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I was ecstatic to register for the Great Graphs: Excel How-To’s course and transform my graphs using Excel instead of online data visualization software. I already had Excel, my data was there, and it did not make sense to use another platform to make the graphs I needed.

I had tried other options before, and it was always clunky because instead of focusing on the data and the message I was trying to show, I was inevitably focusing on the different software programs and how they would interact.

Tips, Tricks and Community

Through the Great Graphs: Excel How-To’s course, I learned more tips and tricks than I ever expected. But the most impactful, yet unexpected, outcome was joining a community and making connections with others that were also along their own journey of improving their graphs as a way of sharing messages and insights through data.

Throughout the course, I was exposed to new ways of thinking about graphs, Excel, and how data visualization can be used in the workplace to guide more data-informed decisions.

Now, I look at data visualization differently.

When I see data visualization in the newspaper or on social media, I think about what it was trying to show and whether it was successful. This critical eye helped me be a better peer and colleague when seeking to get the message across from my own data and in collaborating with others.

As we started the course, I got to know my peers also in the course.

When I was working on producing graphs for a blog based on my graduate work in evaluation surrounding the perceived effects of medication access from COVID-19, we reviewed and workshopped them together with live feedback in an office hour session.

Because of the varied backgrounds of those in the course, I was thinking about my work in a completely new and improved way. I started experimenting with graph types I had not used before, with some having greater success than others.

Before: Audience Left with Questions

My original graph applied many of the lessons we had covered – I used my color palette, I had storytelling titles, but there was room for improvement.

There was much left up for the audience to discern when reviewing the graph – was the 85% or 70% most important? Why were different values mentioned in the heading?

Live Editing During Office Hours

During the office hours, we mocked up what the graphs could look like and ultimately edited them to produce higher quality, easier to understand visuals. We took the original column charts and turned them into 100% stacked bar charts.

After: Clear Focus

Focusing on the major effects (both positive and negative) also provided a stronger narrative instead of hoping the audience would understand the message.

 Highlighting the ends of the bars gave not only visual focus, but a clear visual.

The final product was influenced by the feedback I received throughout the course. Getting feedback from the group changed the style of the graphs, colors, way I utilized text, and I was even encouraged to turn the graphs into a GIF for better online engagement.

Shared Learning is the Best Learning

As much as I looked forward to continuing the lessons, I was excited for the live office hour sessions when I would get to hear from someone else in the field and learn how they might use a graph style as well as how they did it.

Questions and discussions sparked thinking and problem solving about my data visualization in new ways, past the technical work in Excel.

As the course continued, my skills improved, and so did my connections with my peers. We connected on LinkedIn and offline to get to know more about each other’s work – similarities and differences abounded.

I found a great benefit in this as there was so much knowledge to be shared, wherever we were along our data visualization or professional journey. As a new evaluator, learning what others found to be successful also helped me in thinking about what I could apply to my own work.

The supportive community that was built goes beyond the technical skills we learned throughout the course, as I know I have a wider network to connect with for questions, feedback, and advice long after the course ended.

Connect with Maddison

LinkedIn: @maddison-staszkiewicz

Website: www.maddisonstasz.com

The post Creating a Community through Graphs appeared first on Depict Data Studio.

Before

Here’s the “before” graph from a recent project. The categories and numbers are different, but you get the idea. Most stacked bars look like this—there’s too much going on for the graph to be useful.

After

At a bare minimum, we need to declutter the default graph. You need to:

• remove the border;
• remove the vertical grid lines;
• declutter the horizontal axis (0 to 100 percent is plenty of detail—no need to label each of the 10 percent increments);
• move the legend to the top (since the legend is critical for understanding the graph);
• re-color the words in the legend to match the colors in the graph;
• declutter the title (short and sweet for slideshows, please);
• add numeric labels;
• outline the rectangular shapes in white (so the colors don’t bleed together); and
• reduce the gap width.

Check out the click-by-click breakdown:

Storyboard for a Live Presentation

The decluttered version is easier to read… but it’s still too dense for a slideshow. Here’s how we can “declutter” the graph so that our audience can follow along without wanting to bang their heads against a wall.

First, show the “completed” slide and provide a brief overview. I would say, “Let’s talk about our market share compared to our 4 competitors. We’re looking at five different products, A, B, C, D, and E.” You’d only spend a few seconds on this slide.

Second, dive deeper in the details. Focus attention on the ABC Org with dark colors. I simply changed the other segments of the stacked bar chart to an 80% transparency. I also selected lighter colors for the legend. You would say, “Here’s how we’re doing. Check out Product C–that’s where we hold 80% of the market share.”

Third, focus attention on Competitor 1 with dark colors. You would say, “Here’s how Competitor 1 is going. They hold 30% of the market share in Product A, but they don’t offer Product C at all.”

Fourth, focus attention on Competitor 2. You know the drill! You would say, “Competitor 2 holds a smaller piece of the market—between 7 and 13 percent for these five products.”

Finally, conclude your storyboarding with the “finished” slide again. Invite your audience to ask questions and open the floor to discussion. You would say, “Here’s that same overview again. I won’t go through Competitor 3 and Competitor 4 in detail, but you can see how they’re doing, too. Next, let’s talk about what these numbers mean for our future work…”

Storyboarding–guiding your viewers through one piece at a time with dark colors–guarantees that your audience will be looking at the data, and not scrolling through their phones. How are people supposed to make decisions based on your data if they’re not even paying attention to you? Break up dense visualizations into multiple slides. Your audience will thank you.

Bonus! Download the Materials

Want to explore how I edited the graph? Download the spreadsheet.

Bonus! Watch a Sample Class

I’m teaming up with 13 guest experts to bring you Great Graphs, an online course about getting your data out of spreadsheets and into real-world conversations through better data visualization, reports, slideshows, and dashboards.

Want to see what the storyboarded slides would look like and sound like? Watch a sample class:

Great Graphs begins October 1. The course only opens once a year and we’re only able to take 100 students. Reserve your spot today before they’re gone!

The post How to Declutter Your Cluttered Stacked Bars appeared first on Depict Data Studio.

A few years ago, I found myself in a frustrating predicament. I was writing dozens of evaluation reports for my grantees. Next to none of them were being read. I scheduled webinars, conference calls and face-to-face meetings to talk through the results. Each time, it was more or less the same: I was verbally summarizing a report no one had read. I assumed folks were too busy, or that they didn’t really care that much about the results. Then I went to one of Ann’s data visualization workshops and had a heart-to-heart with myself. The problem wasn’t my grantees busy schedules or lack of interest in outcomes – it was the report.

Before: Six Pages of Boring Text and Yawn-Worthy Tables and Graphs

I was sending my sites six pages of mind-numbingly boring text, accompanied by a few yawn-worthy tables and graphs:

I was making my readers work way too hard. Instead of reporting agency outcomes front and center on page one, I was expecting folks to wade through six pages to find them, each on a separate page. Instead of titling my table and figures in a way that drew attention to the take-home message, I was using titles like ‘Figure 1. Percent of Respondents Who Correctly Answered Questions on Sexual Knowledge.’ Instead of using color for emphasis, I was using color with the enthusiasm of a toddler. It was time for a reboot.

After: A Two-Pager with Intentional Data Design

I started out by talking to my grantees. I wanted to make sure that this time around, they got something they would actually read. And hopefully use. My sites told me that my suspicions about what was wrong with the reports were absolutely right. I wasn’t making it easy for them to find what they needed, and for the few who actually read the report, they were having to repackage what I sent them to share with their stakeholders.

Based on Ann’s data viz best practices and the feedback from the grantees, I made myself the following to do list:

• Don’t bury the lead. Start off with the important stuff, then follow with details.
• Limit yourself to one page (front and back).
• No blocks of narrative text!
• Use color for emphasis.

Here’s what I came up with:

On page one, after the title, I listed all three outcome objectives, the target and actual values, and add a check mark to clearly identify which had been met. The green in the title matches the green in the arrow.  The dumbbell plots that follow clearly show pretest and posttest values for each objective if folks want to dig deeper.

Page two is reserved for data about program implementation and demographics. (In my previous report, these hogged all the space on page one.)  I designed a snazzy new graphic to depict whether or not the grantee met the target number of youth at the correct dosage level. For the demographics, I created side-by-side bar charts so readers could more easily compare the age, gender and race/ethnicity of the two groups.

The Result

It’s not perfect, but it’s better. Much better. I pilot tested the changes with a few sites (the lucky few who actually read the six page report), got their feedback, and made a few more tweaks. The response was overwhelmingly positive: Grantees could easily find their results, make sense of them, and communicate them with their stakeholders. What more could a girl ask for?

The post It’s Not You, It’s Me: When You Realize Your Reports, Not Your Readers, Are the Problem appeared first on Depict Data Studio.

Survey Results

Last month I spoke to Harvard University graduate students about visualizing survey results. I made up some fake numbers to accompany their real survey questions.

Stacked Bar Chart with Diverging Color Palette

Option A is a stacked bar chart. I chose horizontal bars over vertical bars because I arrange ordinal variables from left to right across the page. I also chose to display numbers of people instead of percentages. I use numbers when we’ve got fewer than 100 responses and I convert those numbers into percentages when we’ve got more than 100 responses. Agree/disagree survey scales are diverging variables so we need a diverging color palette. I selected two hues (blue and magenta). The most saturated version of each color goes on the outer poles. The only problem with this traditional approach is that the graph is a bit crowded. Where are the viewers supposed to look? What’s most important? When everything all at once is competing for attention, it’s easy to lose viewers.

Draw Attention to Strongly Agree or Strongly Disagree

Option B draws attention to the strongly agree responses. This is a good option for personalities who like to see the world through rose-colored glasses.

Option C draws attention to strongly disagree responses. This is a good option for internal audiences or for audiences with whom you’ve already built some trust and rapport. Very few viewers have an appetite for bad news.

I often use a combination of these two styles when presenting slideshows to live audiences. On the first slide, I show the good news. A few moments later, I show the bad news on the next slide. Then, we pause and discuss what might’ve led to these results.

Collapse the Categories

Option D combines the strongly agree and agree responses and the disagree and strongly disagree responses. This approach can declutter a crowded graph; focus viewers on big-picture patterns; whet their appetite for additional details when they’ve got time to spare; or satisfy their appetite for just a bit of information if they’re in a hurry.

Diverging Stacked Bar Chart

Option E is a diverging stacked bar chart in which the agrees slide over to the left and the disagrees slide over to the right.
Option F draws our attention to the poles.

Square Pies

Finally, I built four square pies that draw attention to the strongly agree and agree responses. Square pies are a good option for small n‘s because each shape subtly reminds us that we’re talking about a small group of real human beings. You could take this concept a step further with illustrated icons instead of squares. But good luck finding an icon to represent feeling connected to a museum or having your voice valued…

Bonus: Download the Materials

Purchase the templates

The post How to Visualize Survey Data with Crowded Agree-Disagree Scales appeared first on Depict Data Studio.

Then, fine-tine your bar chart. Try your numbers as a regular bar chart and as a stacked bar chart. There are subtle differences between the two options and it’s worth discussing the pros and cons of each approach with your coworkers before publishing your work.

Option A: A regular bar chart

A bar chart should be your go-to chart. No, not a donut, a radar, or a spaghetti line graph. Those are tempting options, I know! Stick with the basics. Don’t distract your viewers with elaborate graphs. Let them look past the graph and think about what the numbers actually mean.

Option B: A stacked bar chart

Light gray shading reminds your viewers that the blue section is only part of the story. No, I didn’t label the gray section. Yes, that was on purpose. Keep the focus on what matters–the blue.

Five years ago, when I was an in-house data person for a large nonprofit in DC, the regular-versus-stacked-bar-chart conversation went like this.

Me: “Hello there, High Up Manager. I’ve got drafts of that important slideshow for the Board to share with you.”

High Up Manager: “Why thank you for being so prepared. I always appreciate a number-cruncher who takes time to look at the numbers in a lot of different ways.”

“My pleasure. Here’s the first visualization: A bar chart showing the number of youth in our programs that graduated from high school on time. Tell me what you think.”

“Wow! We’re doing great! Especially the Category A youth! I wasn’t sure if we were on track! We won’t have to change our programmatic approach at all because everything is going swimmingly!”

“Okay. Now here’s the second visualization: Another bar chart, but this time, the number of youth that didn’t graduate on time is shaded in light gray. Your thoughts?”

“Hmm. Does that gray section show the students who didn’t graduate on time? That’s a pretty big proportion of the graph! I had a feeling that there were changes to make. This is a good reminder that there’s still room for improvement. This is the version we need to show the Board at next month’s meeting. But first, let me run down the hall and share the numbers with our program staff so we can start acting on the data.”

Verbatim? Of course not. The gist of what you should be discussing with your teammates? Absolutely. There are a dozen correct ways to visualize every dataset, and the regular vs. stacked conversation should be part of your drafting process.

Download the template.

The post Bar Charts: Regular or Stacked? appeared first on Depict Data Studio.