I recently saw a boring, black and white table as I was scrolling through LinkedIn.

The topic caught my attention—it was about Hispanic adults living with HIV—but the poorly-formatted table wasn’t making the patterns easy to understand.

I had a 30-minute window before I needed to pick up my kids from school, so I dove in!

In this 7-minute summary, you’ll learn:

1. bare-minimum edits for tables (alignment, decluttering, etc.);
2. a few different ideas for visualizing percent changes (checkboxes, slopes, deviation bars, icons); and
3. the winning design.

What’s Inside

• 0:00 Intro
• 0:18 The original table
• 1:06 Remaking the table with the same formatting
• 1:23 Gray horizontal lines
• 1:31 Left-aligned text
• 1:48 Trend lines
• 2:27 Percent changes with deviation bars
• 2:53 How-to tips in Excel (sparklines, data bars, IF statements)
• 3:28 Checkboxes to show increases or decreases
• 3:52 Adding state icons with the StateFace font
• 4:27 Adding arrow icons
• 4:54 Narrowing-down the best ideas
• 5:25 Re-sizing the columns
• 5:40  Not so Debbie-Downery

Download the Spreadsheet

It’s here.

The post Visualizing Percent Changes appeared first on Depict Data Studio.

In this video, you’ll see more than a dozen different charts that are possible inside Microsoft Excel.

What’s Inside

• 0:00 Intro
• 0:17 Dataviz On The Go
• 0:36 3 Levels of Excel Skills
• 1:05 Level 1: Overused Native Charts (Bars, Lines, Pies, etc.)
• 2:35 Level 2: Underused Native Charts (Combo, Tree, Sunburst, Maps, etc.)
• 4:01 Level 3: Non-Native Charts (Population Pyramids, Dumbbell Dots, Lollipops, etc.)
• 5:02 Disguises Needed for Non-Native Charts
• 5:54 Interactive Dashboards in Excel (Excel Tables, Pivot Tables, Pivot Charts, and Slicers)
• 6:16 Static Dashboards in Excel (Made in Excel, but Saved/Shared as PDFs)
• 6:41 Your Turn
• 6:55 A Personal Note

3 Levels of Excel Vizardry

I’ve taught data visualization in Excel a dozen different ways over the years.

Nowadays, I teach Excel dataviz based on the degree of behind-the-scenes hacking needed to produce that chart.

We start easy. Then, we work up to harder battles.

Here are the three Levels of Excel Vizardry:

• Level 1: Overused Native Charts
• Level 2: Underused Native Charts
• Level 3: Non-Native

Let’s go through some of the Excel secrets in more detail.

Level 1: Overused Native Charts

These are the familiar faces:

• Pies
• Donuts
• Bars and columns
• Clustered bars and clustered columns
• Stacked bars and stacked columns
• Line graphs

What are Native Charts?

“Native” charts mean they’re available from our menu with just a few clicks:

What’s Wrong with Overused Charts?

There’s nothing wrong with a bar chart here or there… but any chart gets boring when we show it over and over and over and over and over and over.

There’s also the issue of analytical depth — or lack of depth.. If we’re only using bar charts… then we’re only showing totals and averages. There are dozens more statistical approaches!

Beware! Formatting Needed

Stacked bar charts, for example.

They’re easy to make.

But we still have to:

• enlarge the font;
• darken the font (to pass official Accessibility rules for color contrast);
• directly label the data (so viewers aren’t relying on the colored legend alone — another Accessibility rule);
• outline the touching shapes in white (which helps with colorblindness and grayscale printing);
• show fewer increments in the scale (so it’s not so busy);
• decide whether to apply a dark-light contrast — or not (learn about data storytelling here); and
• adjust the gap width (if you want) to nudge the bars closer together.

Level 2: Underused Native Charts

This is where it starts getting fun!!

Excel can make:

• Combo charts (e.g., a column chart with a target line, as shown below)
• Overlapping Bars
• Area charts (where you shade the area underneath the line for better oomph and high color contrast)
• Slopes (a line chart with exactly 2 points in time, like pre and post)
• Small Multiples Lines (to combat the spaghetti line graph)
• Bumps (for rankings)
• Scatter plots (x and y)
• Bubble charts (x, y, and z)
• Tree maps (for nested categories)
• Heat Maps
• Sunbursts (nesting)
• Box and Whisker (to go beyond averages and show the min, quartile 1, median, quartile 3, and max)
• Waterfall (how pieces add to a net number)
• Radar (to compare several ordinal categories at once)
• Icons & Symbols (to make our graphs easier to navigate — and more memorable!)

Yes, These are Native Charts

Well… if you’re using the latest version of Excel.

If you’re on outdated software, (most of) these charts are still possible. They just get harder to make, i.e., they’re in Level 4 territory.

Yes, Underused Native Charts Add Variety (and Analytical Depth)

We’re not just adding variety for variety’s sake.

(Although common sense — and hundreds of consulting projects — has shown me that dataviz novelty is one of the best ways to increase engagement.)

Most importantly, we’re adding analytical depth. For example, a regular ol’ bar chart just compares the average or total of several categories. What if we compare them by location, too? Now we’ve got a heat map! We can spot geographical patterns, which would’ve been impossible in a bar chart.

Beware! Formatting Needed

Scatter plots are easy to make.

But we still have to:

• enlarge the font;
• darken the font (to pass official Accessibility rules for color contrast);
• add a key (that each dot represents one student);
• label the scales (with everyday language, like More skills gains, because scatter plots are notoriously difficult to read for people who don’t stare at graphs all the time); and
• decide whether to add a dark-light contrast.

Level 3: Non-Native Charts

Are you already using a variety of charts? Have you actually analyzed your data (beyond averages, and beyond totals)? Can you adjust the gap width, annotate the data, and apply colors strategically in your sleep?

Then you’re ready for Level 3!

With behind-the-scenes elbow grease, you can make:

• Stream Graphs
• Waffles
• B’Arcs
• Small Multiples Bars
• Population Pyramids
• Diverging Stacked Bars
• Lollipops
• Dots
• Swarm Plots
• Tile Grid Maps
• Sankey Diagrams

What are Non-Native Charts?

You won’t find any buttons that automatically make these charts.

Instead, we have to insert one chart type… and disguise it as something else.

For example, we have to insert a stacked bar chart… and disguise it as a waffle chart. You’ll need a Magic Table behind the scenes, too.

A stacked bar chart gets disguised as a population pyramid. Yes, you’ll need a Magic Table with placeholder values.

A scatter plot gets disguised as a dot plot, and so on. Each value gets assigned a x-y placeholder location inside the Magic Table.

Do these maneuvers turn your brain inside out and upside down? You’re not alone.

Learn More

If you’re consistently making, editing, and applying graphs from Level 3, you’re already a vizard. Get in touch so I can send work your way!

If you’re in Level 1 or 2, you’ll love Data Storytelling in Excel. You’ll go slow and steady so you don’t feel overwhelmed. You’ll dip your toe in… and then you’ll be swimming in the deep end in no time.

The post Which Graphs Can I Make in Excel? appeared first on Depict Data Studio.

This past year, I’ve spent more time teaching about data management than data visualization.

When I look under the hood of companies’ spreadsheets, I’ve noticed way too many data management issues that could be avoided altogether.

In this article, you’ll learn about a prerequisite for data visualization: contiguous datasets.

Benefits of Contiguous Datasets

In this video, you’ll see how a single contiguous dataset lets you use:

• ONE set of formulas for data cleaning, recoding, and analyses
• ONE set of pivot tables
• ONE set of charts

Then, at the end, you can slice and dice your charts with a variety of filters.

Mini Datasets Spread Across One Sheet – NO!

Here’s what I often see:

Separate datasets for each time period.

NOOOOOOOOOOOOOOOOO!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

Sometimes there are dozens of mini datasets, like this:

Mini Datasets Spread Over Multiple Sheets – NO!

Or, just as terrible for graphs and dashboards — one mini dataset per sheet, like this.

NOOOOOO!!!!!!!!!!!!!!!!!!!!!!

Or, separate mini datasets spread across different Excel files altogether.

NOOOOOOOOO!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

Ann, What’s So Bad about Mini Datasets?!

Separate mini datasets (“non-contiguous” or “non-touching” datasets) mean that we can only look at one time period at a time.

We have to make a bunch of mini charts.

It takes forever to make these the first time, and they’re a huge pain to update over time.

It’s also tougher for our viewers to find patterns because the numbers are scattered across too many charts.

NOOOOOO!!!!!!!!!!!!!!!

Dataviz Prerequisite: A Single Contiguous Dataset

Instead, the numbers should be stored in a single dataset, with the timeframe as its own column, like this:

This running list of new entries — a log — is going to get very long.

In real-life projects, the logs might have hundreds of thousands of entries.

That’s okay!!!!!!!!!!!!!!!!! That’s preferred!!!!!!!!!!!!!!!!!!!!

It’s counterintuitive, but contiguous logs make dataviz faster, not slower.

Excel can handle millions of entries.

The length of a dataset won’t make your analysis or visualization take any longer. Repeat after me: Contiguous logs make dataviz faster, not slower.

However…

The width — the number of columns — can certainly take a while, because there are so many different variables to consider.

Bonus: Save Your table as an Excel Table for Easier Updating

A table is the generic term for a collection of rows and columns.

An Excel Table is a special feature that makes it faster and easier to update our log.

In other words, Excel Tables make it easier to append our contiguous logs as we get new data.

How to Turn tables into Excel Tables

You’ll simply click on your contiguous log — your generic table.

Then, go to the Insert tab.

Choose a Table.

Click OK.

You’ll recognize the banded rows.

Adding New Entries to Datasets Stored as Excel Tables

Adding new entries — or appending — is easy.

Let’s pretend you’re downloading data from your organization’s database. You might only be able to download one month at a time into its own sheet. That’s okay!

We’ll simply copy and paste those new entries into our running log.

Then, we’ll add the timeframe to that right-most column, too.

Excel is smart, and it’ll know that your new entries are part of your new dataset. In other words, your new entries will feed into pivot tables and formulas seamlessly.

Contiguous Datasets are Required for Static Dashboards

Want a short handout, PDF, or email attachment to share with others?

Maybe you’d want to see how all the projects combined are doing.

Or, maybe you’d want a breakdown of the different projects.

You could even add quick vizzes like sparklines to see trends, like this:

Contiguous datasets are required in order to make static dashboards.

Otherwise the sumifs, countifs, and averageifs behind the scenes will be impossible. Or, the formulas will be painfully slow to set up.

Static dashboards should take less than an hour to design from start to finish.

If it’s taking longer than that, it’s probably because (a) you don’t have a contiguous dataset or (b) you need more practice with formulas.

Contiguous Datasets are Required for Interactive Dashboards

Want to make interactive dashboards in Excel?

Your technical coworkers will love exploring the insights for themselves.

Interactive dashboards involve four pieces:

1. A single contiguous dataset stored as a regular ol’ table or an Excel Table. You already know I prefer Excel Tables for datasets that are going to be added to or appended in the future.
2. Pivot tables to tabulate the numbers (and bypass formulas, which can be tricky for novices).
3. Pivot charts to, you know, visualize the numbers.
4. Slicers (a fancy name for the filters).

Once again, contiguous datasets are the foundation of data visualization.

Have I sold you on contiguous datasets yet???

Contiguous datasets are required for:

• Making a single graph to show comparisons over time (not January, February, and March in separate graphs that take three times as long to create and update);
• Making static dashboards with formulas and trendlines that’ll update (nearly) automatically as you add new entries to your log; and
• Making interactive dashboards with charts that’ll update (nearly) automatically as you add new entries to your log.

If your data visualization is taking too long… it’s usually a data management problem.

And it can be easily fixed!!!

Start storing all your non-contiguous datasets as a single contiguous dataset.

The post Two Types of Datasets: Contiguous vs. Non-Contiguous appeared first on Depict Data Studio.

Technical tables have so much value, especially as visual appendices for reports.

In this blog post, you’ll get ideas for bringing your technical tables to life.

Before

Here’s what the “before” version of some technical tables looked like.

These are made-up numbers, but you get the idea.

The public health staff wanted to look at quarterly numbers, the total annual number, and the rate (the number of cases per 100,000 live births).

Even if you’re not measuring neonatal abstinence syndrome, I bet there are numbers that you track each quarter. You might even want to look at the total annual number, too. Get some inspiration from this blog post, and then adapt the ideas to your own workplace.

Re-Created in Excel

First, I re-made their table in good ol’ Excel.

The finished product will be a PDF, but the most efficient way to bring technical tables to life is to keep the numbers inside Excel the entire time. We’re not going to transfer anything to Word.

Declutter

Let’s tackle the easy edits, such as:

• removing all the borders;
• adding back just the gray horizontal borders;
• removing the background fill; and
• left-aligning the text and right-aligning the numbers.

Add Trendlines

We’ll bring the quarterly trends to life with sparklines.

Add Bars

We’ll bring the annual totals to life with data bars:

We’ll bring the rates to life with data bars, too:

Brand Colors & Brand Fonts

Time to format!

We’ll apply brand colors and brand fonts:

We’ll color-code the text to match the bars.

(Sometimes the table’s columns get so narrow that it’s tricky to tell which number corresponds to which bar. That’s where color-coding comes to the rescue.)

As a general rule of thumb, colored font should be bold so that it passes 508/ADA color contrast guidelines.

Text Hierarchy & Intro Sentences

A text hierarchy means the title should be largest, boldest, and darkest so that it’s easiest to spot. (Followed by H1s and H2s if we had them.) We’re developing a hierarchy of information so our readers can stay organized.

As a general rule of thumb, I make sure headings are twice as big as body font. The body font is size 11, so this title is size 22 and bold.

We’ll also add intro sentences, and move that footnote info about the asterisks to the top. (People need to read that sentence before the table, not after.)

PDF- and Printer-Friendly

We’ve kept everything in Excel — that’s the only way to add the spark lines and data bars, and pasting tables into Word is a waste of time — but the final version will be shared with others as a PDF.

In the real version of this project, the PDF was about 15 pages long. There were various tabulations on various topics, not just neonatal abstinence syndrome.

We’ll need to:

• set the Print Area;
• adjust the Page Layout (portrait to landscape for easier on-screen reading);
• adjust the margins (0.5 to 1 inches is sufficient);
• add contact info and a logo so people can get in touch with questions; and
• adjust the column widths and row heights so everything fits juuust right.

Optional: Sort by Rates, Not Alphabetically

Finally, we might choose to sort the table by the most important column (rates, in this example) instead of alphabetically by county name.

I’m usually a fan of sorting. But I’m on the fence here. I also see the value in the leaving the counties alphabetized so readers can search for their own county. Hmm.

The Final Version

The visuals help us spot the patterns (thanks, Picture Superiority Effect).

The branding will help us look more professional to outside audiences (so we don’t look Frankensteined — when all our colleagues use different colors and fonts, and we put everything together in one doc, and it’s a hot mess).

The PDF’d appendices can be merged with the PDF’d report (thanks, Adobe Acrobat).

The Before-After Transformation

Once you’ve got intermediate/advanced Excel vizardry skills, the whole process will take less than an hour.

Really, this should take you less than 15 minutes!

If not, you’ll simply need to brush up on your Excel skills.

Dataviz is supposed to be fast and easy.

Bonus: Download the Materials

Want to explore my spreadsheet? Download my Excel file and adapt it for your own project.

The post How to Bring Your Technical Tables to Life appeared first on Depict Data Studio.

In this video, let’s make a starter dashboard in Microsoft Excel.

You’ll learn how to make four quick visuals:

1. Sparklines
2. Data bars
3. Symbol fonts
4. Color scales

I use these visuals over and over in my real-life consulting projects.

Watch the Tutorial

Sparklines

Sparklines are helpful for visualizing patterns over time, like daily, weekly, monthly, quarterly, or annual data.

To create sparklines:

1. Highlight the first row of your table.
2. Go to the Insert tab.
3. Go to the Sparklines section.
4. Click on the first one (a Line sparkline).
5. Choose where we want to put the sparklines (off to the right of the table).
6. Click insert and enjoy the sparklines!

We can also edit our sparklines!

We might adjust the data source, type (from line to column), or color. I typically gray everything out and highlight a high point or low point in a dark brand color.

We can also group and ungroup our sparklines (e.g., if we want each category in our dashboard to have its own color).

And if we change our mind, we can clear them out.

Data Bars

Data Bars give us horizontal bars (as opposed to sparklines’ vertical columns).

They’re helpful for visualizing summary statistics like totals or averages.

To create data bars:

1. Highlight the cells you want to visualize (e.g., the total column).
2. Go to the Home tab.
3. Click on the Conditional Formatting button.
4. Select solid-filled data bars.

Symbol Fonts

I use checkboxes to visualize whether I met a goal or target.

We can get quick checkboxes through symbol fonts!

In the video, you’ll see me write an =if() statement to transform g’s and c’s into Webdings checkboxes.

Audiences love the checkboxes. They’re intuitive, colorblind-friendly, and grayscale printing-friendly.

Color Scales

a.k.a. heat maps or heat tables.

I love color scales for visualizing the interior of my table—when I want to compare lots of rows and columns to each other.

To create color scales:

1. Highlight the cells you want to visualize (i.e., the interior of the table).
2. Go to the Home tab.
3. Click on the Conditional Formatting button.
4. Select Color Scales. Most of the time, we’ll use a Green-White Color Scale. That’ll make the big numbers dark (and the small numbers will be light).

Combos

In real life, we might combine several of these techniques.

We might add color scales to the interior of the table…

We might compare the totals with data bars…

We might add Webdings checkboxes to see whether we met a goal…

And we might add more data bars to see how far we were over or under our goal.

Formatting

In real life, we’d edit these quick visuals.

I suggest:

• Using brand colors and brand fonts.
• Outlining the color scales in white (so the cells can be differentiated against each other).
• Placing the data bars in a separate column than their numeric labels.
• Coloring the checkboxes (rather than boring black).
• Adjusting the colors in the over/under bars (to avoid scary red).
• Moving the labels to the over/under bars to their own column (via an =if() statement to save time).

Download this Spreadsheet

Try it yourself!

Download this spreadsheet.

Explore the completed version with the =if() statements.

Use the empty version to practice alongside me as you replay the video.

Get in Touch

If you get stuck, reach out o­n LinkedIn.

The post How to Make a Not-So-Scary Starter Dashboard in Excel appeared first on Depict Data Studio.

—–

Using a dashboard has been central to my work as a program evaluator.

My office funds several early childhood programs that all differ in their program content, performance indicators, and outcomes.

As the person who reviews each program’s quarterly report showing progress on each of their performance indicators, I am also often asked to report overall performance for our office—for example, total number of families served or number of home visits made.

This can be unwieldy when looking across many reports, and it’s useful to have a document that allows us to assess progress across all the programs at once.

When I enrolled in Ann’s Dashboard Design course, my goal was to build on an existing document, making it easier to read and identify successes and areas for improvement.

From a Basic Many-Paged Table in Word…

Initially, our office used a table in a Word document to track quarterly performance across programs.

It served the basic function of being able to see, in one file, how each program was doing each quarter. But it was lacking in a few areas.

One was that, although the annual targets for indicators were clearly marked in red and there were quarterly totals, there was no annual or year-to-date total to compare to the target.

Additionally, although it was very helpful to have all the performance data in one place, it wasn’t especially easy to see trends from quarter to quarter and the table split across two pages.

…To a One-Page Visual Overview of Key Performance Metrics

The first thing I did to make data tracking easier was move to Excel.

Even before taking Ann’s Dashboard Design course, I knew Excel was the smarter choice just for the ability to use formulas.

I also worked with my colleagues—the main audience of this internal performance-monitoring dashboard—to determine what features would be most useful. We came up with a few that make the dashboard much more user-friendly.

First, we chose a few key indicators to include on a cover page (pictured below). This allowed us to see the most critical data for each program all on one page, rather than having to scroll or flip through several pages.

In this Excel workbook the cover page is followed by separate worksheets, each showing one program’s data on their full list of performance indicators, which is helpful when we are taking a deeper dive into one program’s work.

Second, we all agreed the dashboard needed year-to-date totals to compare with the yearly targets.

This is especially helpful for some indicators, like number of individuals served, where many people continue to participate in a program from quarter to quarter.

Adding up the quarterly number served would count longer-term participants more than once; the unduplicated total is essential for understanding whether the program is meeting its contract target.

I took what I learned in Ann’s Dashboard Design course and added a third feature to visualize progress toward the yearly target: checkboxes and progress bars.

The checkboxes allowed us to see whether, at the end of each quarter, the program was on track to meet the yearly target. So, for example, a program would have to exceed 50% of the performance target at the end of Q2 (halfway through the year) in order to be “on track.”

The progress bar shows exactly what percent of the yearly goal has been achieved year-to-date. I used helper cells outside the print area to determine whether the checkboxes would be filled or empty.

Finally, we found it helpful to use sparklines (another tool learned in Ann’s class!) to succinctly show how performance changed from quarter to quarter.

In 2020, the second quarter was an especially unusual time as programs adjusted to the start of the pandemic. Seeing dips and spikes during that time helped us get a quick sense of what was working and what was not, and we were able to use that information to drill down with program staff.

The Outcome: More Effective Use of Data in Decision-Making

Even with just these few changes (and using a program nearly everyone can access!), our new performance monitoring dashboard has made it so much easier for our team to review quarterly progress in one place and visualize how our system of early childhood programs are working for children and families in the county.

The dashboard has become a quarterly staple at our staff meetings, where we review as a group and use the data to generate next steps.

It is also easy to share with senior leadership, so they can see at-a-glance the important work our programs are doing.

The post Designing a Prettier and More Effective Dashboard with Excel appeared first on Depict Data Studio.