Category: Visualization

Lewis Chou has some advice for choosing color schemes for data visualization:

When making a chart, we should use the same color scheme for the same metrics. And we need to avoid the excessive color interference to the user.

For example, when we do sales analysis, we usually analyze the indicators of sales and payment collection. Then, when we do data visualization analysis of different dimensions for the same indicator, we recommend using the same color system for sales and payment collection. It means that the sales amount can be indicated by the yellow-green color, and the return amount can be indicated by the blue color accordingly. After following the principle of consistency of indicator color, the user can quickly understand the meaning of the indicator expressed by the current data visualization chart according to the color distinction.

Color is a pre-attentive attribute: we sub-consciously pay attention to it before we consciously observe it. That has advantages but it also comes with responsibilities.

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Lisa Charlotte Rost takes us through sparklines:

Sparklines are curious things. They’re supposed to show a trend, and a trend only. They’re supposed to show when something (like stocks) increase and decrease, where the peaks and the valleys are. But sparklines are not supposed to be comparable with each other.

So when you’re seeing two sparklines with the same height, the ebbs and flows of the first one could play out between 0 and 10 (e.g. US-Dollar), while the other sparkline’s peak is at 10,000.

But that’s odd, no? Doesn’t that invite people to make totally false assumptions?

I like sparklines a lot, but I’m apt to violate this particular rule and make them cross-comparable unless I know people will never care about comparisons between elements. One way to get around the “what if the range is big?” problem is to plot sparkline heights as logs so that 1000 is a bit bigger than 100, which is a bit bigger than 10. The argument I make for doing that is you still see size differences and sparkline comparisons are imprecise to begin with, so magnitudes are more important than exact values.

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Meagan Longoria takes us through a corporate coloring problem:

Last week, I had a conversation on twitter about dealing with corporate color palettes that don’t work well for data visualization. Usually, this happens because corporate palettes are designed with websites and/or marketing collateral in mind rather than information graphic design. This often results in colors being too bright, dark, or dull to be used together in a report. Sometimes the colors aren’t easily distinguishable from each other. Other times, the colors needed for various situations (main color, ancillary colors, highlight color, error color, KPIs, text, borders) aren’t available in the corporate palette.

You can still stay on brand and create a consistent user experience with a color palette optimized for data visualization. But you may not be using the exact hex values as defined in the corporate palette. I like to say the data viz color palette is “inspired by” the marketing color palette.

Click through for lots of goodies, including a link to a really interesting color tester.

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Rishi Khandelwal walks us through automating Grafana dashboard and alert creation:

We have already discussed the creation of Grafana dashboards and alerts in my previous blogs. We were doing that manually. But think of, if we need to do that in more than 10 environments then we need to repeat that manual process again and again and sometimes we get frustrated by doing all these repetitive stuff.

We should have some automated process for doing this. So let’s discuss that.

Read on for an example.

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Elizabeth Ricks shows how you can remove extraneous noise from a visual and make it easier for a person to understand what you’re trying to show:

This caught my eye not because of the topic but because of how much time it took me to figure out what information it was trying to convey. What should I do with this? There’s a lot competing for my attention in this chart and distracting me from the data.

Spend a moment examining this graph and take note of which specific elements are challenging. Make a list: what might we eliminate or change to reduce cognitive burden?

I came up with eight specific design changes I would make. How does my list compare with yours?

There is a subjective element to this, but the end result does look better.

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Lisa Charlotte Rost shows different sorts of callout lines and how they can affect the way a person views your chart:

After Defne’s awesome monster Weekly Charts in the last 14 days (here’s number 1, here’s number 2), this weeks’ Weekly Chart keeps it small. Today we launched callout lines for our locator maps (they are lines connecting marker and label), and I wrote a few words about how to create them and when – so I will simply refer you to this announcement blog post and explain the importance of our new feature with this map:

In moderation, they can be quite useful for indicating and explaining important but subtle points you’d like to make.

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