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