There’s a small extra step that you can take to make written presentations much more effective.

Create a persona of your stakeholders based on their data needs.

This was an idea that I encountered while taking a course on Tableau: it came up unexpectedly when talking about creating visualizations for an audience.

But it made sense the more I thought about it.

Especially if your ideas, not you, are going to be at the meeting.

Live versus written presentations

Until now, I’ve talked with the assumption that you’d be giving your stakeholders a live presentation. However, that’s not always the case.

You may be asked to create infographics, written reports, or other e-mailed summaries for your stakeholders. Whether your team member was out sick or it’s another person that needs to be part of the team, you may just have to hand a presentation over. And that’s when it can get a little tricky. …

One of the hardest skills for me to learn was how to present to stakeholders effectively. After spending days, weeks, or months doing user testing, collecting data, and analyzing it, it almost seemed impossible to condense that into a single hour with stakeholders.

Many times, I either presented too much data, didn’t highlight key points, or otherwise confused stakeholders with the terminology they were unfamiliar with. I learned how to plan out my presentations, but they never seemed to be as effective as I wanted them to be.

But visiting the world of data visualization has not only taught me a better way of planning my presentations. It’s given me a new appreciation for a UX method that many of us already know: storyboarding. …

One of the questions I’ve gotten after writing about Data Visualization has been how to start learning the subject. It’s a more complex question than I realized, and it’s partially due to the prevalence of Data Science. If you’re a UX Designer interested in learning Data Visualization, but you’re a little intimidated by Data Science and coding, here’s the better approach to take to learn it.

Why data viz is always lumped with data science

When I first was interested in learning more about Data Visualization, I did what anybody would do: I googled learning resources for it.

And nearly every single result that came up had to do with Data Science. …

When I recently had the chance to sum up my research findings with visualizations, I found some gaps in my creation process.

I had learned about each part of the process, but putting it together from start to finish was another story: I was stuck with too much-unfocused data, with little clue what to cut down for effective visualization.

So when I went to seek out more knowledge about the subject, I found a guiding creative process from an unusual source: Journalism.

Here’s how I adapted their approach for my UX.

The explanatory-exploratory gap

I’ve talked about this before, but one of the crucial things you must realize when you start doing user research is that you become an expert about the data. …

I didn't really understand perception and cognition as a UX Designer until I started learning Data Visualization. When I was learning UX, there was always some time dedicated to understanding human perception to explain Gestalt principles and colors. But it wasn’t until I tried to visualize more complex ideas that I truly began to understand the importance of perception and cognition in Data Viz.

Because I didn’t understand those topics, I made a mistake that most people make when visualizing a complex topic. Here’s how to avoid that.

Why perception and cognition matter more in Data Viz

In UX Design, perception plays a vital but mostly secondary role in the Design Thinking process. Perception is often related to navigation through a site, completing tasks, or noticing changes based on their actions. …

The pie chart is one of the most hated charts in all of the visualization, and it became that way because it was misused. They are less accurate than other charts based on the elements of their visualization and should have only be used for niche scenarios. However, because they were used for nearly everything, they’ve garnered a reputation for being ineffective, lazy, and just plain bad.

But pie charts are still useful for certain types of goals, and it highlights the importance of choosing the right chart to answer your questions. …

I’ve been able to maintain a consistent writing schedule for over a year now, despite all of the turmoil that 2020 has brought.

For 52 weeks, I’ve published something consistently at least once a week, sometimes twice. This is on top of work and other personal projects that I’ve been working on.

I won’t say that it’s all been amazing writing (in fact, there’s one I unlisted because it was so bad), but I’ve been able to be productive based on one rule.

Whenever I’m stuck, it’s because I don’t know enough.

That rule alone has helped me overcome creative blocks more than I can count. …

What makes a good visualization? That’s a question I had to ask myself when one of my early visualizations was heavily critiqued. And it was an answer that I found when reading Alberto Cairo’s The Functional Art: An introduction to information graphics and visualization.

According to him,

“A good graphic has two basic goals: It presents information and then allows users to explore that information.”

That extra step, allowing exploring the information, is crucial in order to promote user understanding. And to understand this viewpoint, let’s revisit the Information-Knowledge gap.

DIKW and the User side gap

Previously, I talked about the DIKW information model (Data, Information, Knowledge, and Wisdom) and how structuring data efficiently can only get you so far. …