor, emphasizes that data storytelling goes far beyond simple visualization: it involves explaining not only what the data shows but why it matters and what actions should be taken.

What year was the first iPhone released?

A. 2005
B. 2006
C. 2007 ✅
D. 2008

Steve Jobs unveiled the original iPhone on January 9, 2007, at the Macworld Conference & Expo in San Francisco, famously introducing it as "an iPod, a phone, and an internet communicator" all in one. It went on sale that June and fundamentally changed the mobile industry.

📖 Read more → Wikipedia: iPhone (1st generation)

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Or email me directly at → [email protected].

If you’re feeling generous, I also keep an Amazon Wishlist of books and tools I’m interested in.