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.

It's probably a lot less often that you think. In fact, there are just three specific—and quite rare—situations in which I'd show data as a table only (as opposed to a graph or a graph plus a table).

What are those three situations? Find out in my latest video (10 mins)!

I recently had a great exchange with dataviz luminary Salma Sultana about where best to locate slice labels in a pie chart. The consensus that we came to was that it depends on the lengths of the category names and whether or not there are any very small slices.

“% change vs. previous period” numbers are ubiquitous on dashboards and they look useful, but they don't actually tell users anything!

Don't believe me? Check out my latest video (8 min.), which I recorded during a recent workshop for the lovely folks at Funnel, makers of great marketing dashboard software.

We often need to show deviation values over time, e.g., deviation of our monthly expenses from the budget for each month but, unfortunately, these values are often shown as line charts, which can be very confusing or misleading for audiences.

To see a better solution, check out my latest video (5 mins.).

Not to flex or anything, but I’m somewhat of a connoisseur of hotel shower faucets. I travel frequently to deliver workshops, so I’ve seen many different models. A lot of them look almost like modern art sculptures. Clean lines. Pleasing symmetry. Modern aesthetics.

But how do I, y’know, have a %#*$ing shower?

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.

You might have seen this brilliant piece of viral marketing from cybersecurity firm Hive Systems, that seems to pop up every year in my social media feeds. While it's a great piece of content marketing, the color choices leave a few things to be desired, IMHO, which I discuss in this blog post.

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.

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.

You may or may not be familiar with Marimekko charts, but they're popular in some fields, such as management consulting.

To see why I avoid using this chart type and what I use instead, check out my latest video (8 mins.).

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?

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.

One of my pet peeves is dates in charts that are ambiguous, but the problem can easily by avoided by choosing good date formats.

Probably the most common way to show values on a map is the "colored-region" or "choropleth" method. I'm not the first to notice, however, that this method presents some potentially serious perceptual risks.

I was recently invited to deliver a webinar as part of JMP's great Statistically Speaking series. The webinar consisted of a 35-minute talk entitled "How to choose a chart type (it's trickier than you think)," followed by about 25 minutes of Q&A from a very engaged (and large) audience. The recording is now available to view online.