Last week, I applied the optimist vs. pessimist style to a map during Office Hours.

(The real version of the map was about a different topic in a different state, but you get the idea.)

The Traditional Map

I’ve written about traditional and storytelling approaches before. You’re probably familiar with this terminology already?

The traditional version would have a topical title and regular ol’ brand colors.

The Storytelling Map: Optimist Version

In this optimist version, we’ve got a:

• “good” takeaway title;
• “good” brand color;
• “good” icon; and a
• call-out box highlighting a superstar.

The Storytelling Map: Pessimist Version

In this pessimist version, we’ve got a:

• “bad” takeaway title;
• “bad” brand color;
• “bad” icon; and a
• call-out box highlighting a laggard.

Which Version Should You Use?

That obviously depends on your audience and context.

You’re welcome to comment here with your own insights. I read every response.

Download These Maps

Want the Excel file that I used to create these maps? You can download it here.

The post Optimist vs. Pessimist Maps appeared first on Depict Data Studio.

Beyond Graphs 1: I made a Great Graph after just a Few Course Modules

Soon after the course started, I brought Ann data about who connected with me on LinkedIn after I was listed as one of Nick Martin’s 9 Amazing Humans to Follow. Nick has a HUGE network and I got over 69 connection requests in the first day. And requests continued for more than a week!

So I made a graph to go with a post on LinkedIn, applying all the learnings from the first few course modules.

Beyond Graphs 2: 2 Things I Learned in 10 Minutes of Help in 1 Office Hour Session

I examined the few data variables on the LinkedIn connection requests. My impression was validated. Only 2 of 136 requests had a personalized message (despite LinkedIn experts emphasizing the need to personalize connection messages).

I tried different ways to display this finding (waffle chart, pie chart, and this one). Luckily, Office Hours were the next day. (Office Hours are a CAN’T MISS opportunity for immediate feedback!)

Ann took one look and exclaimed, “Ooh, let’s try the WeePeople font!” (Well, maybe not exactly like that!)

She then quickly used WeePeople to show the data.

Learning 1: More relevant and representative visuals with icons showing diverse silhouettes

(Hooray – No more using just the standard male icon.)

And then Ann taught us all how to make a gif which was even more effective at telling the “only 2 of 136 people” story.

Learning 2: Using a gif can give readers a quick result from your data

And, for those who follow LinkedIn stats to see how their posts engage, the post with the bar graph got 4,765 impressions and the 2^nd post (the next day) with the gif got 8,778 impressions!

Beyond Graphs 3: Now I’m Applying a Mental Checklist to Graphs and Charts

No – not only to the few graphs I’m making.

The course taught me and heightened my awareness to look at all the visual elements in the many graphics we see each day. There was so much learning from the course modules. And then many great opportunities in Office Hours to learn from what others were working on.

Here are things I find I am automatically looking for in these graphics:

• Color choice(s)
• Is it a Traditional or Storytelling title?
• The clarity of the data message
• Is the graph or chart the best for that message and the data being used?
• What elements could be removed for better clarity?

And a Beyond Graphs Bonus: Consistency and Efficiency

I consider myself a digital pioneer. But I didn’t know what I didn’t know, even being a longtime Word, PowerPoint, and Excel user.

I jumped into the course, and my efficiency increased in the first week! The course started – not with graphs – with ensuring basics including branding by setting my color and font defaults.

And then, a couple weeks later, I set up branding for a 3-part seminar series I did for Waey, the Association for Community Health in Saudi Arabia.

And I now have the consistency across Excel, Word, and PowerPoint and across my different PCs. What a difference!

This is just the tip of the iceberg of everything I am doing differently after Great Graphs – Excel!

Connect with Sue Griffey

LinkedIn: https://www.linkedin.com/in/suegriffey/

Twitter: @SueMentors

Youtube: https://www.youtube.com/channel/UC-rjWX4ZmTdo0S3ssKbut_A

SueMentors Resources: https://suegriffey.fyi.to/suementors-resources-for-your-professional-presence

A no-cost short course: Build and Update Your Professional Presence in 4 Steps at this page: https://www.linkedin.com/company/4-steps-to-build-update-your-professional-presence

The post When a Course is More Than a Course: 3 Ways “Great Graphs in Excel” Was Beyond Graphs appeared first on Depict Data Studio.

Here’s my definition of data storytelling, in case you missed the previous blog post. 

Great!  

Let’s remove the guesswork from our graphs. 

The next step is to figure out which message we’ll highlight. 

We can’t visualization everything—that dilutes the power of our graph. 

What Makes a Useful Data Story? 5 Questions to Ask

Here are five thought-starter questions to help you uncover useful nuggets in your data.  

1. What’s Going Well? What’s Not Going Well? 

2. Did We Reach Our Goals? Why or Why Not? 

3. What’s Surprising? What Unfolded as Expected? 

4. Which Information Needs to be Shared with Others? Who Needs to See This Information? 

5. What Increased Over Time? Decreased? Stayed the Same? 

If you analyze data for a living, then I bet you’re already asking yourself these questions. You were probably trained to approach data this way in grad school. Or, it’s become intuitive after years of on-the-job practice. 

Dabblers in data, this one’s for you.  

Newcomers to data, this one’s for you.  

What’s Going Well? And What’s Not?

Everyone loves a success story.  

Look through your dataset.  

Find the good news and highlight that finding with dark colors and takeaway text.  

I often start with positive findings so that my audience can celebrate a small victory right away. 

But, facilitating an honest conversation with data visualization requires balance.  

After presenting positive news, I present the less-than-positive news.  

For example, the next graph in my report might use a darker color to draw attention to something that isn’t going well. 

Did We Reach Our Goals? Why or Why Not? 

I consult to dozens of grantmakers and grantees each year—Federal, state, and local government agencies, foundations, and nonprofit organizations.  

In the grantmaking world, it’s common for funders to ask their grantees to explain whether they are meeting their targets.  

For example, one goal of a parenting program for teenage mothers and fathers might be to avoid repeat pregnancies. The health centers and high schools that are running the program might have to report whether there was, in fact, a decrease compared to a control group.  

Graphing these goals is an obvious choice. 

What’s Surprising? What Unfolded as Expected? 

Take off your data nerd hat. 

Put on your human hat.  

Step outside the math for a bit.  

Trust your gut instinct.  

I look for numbers that are surprising and unexpected.  

What’s surprising to you, personally?  

Surprising new facts make for interesting reports.  

Nobody wants to read the same old stories over and over and over.

Which Information Needs to be Shared with Others? Who? 

This thought-starter question keeps your data actionable.  

Examine your numbers.  

Who, in particular, needs to see these numbers?  

Think about all the different people who are involved in your project.  

Are there certain takeaway findings that your boss should probably know about? Or the boss’ boss? Or someone outside the organization?  

Who might act differently or make a different decision based on this new information?

What Increased Over Time? Decreased? Stayed the Same? 

Most projects have numbers available at multiple points in time.  

Examine how your numbers are changing over time, if at all.  

Sometimes a number will increase over time. Other times, a number will decrease over time.  

And other times, you might not notice any difference whatsoever. Flat lines can be useful, too! 

Data Storytelling Example: Highlighting a Flat Line in a Workforce Development Project 

I changed around the details, but this example is based loosely on a past project.  

Let’s pretend that you’re leading a career coaching program for adults who recently immigrated to the country. I consulted on a project like this a couple years ago.  

The purpose of the career coaching program was to get those adults into higher-paying jobs.  

A few times a year, the career counselors collected data on the participants. For example, they asked the participants how much they were being paid. The career counselors might even verify their wages by looking at pay slops or tax forms.  

The person responsible for compiling all this data should see whether wages are improving, declining, or staying steady.  

Imagine that you uncovered that wages for most program participants were staying steady—despite hundreds of thousands of tax dollars being poured into this program. 

That flat line has to be shared and talked about! Something needs to be adjusted ASAP. 

Your things-stayed-steady-over-time graph might look like this. 

We applied several data storytelling techniques. I bet you recognized them right away: 

1. We’ve got color contrast (all 30 participants’ individual lines are grayed out, and the average is highlighted in a darker brand color). 

2. We’ve got a takeaway title (“Wages Did Not Increase”). 

3. We’ve got numeric labels on a handful of key data points (the $18.27 average wage at the beginning, and the $18.30 average wage at the end). 

4. We’ve got (light) narrative annotations (“Average hourly wages: $18.30”) 

Looking for Useful Stories throughout the Analytical Process 

When do you look for possible data stories? 

Not the day before your project’s due!!!!!!!!!!!!!!!!!! 

Revisit these questions at each stage of your project’s analytical process. 

Look for Useful Stories in the Raw Data 

I start with my spreadsheets of raw data.  

I ask myself, “What’s going well? Did we reach our goals? What increased over time? What’s surprising? Which information needs to be shared with others?”  

I keep a running list of interesting nuggets in a notebook. 

Look for Useful Stories as You’re Compiling Tables for Your Appendices 

Later, I compile my analyses in tables. The tables often go in the appendix of a technical report.  

This means that I write the last pages of my report first.  

As I’m designing the tables, I ask myself those five questions again, and I add to my running list. 

Look for Useful Stories as You’re Designing Your Full Reports or Slideshows 

Next, I write my report (or create my slideshow, or whatever the finished product will be).  

I look through the tabulated data as I’m designing the report: Which numbers deserve to go into the body of the report?  

Look for Useful Stories as You’re Designing Summaries (One-Pagers, Infographics, Briefs, etc.) 

Finally, when my full report/slidedeck is complete, I pull out graphs that are so interesting that they deserve to go in a summary.  

I’m using the word summary loosely here.

A summary could be a one-page handout, an infographic, a shorter brief, etc. 

Yes, this is the place for those stories to shine.

Yes, you should’ve found stories along to way to include in your summaries. Hopefully!!!

This stage gives you one more chance to think carefully about useful gems in your dataset.

Don’t wait until the end of a project to think about the “so what?”  

This should be an ongoing, intentional process.  

When we think deeply about the data, our audiences will benefit from the added clarity. 

Your Turn 

What’s your process for uncovering interesting stories in your data? 

Do you have more thought-starter questions to add to the list? 

The post What Makes a Useful Data Story? 5 Questions to Ask  appeared first on Depict Data Studio.

That was the overarching theme of my Lightning Talk at the DATAcated Expo, which was held virtually in October 2021.

You’re not going to keep the ugly, outdated defaults. Great!

But what should you do instead?

And how do you modify a graph so that it’s just right for your audience?

Surely a group of scientists will need something different from a group of policymakers.

Some audiences adore data. Others don’t.

Some audiences have plenty of time. Others don’t.

In this blog post, you’ll learn about:

• the differences between default, traditional, and storytelling graphs;
• which techniques can help you tell a story with data (e.g., dark colors); and
• when to use each type of graph.

Watch the DATAcated Expo Lighting Talk

Missed the live event?

Watch the Lightning Talk here.

This is a 17-minute video. If you’re short on time, just watch a 10-minute segment — minutes 2 through 12 of the video.

Here’s a summary of what’s inside.

Defining the Term “Data Storytelling”

This is a tricky term with lots of definitions.

Some people love this term.

Others hate it.

In the recording, you’ll see me ask the attendees to share what “data storytelling” means to them.

You might define data storytelling as:

• “What does data really mean, and what do you want it to tell.” — an Expo attendee
• “Translating data for non-data centric users.” — an Expo attendee

And data storytelling is NOT:

• Fiction
• Making things up
• Biasing our audience
• Fudging the numbers

Data Storytelling in a Bar Chart

In the Lightning Talk, I showed attendees three versions of the same graph: default, traditional, and storytelling.

We’ll look at each of these side by side, so that you can see how they’re similar and how they’re different.

At the end, I’ll ask you to comment and share which style you think each of your audiences need.

The Default Bar Chart

We never, ever keep the default settings.

The Traditional Bar Chart

Instead, at a bare minimum, we need to design a traditional graph.

We would:

• Enlarge the font
• Enlarge the bars (by decreasing the gap width)
• Remove the border
• Add labels (optional—if we think our audiences would want specificity)
• Adjust the scale
• Use brand colors
• Use brand fonts

It’s up to the viewers to read the chart and figure out the “so what?” for themselves.

The Storytelling Bar Chart

Sometimes, our audiences prefer storytelling graphs.

You already spent 60 seconds cleaning up the default settings.

In another 60 seconds of editing, we would:

• Sort the bars (e.g., greatest to least)
• Gray everything out
• Highlight one takeaway finding with a dark color
• Add the takeaway finding to the graph title
• Bold a few key words to make the title even more skimmable

Data Storytelling in a Slope Chart

You can apply these principles to any and all chart types.

Here’s what the three different styles look like in a slope chart.

(A slope chart is just a fancy name for a line chart that has exactly two points in time.)

The Default Slope Chart

Defaults are for 2005.

We know better.

C’mon, Excel. And Tableau. And PowerBI. And and and.

The Traditional Slope Chart

At a bare minimum, we need to:

• Enlarge the fonts
• Adjust the scale
• Remove the border
• Add brand colors
• Add brand fonts
• Remove the legend and directly label the data

(Direct labels have three key advantages: They’re faster to read; they’re better for people who are colorblind; and they print better in grayscale.)

The Storytelling Slope Chart

Take the edited graph you just made, and keep going!

In a storytelling slope chart, we would:

• Gray everything out
• Highlight one thing at a time
• Re-write the title and put the takeaway message in the title
• Bonus points: Bold a few key words to make it even more skimmable

Which finding will you highlight in a darker color?

You might highlight:

• The Success Story (Project A)
• The Debbie Downer Story (Project C)

Be careful with red; in Western cultures, red means caution! warning! But colors are culturally-specific; in Eastern cultures, red doesn’t necessarily mean anything bad.

Data Storytelling in a Scatter Plot

We didn’t have time to discuss scatter plots at the DATAcated Expo, but I’d still like to share this example with you.

Here’s how data storytelling would be applied to a scatter plot.

Never keep the default settings!!!!!!!!!!

Traditional graphs are all one color and they have topical titles.

Storytelling graphs have a dark-light contrast and takeaway titles. For bonus points, you could label a few key points.

Data Storytelling in a Map

Finally, here’s how data storytelling would be applied to a choropleth map.

Never keep the default settings!!!!!!!!!!

In traditional maps, none of the colors stand out, and they have topical titles.

In storytelling maps, we’d add an intentional dark-light contrast and takeaway title. For bonus points, you could label a few key points.

When Should You Use Data Storytelling?

Comment below: When would you use each style?

Which of your audiences prefer traditional graphs?

Which of your audiences prefer storytelling graphs?

In the video, you’ll also hear the conference attendees share their perspectives, and you’ll hear from me, too.

The post Are Viewers Expecting a Story? Lightning Talk from the DATAcated Expo appeared first on Depict Data Studio.

_________________

I work at a grantmaking organization and it is my job to update the board three times a year on the grant applications we are considering for awards, the grantmaking budget, and other information.

You will see below an example of the grants docket (slate of grant proposals under consideration) as well as a grantmaking budget summary. I am almost too embarrassed to show these!

Fortunately, after completing about 70% of the Dashboard Design course, I was able to revitalize a tired spreadsheet into an information powerhouse and a colorful budget visualization into a professional presentation.

It’s the same information, just presented differently!

The Docket

Before: Gridlines Galore and No Visualizations

This docket was printed on 11×17 paper for easier readability and called a “placemat”.

Gridlines, some highlighted cells in yellow to draw the eye.

A lot of information and a bit overwhelming to take in.

Applications were presented alphabetical by organization and not grouped in any way.

After: Fewer Columns, Bigger Impact

When I started the Dashboard Design course with Ann, my intention was to revamp our organizational dashboard and not even this document (once I get that done, I will have to write another blog!).

But the course showed me what little changes I could make to have a big impact.

Although we do not compare numbers over time in this document as it is just a list of our applications, I was still able to use some helpful visualizations, icons, colors, and conditional formatting to tell a story.

A summary of the changes made:

• Added logo and subtitle
• Used brand colors in headings and their corresponding icon
• Minimized the use of gridlines
• Removed highlighting of a column but used font colors for key info
• Grouped applicants together by type of grant (general operating vs. project)
• Hid the column with the organization budget, but added an icon set of pie charts via conditional formatting
• Correct alignment for numbers vs. text
• Deviation bar visualization to show the percent change of the current request from their previous award instead of just expecting the reader to do the math
• Added a budget summary chart at the bottom

This resulted in a much more well-received document with lots of compliments from board members!

I have begun creating a cohesive aesthetic for all the documents I produce for the board to give them the information they need but in the most streamlined way possible.

The Budget

In 2019 I developed the below budget visualization to up my game a bit from a spreadsheet that board members had a hard time understanding.

This was an improvement over the previous version, but Ann’s course challenged me to turn it up a few notches.

Before:

After:

There were not many “big” changes to this in terms of conditional formatting or visualizations, but it has definitely been toned down.

The biggest change was that I replaced the text explanations with icons to illustrate where we are in the budget, which the board members appreciated.

As you can see, there is that cohesive title, subtitle, font, and color choice to keep the branding in line.

Take the Time to Take the Course!

I am pleased with what I am able to do now that I have completed Dashboard Design. The lessons were fun and I was able to put my skills to the test in a matter of weeks!

Now I am enrolled in Simple Spreadsheets which will really provide me a good base as my role stretches me to do more community engagement data evaluation and learning. Thank you, Ann!

Connect with Kristen

LinkedIn: @summerskristen

Lift every Voice 216: https://www.facebook.com/216lift

Saint Luke’s Foundation: https://www.facebook.com/saintlukesfoundationcleveland

The post Better Storytelling with the Same Data: Upgrade that Board Packet! appeared first on Depict Data Studio.

Friends, Esther’s data visualization edits are game-changing.

In one of her presentations, for example, Esther swapped her bullet points for engaging visuals. She added graphs, diagrams, screenshots, logos, and photographs. Every visual had a purpose. Every visual helped to better communicate her research project’s key takeaway findings.

Keep up the great work, Esther!!!

Taking Ann’s Introduction to Data Visualization course through The Evaluators’ Institute (TEI) was the most immediately applicable skills-building workshop I have ever taken. Not just at TEI, but ever. Within days of completing her course, I began going through every presentation or report that I was working on and began Emerizing them. My colleagues and friends took notice and started asking me, “Where did you learn that? Teach me that!”

I wish I could do her material justice. Ann has such a great way of delivering examples and tips for better data visualization. Each lesson makes you feel like you’ve always known it to be true, but just needed someone to show you how to make it so. She provides the perfect balance of theory and practice so that methodologists like myself appreciate the research behind data viz methods and then also know how to create the most appropriate visualization(s) for my data.

You can feel tension releasing in the room as she demonstrates before and after transformations of some of the most heinous visualizations that we have all done at one point in our lives—admit it, you’ve created a 3D pie chart. Hindsight is truly 20/20 because now there are things I just cannot unsee, like old reports or presentations that I am now too disgusted to ever look at again.

Now since I’m one class wiser, the only way I can give back is by continuing to spread the data viz gospel to others who are on this journey as well.

Here are the three most used lessons that I took away from Ann’s class.

1. Never Guess Colors Again

Ann taught us about the importance of using your company’s or client’s color palette to better visualize your work. Part of your/their branding should be that all outward-facing media align with the company’s theme. Again, this was not necessarily new information to me, but it was a healthy reminder because I was previously most concerned with the differentiation of categories. Ann taught us three things about colors that I now use with every data visualization.

Use a Color Identifier Tool

If you’ve ever been like me, you might have guessed at the closest shade to a color that you were trying to select. This might start with selecting a generic purple, for example, and then using the hue slider until you decided to accept the difference was negligible. She showed us a neat (FREE!) tool called the Instant Eyedropper (http://instant-eyedropper.com/), which will give you the color code for any color that you hover over on your computer screen.

Save Color Palettes

I was always reentering colors for new presentations and documents for the same company. This was exhausting and frustrating! I had enough sense to write down the RGB codes on a Post-It note for one company’s palette that I most frequently used and stuck it on the corner of my monitor. However, Ann taught us to save frequently used color themes in our Microsoft programs (see figure below) so that they could be easily selected and used across documents. Once saved in one program, it would be available across all Microsoft programs on that computer. Voila!

Use Colors to Tell a Story

Sometimes, we use colors to differentiate every category from one another. Ann really emphasized the use of colors for story telling because, maybe, you just want to highlight one category/statistic apart from all the rest. Accompanying this with a meaningful call out box or title are tricks that were game changers for me.

2. Less is More

You really don’t realize how much unnecessary stuff we include in data visualizations until Ann starts stripping them away in front of you one piece at a time and you realize…you don’t really miss it. Coming from academia, my traditional sense of formatting charts and tables were defined by some formal style guide. When given the freedom to break the mold, you begin to appreciate what is essential and what really is not. This minimalist approach helps to declutter your delivery and helps your audience appreciate the message you are trying to convey.

This lesson also came in the form of making your written work concise and palatable. Ann’s 30-3-1 approach is a hard but good lesson in focusing on the aspects that are absolutely necessary to be shared? If you’re not sure about your storyline or take-home message(s) to feature, this approach has helped me draw them out because you are forced to keep only what is pertinent for your audience to follow.

3. Work Smarter, Not Harder

Although Ann recommended fancy software options for data visualizations, she also took us back to the basics. For those of us who may not be able to or want to invest in expense software, Ann showed us how to take advantage of programs like Microsoft Office to produce useful visualizations. Many techniques are not very complex and can be duplicated or modified for future uses as well!

In short, I really enjoyed Ann’s class and continue to apply these skills regularly in my work. Although I still have much to learn, Ann continues to be a wonderful resource and cheerleader to support my growing skills in data visualization. Thanks, Ann!

The post Work Smarter, Not Harder, When Visualizing Data appeared first on Depict Data Studio.