But what if there are more countries? A horizontal bar chart will quickly run out of room on the x-axis and the charting software will start tilting the labels: Format dates per the ISO-8601 standard, like YYYY-MM-DD. When was this the number of users? In 2019 June or in 2020 February? Let's add that to the title. But now we're forced to do eye gymnastics and track the height of the bars to the axis to try to read off values, and we'll be reading off estimated values. Let's add some axis labels and a chart title to make it more clear what we're looking at: But what happened with the percentages? The bar chart shows the original metric values now. But if you're reading it off the text anyway, what's the point of the pie chart? Let's try a bar chart.
You actually have to move your eye to the text and read it off. It's really hard to tell how the orange and red areas compare. The problem is, the human brain is not good at reading off proportions from a circular scale. Okay, now the pie chart is pretty minimal, but it's still not good. Doing a poor job presenting data in the form of charts and dashboards can mislead the audience, turn off the audience and block the impact of the underlying work. Making clear, readable charts is part of the craftmanship minimum for any data related role, whether it's a Data Engineer, Product Analyst, BI Analyst, Data Scientist or ML Engineer. Unfortunately, many data professionals consider building charts and dashboard to be low-level activities that's "just BI", and they can't be bothered with such considerations. The data professional who builds the start needs to know know "it" is, or at least how the user can get at it. The user should be able to look at the chart and "get it" quickly. The goal of building charts and dashboards is to convey information about the underlying data effectively.
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