New video! Should you be using AI to create charts instead of Excel/Tableau/etc.?

 
 

Generative AIs like ​ChatGPT​, ​Claude​, and ​Gemini​ have been steadily improving their chart-making abilities during the past year or so and now claim to be top-notch chart creation tools. Is this just AI hype, or is it time to switch from Excel/Tableau/etc.?

❓ Is it faster and/or easier to create charts in AI?

❓ Can AI handle the kinds of charts that you need to create? How good are the results?

❓ How much human help does AI need? Can it produce well-designed charts without human intervention?

❓ Are AI-generated charts reliable and accurate?

Having recently put various AIs through their chart-making paces, I can say that the answer to all of these questions is a resounding “it depends.” Sometimes it makes sense to use AI (although there are important caveats to be aware of). Sometimes, however, a tool like Excel or Tableau will be the better choice. When should you use which tool? What are the caveats around using AI? Find out in my latest video (see links below).

One thing that became clear in my testing was that, if you want top-notch charts, you still need top-notch dataviz skillz—even if you're using AI. How can you acquire those skills quickly? Well one pretty good way would be to take my ​Practical Charts course​, which I’ll be delivering in a live online public workshop in June! Info/registration: ​https://www.practicalreporting.com/june-2025-online-workshop​

Watch the "Should you use AI to create charts?" video on...

Why it wouldn’t make sense to adapt Practical Charts to Power BI’s limited visuals

My Practical Charts course is tool agnostic, that is, it doesn’t assume that any particular dataviz software product (Excel, Tableau, Google Sheets, etc.) is being used. There are three reasons for this:

  1. The guidelines for making design choices like choosing a chart type or choosing a color palette are the same, regardless of which dataviz software product is being used.

  2. All the chart types and techniques in Practical Charts are pretty standard and can be implemented in just about any modern dataviz software product.

  3. If a workshop participant is unsure of how to create anything that I show in the course, high-quality tutorials for just about every major dataviz software product are just a Google search or AI prompt away.

You might have noticed that, in those last two reasons, I referred to just about any major data visualization software product. The “just about” qualifier is there because of one unfortunate exception:

Power BI.

In this post I explain why I don’t think it makes sense to change my Practical Charts course due to Power BI’s limitations.

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No, everyone DOESN’T know how to read a scatterplot

When I’m chatting with other chart creators, it sometimes feels like there are two different groups that live in two completely different worlds:

The first world is populated by those who create charts for relatively data-savvy audiences. In this world, chart types like scatterplots and histograms are “basic” chart types that everyone knows how to read.

The second world is populated by those who regularly create charts for “non-data” audiences who often struggle with anything other than simple bar, line, or pie charts.

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Should you avoid using “advanced” chart types? (+ Black Friday Sale!)

I’ve seen the following scenario play out many times in the organizations with which I work:

A chart creator decides to “get creative” by using a histogram, connected scatterplot, ribbon chart or some other chart type that they know to be unfamiliar to the audience. They could have used a simpler, more familiar chart type to say the same things about the data, but they wanted to “challenge the audience,” or “teach them new chart-reading skills.”

The chart then goes over like a lead balloon, however. The audience misreads the chart, skips reading it altogether, or gets annoyed with the chart creator, who then feels bitter, believing their audience to be intellectually lazy or just dumb.

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“I don’t want red on my dashboards. It looks too negative.”

I first heard this objection from a client a number of years ago and it took me so off-guard that I just stared at them and then mumbled something about getting back to them on that. It just seemed like such a bizarre thing to say…

Since that time, I’ve heard this objection at least half a dozen times and have had a chance to formulate a couple of responses that usually convince dashboard users to rethink their “no red” policy.

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Dashboards should only show the “most important” metrics… right?

I regularly hear complaints from dashboard creators that go something like this:

  • "My users consider dozens of metrics (or more) to be ‘KPIs’ and they want me to put them all on the dashboard."

  • "My users don’t understand that, among all the metrics that we could show on the dashboard, only three to five of them are truly important. The rest are just noise that distract from the truly important numbers, and so don’t belong on the dashboard."

Think about that for a second, though. Does it seem plausible that someone could run an entire team, department, or organization based on just five numbers?

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Creativity is great in charts... except when it gets in the reader's way!

I recently saw people praising a chart from The European Correspondent​ on social media comparing the years of compulsory schooling for different European countries.

While it's certainly creative, when I tried to actually read the chart and spot the insights that were in the callouts, I had to work pretty hard, and some insights were difficult—or even impossible—to spot. In this blog post I critique the original design and propose a redesign that I think performs better.

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