Modern data visualizations are often a great way to review design fundamentals.

A computer generated image of several layers floating on top of one another going from blue to red in a gradient.
A computer generated image of several layers floating on top of one another going from blue to red in a gradient.
Photo by Clark Van Der Beken on Unsplash

One of the most interesting things that I’ve found is that working with advanced visualizations often requires a sharper design eye.

While bar and line charts have been around for decades, there may sometimes be situations where a different (and newer) visualization is more suitable.

In these situations, I’ve found that my design fundamentals have been tested, as this often involves taking multiple types of data and encoding it into a single visual. And to illustrate this, let’s talk about a chart that’s emerged in the modern day: bullet charts.

If you were asked to combine qualitative and quantitative data…


The surprising influence flowcharts have in changing business processes

A Desire path (i.e. a path that’s worn into the ground that users take that differs from the paths that urban planners lay out) that goes up a hill. There is a sidewalk at the bottom of the hill heading to the right and university buildings behind, but the desire path goes through the grass and up the hill.
A Desire path (i.e. a path that’s worn into the ground that users take that differs from the paths that urban planners lay out) that goes up a hill. There is a sidewalk at the bottom of the hill heading to the right and university buildings behind, but the desire path goes through the grass and up the hill.
Source: Chrisinplymouth on Flickr

More than any other chart in my work life, Flowcharts have probably had a greater impact on changing my projects' direction, focus, and design.

But I never gave them a second thought until I had to generate two flowcharts at the same time: it was only then that I saw the impact they had on both the user and business process.

The process to create one is often quite tricky. But it’s not because flowchart design is overtly hard: it’s the opposite.

Because you’re often developing flowcharts with other team members, there may be multiple overlapping flowcharts that you could…


Creating scatter plots teaches you advanced lessons about color

A night sky broken up by a colorful pattern in the middle. A line of bright orange and red clusters of stars is in the middle, while the rest of the stars are normal and blue.
A night sky broken up by a colorful pattern in the middle. A line of bright orange and red clusters of stars is in the middle, while the rest of the stars are normal and blue.
Photo by Denis Degioanni on Unsplash

Scatter plots are usually charts that people are vaguely familiar with.

It’s a chart that’s often thought of as complicated and niche. But they can offer important lessons about color that you might not realize until you’re trying to create them.

And it starts with the type of message it’s trying to convey.

Scatterplots, distribution, and relationships

Scatterplots are mainly used for two major reasons: to show distribution patterns and relationships. They allow you to encode data on both the x and y-axis to see if relationships or patterns exist between two variables.

But they have a reputation for being hard to understand. This…


How thinking like a stock trader can help you interpret time-series data

A man sitting in front of two laptops with stock charts and market data open. He’s browsing different stocks on each of the laptops.
A man sitting in front of two laptops with stock charts and market data open. He’s browsing different stocks on each of the laptops.
Photo by Adam Nowakowski on Unsplash

Line charts, or time series charts, are one of the oldest and most common charts used to represent data.

But it can be one of the simplest to mislead with as well. The reason for this is simple: we often use line charts to try and predict the future.


Two techniques for adding quantitative depth to your qualitative research.

A lot of question marks scattered on a table. 3 ?’s are highlighted in orange while the rest are black.
A lot of question marks scattered on a table. 3 ?’s are highlighted in orange while the rest are black.
Image by Arek Socha from Pixabay

Working with large datasets has made me realize the importance of making sure people start from the same place. I recently had to summarize survey and remote testing findings from over 130 participants for my stakeholders, and to do that, I needed to use a quantitative approach.

The typical ways I presented user findings, such as utilizing user quotes, didn’t seem appropriate with such a large dataset. Still, I didn’t think that going with complex quantitative research methods would be very helpful either. So I adopted two Exploratory Data Analysis techniques to convey findings to my stakeholders.

“The greatest value…


The two-word data visualization revolution that changed the way I think about bar charts

Source: Lauren Manning on Flickr

Bar charts are the most common, least problematic, and usually dullest data visualization solution.

They’re usually suitable for nearly every situation: their visual cues support any data and context. You’ve probably seen a bar chart this week or generated one if you’re working with data.

You also probably forgot about it until I reminded you.

Visually, there’s nothing problematic about bar charts: they are accurate and standardized charts used to compare between different categories. But because bar charts are assumed to be “the default chart”, sometimes they’re generated without thinking. …


How to avoid turning your visualization into a data graveyard

My worst data visualization work has all been dashboards, and I recently realized why.

Part of learning Data Viz is experimenting with different types of visualizations to present information, and dashboards are something I’ve been looking into as I’ve started working with more complex datasets.

It’s a standard visual format that a lot of people have come to expect, but there are several catastrophic mistakes you can fall into when creating one.

To elaborate on this, one needs to look no further than Jared Spool.

A grey photo of a graveyard, with several graves in the front, a fence, and then a hill with the sun shining behind it.
A grey photo of a graveyard, with several graves in the front, a fence, and then a hill with the sun shining behind it.
Photo by Einar Storsul on Unsplash

“Dashboards are where data goes to die.” — Michael Solomon, Product Strategy Director


How I overengineered a worse solution by making an interactive visualization

A woman in motion walking on a crosswalk while looking the wrong way. Her feet are carrying her in one direction but her head is tilted to look in another.
A woman in motion walking on a crosswalk while looking the wrong way. Her feet are carrying her in one direction but her head is tilted to look in another.
Photo by Vicky Hladynets on Unsplash

I made a common User-centered Design trap when I tried to revise a bad visualization.

As a part of the process of learning Tableau, I’ve been exposed to a wealth of interactivity options. From dashboards to stories, I’ve been exposed to the world of dynamic visualization.

But I recently had to ask myself a question when remaking a bad visualization: rather than ask how to make it dynamic; I needed to ask if I should.

To explain my problem, I should talk about #Makeovermonday.

As part of #MakeoverMonday, I took a dataset from UNICEF that reviews the disparity between adolescent…


How to make sense of a complex visualization technique.

Several people walking in the dark while a several hanging lights are illuminating the rest of the room
Several people walking in the dark while a several hanging lights are illuminating the rest of the room
Photo by Robynne Hu on Unsplash

I didn’t understand the point of treemaps until I worked with a large structured dataset.

As part of an extensive user research effort, we collected open-ended survey data from 130 participants. After standardizing the data and doing thematic analysis, the next topic was to try and figure out a way to visualize it.

After analyzing the data, it was a dataset with over 20 themes and several different categories of respondents. This was a much larger dataset than I was used to visualizing: what was I supposed to do?

That’s when I first started to learn about treemaps. To understand…


What to do if your values don’t fit?

A picture of a dashboard, with bar and line charts. The bars of the bar chart are near the top of the graph.
A picture of a dashboard, with bar and line charts. The bars of the bar chart are near the top of the graph.
Photo by Luke Chesser on Unsplash

Sometimes, the data doesn’t exactly work the way you want it to.

When working with data, you might run into a fairly common problem: some values make it hard to fit everything on one graph;

Kai Wong

Top writer in UX Design. UX, Data Visualization and Data Science. Author of Data Persuasion: https://tinyurl.com/rndb9bw. Substack: dataanddesign.substack.com

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