Then, you learned about two types of datasets: contiguous vs. non-contiguous.

Now, let’s learn about two types of tabulations: formulas vs. pivot tables.

Tabulation Option 1: Formulas

Formulas and pivot tables are both correct… in different circumstances.

Here are the pros and cons of each approach so you can figure out which one you’ll need.

Formulas:

• are necessary for tabulating numbers;
• are faster for datasets with matching columns;
• play well with quick vizzes;
• give us full control over tabulations; and
• give us full control over charts; but
• involve a learning curve.

Formulas: Necessary for Tabulating Numbers

In Simple Spreadsheets, we talk about the calculations needed for different types of variables: nominal, ordinal, interval, and ratio.

When it comes to formulas, we can put these variables into two buckets: numbers and categories.

Numbers are test scores, ages, number of people, amount of money donated, etc.

For numbers, we need to tabulate them using descriptive statistics, which often aren’t possible with pivot tables.

Descriptive statistics for numbers might include:

• Measures of central tendency (=average, =median, =mode)
• Measures of dispersion (=stdev, =var, =min, =max, and range)
• Characterizing the distribution (=skew, =kurt)
• Quartiles (=quartile)
• Percentiles (=percentile)
• Outliers (There are multiple ways to define and deal with outliers; in many projects, we use +/- 3 standard deviations different from the mean)

Formulas: Faster for Datasets with “Matching” Columns

Years ago, I demonstrated how to tabulate satisfaction survey data with “matching” columns.

In the fictional-but-inspired-by-real-projects dataset, each survey question was in its own column.

Every survey question had the same options: strongly agree, agree, disagree, and strongly disagree.

In other words, this dataset had matching columns.

In this 5-minute video, you’ll see how we can write one formula, and then drag it down and across to quickly tabulate matching columns.

Formulas: Play Well with Quick Vizzes

Formulas feed seamlessly into at-a-glance visualizations, like spark lines, data bars, heat tables, and symbol fonts.

(Pivot tables don’t.)

Formulas: Give Us Full Control over Tabulations

Need to compare your numbers to a target?

Need to see how much the numbers have changed over time (e.g., percent change or percentage changes from month to month)?

These tabulations can be tedious or impossible with pivot tables.

Formulas: Give Us Full Control over Charts

We can make a billion different charts in Excel. Here’s an incomplete listing of the Excel vizardry that’s possible with good ol’ Excel.

Want to make a native chart? One of the common built-in charts, like bars, columns, pies, and lines? Pivot tables will feed into native charts just fine.

Want to make a non-native chart? Population pyramids, dots, lollipops, swarms, b’arcs, tile grid maps, diverging stacked bars, etc.? Advanced vizardry is only possible with magic tables, which have formulas underneath, not pivot tables.

For example, if you want to make a swarm plot (a.k.a. jittered dot plot), like this:

Swam plots are non-native charts, so we’ll need formulas behind the scenes to have full control over the chart’s creation and formatting, like this:

Formulas: Expect a Learning Curve

Sure, most people know the absolute basics, like sum and average.

But there are 450+ formulas and functions inside Excel.

Knowing which ones you need… at which point in the analytical process to use them… and how to use them… That takes training and practice.

Tabulation Option 2: Pivot Tables

Pivot tables are a drag-and-drop solution for tabulating our datasets.

In other words, we don’t have to write any formulas! No need to stress over jargon like “” or () or , or A1:A100.

Pivot tables are:

• great for novices;
• great for tabulating categories;
• faster for cross-tabulations;
• slightly faster for appended tables and recurring analyses;
• way faster for mismatched columns; and
• necessary for interactive dashboards.

Pivot Tables: Great for Novices

Let’s start with the biggest benefit of choosing pivot tables over formulas: there’s a minimal learning curve, so pivot tables are perfect for novices.

Here’s an older blog post that shows you how to get started with pivot tables within minutes. You’ll insert a brand new pivot table, and then drag and drop variables into the little boxes.

Sure, there are nuances:

• switching the units from sums and counts;
• double-clicking to explore mysterious entries and outliers;
• placing two variables in the values box (e.g., counts and their percentages); and
• refreshing the pivot table as new entries are added to the dataset.

But, anyone and everyone can learn the basics within minutes — supervisors who don’t have time to delve into the details of formulas, graphic designers who don’t need to conquer statistics, grantmakers who need to focus on the actual philanthropy and not statistical formulas, etc.

Pivot Tables: Great for Tabulating Categories

Formulas are great for numbers, because we’ll need to calculate descriptive statistics like mean, median, mode, standard deviation, variation, quartiles, percentiles, skewness, and kurtosis, among many others.

Pivot tables are great for categories, because we’ll need to calculate frequencies (like how many people).

Yes, we can also calculate frequencies with formulas (countifs, for example).

Pivot Tables: Faster for Cross-Tabulations

A regular ol’ tabulation might be the number of males and female employees.

A cross-tabulation adds another variable or two, like the number of male and female employees in each state.

Yes, we can do cross-tabulations with formulas, too (another perfect opportunity for countifs). But especially for novices, the drag-and-drop functionality is going to be faster than adding to an existing formula.

Pivot Tables: Slightly Faster for Appended Datasets with Recurring Analyses

Need to add to your dataset over time?

Maybe you collect daily outbreak data, like many public health agencies I work with.

Or, maybe you collect quarterly data from grantees, like many foundations I work with.

(Or some other time period — like weekly, or annually, or whatever.)

As you add to your dataset — your contiguous log — you can simply refresh your pivot table and it’ll incorporate the latest numbers. That means that the chart(s) linked to your pivot table will update with the latest numbers, too! Woohoo!

Yes, it’s easy to update formulas as we append datasets, too.

You simply create one anchor formula — the formula in the upper-left of your tabulation — and drag it across and/or downwards to fill all the cells, like this:

Pivot Tables: Way Faster for Mismatched Columns

Earlier, I said I prefer formulas for matching columns (e.g., all the columns contain agree-disagree response options).

I prefer pivot tables for mismatched columns (e.g., one column has agree-disagree options, another column has birthdates, another column has addresses, and so on).

It would be a huge pain to add so many different formulas along the bottom of my dataset! I might need countifs for one column, and sumifs for another column, and averageifs for another column… meh.

Pivot Tables: Necessary for Interactive Dashboards

To build interactive dashboards in Excel, you’ll need to create pivot tables, then pivot charts, then slicers.

To the best of my knowledge, interactive dashboards have to be built off pivot tables, not formulas.

Here’s an example of an interactive dashboard that’s linked to pivot tables:

The Bottom Line

There are two ways to tabulate your dataset: through formulas, or through pivot tables.

Formulas:

• are necessary for tabulating numbers;
• are faster for datasets with matching columns;
• play well with quick vizzes;
• give us full control over tabulations; and
• give us full control over charts; but
• involve a learning curve.

Pivot tables are:

• great for novices;
• great for tabulating categories;
• faster for cross-tabulations;
• slightly faster for appended tables and recurring analyses;
• way faster for mismatched columns; and
• necessary for interactive dashboards.

Neither option is terrible. Neither option is perfect.

As usual, there are pros and cons.

Your Turn

When do you tabulate your datasets with formulas vs. pivot tables?

This isn’t an exhaustive list of pros and cons. What am I missing??

I had the most unsubscribes to my blog and newsletter of all time — in more than a decade of blogging, YouTubing, and newsletter-ing.

One person said something like this:

“I think the issue is you’re a visualization expert and visually the mini sets are easier. From a data prep perspective, one really long table is the correct way to store the underlying data. Dealing with dozens of tables that should just be a single set is a typical rookie mistake.”

Let’s chat more about that distinction: storing underlying data vs. tables that look nice visually.

Two Types of Tables

The term “table” is tricky.

At its core, a table is just a collection of rows and columns.

But you’ll need different types of tables at different phases in the data analysis and visualization process.

Here’s the major distinction you need to understand:

1. Datasets are tables where your data is stored.
2. Tabulations are tables where those datasets are summarized.

Let’s look at each type in more detail.

Type 1: Datasets

The first type of table is a dataset, which is where your data is stored.

Sort-of synonyms:

• Raw data: This is a sort-of synonym. The term raw means the data hasn’t changed since you received it (i.e., a coworker emailed it to you); since you downloaded or exported it (i.e., from a public-facing website, or from your agency’s database); or since you or someone else manually-entered it.
• Clean data: This is a sort-of synonym. The term clean means the data has changed since you received it. You checked for duplicates and missing data; you checked for and dealt with outliers; and/or you cleaned and recoded variables (e.g., by transforming a MM-DD-YYYY into Q1, Q2, Q3, or Q4, among hundreds of other recodings that are often necessary).
• Master dataset: This is a direct synonym — and this is the term I learned in undergraduate and graduate statistics courses — but we don’t use slavery terms anymore. I’ve been hunting for a better term for a couple years. If you’re in Simple Spreadsheets then you’ve heard me talk about this a lot. I’ve experimented with the terms central headquarters or hub to replace master dataset, but none of them felt right. The term that currently feels most accurate is contiguous dataset.

Datasets: Contiguous vs. Non-Contiguous

Datasets should be contiguous, i.e., touching or sharing a border.

If you want to be efficient, that is.

Non-contiguous datasets — dozens of mini datasets located across different sheets or Excel files — lead to wasted time, wasted money, and wasted brainpower.

Datasets: Stored as Excel Tables for Easy Appending

Datasets should be stored as Excel Tables when you need to append them later, i.e., if you’ll be adding to them.

You can learn more about contiguous vs. non-contiguous datasets and tables vs. Excel Tables in this blog post. The Simple Spreadsheets course is all about data management and analysis, too.

Type 2: Tabulations

The second type of table is a tabulation. Tabulations are tables where the datasets are summarized.

For example, the dataset might have one entry per project. The tabulation might show the totals and/or averages across all the projects.

Datasets and tabulations have different purposes. They’re used at different points in the analytical process. They look different. They are different.

Synonyms:

• Summary table
• Summary statistics
• Report
• Key metrics

How to Tabulate the Dataset

You’ve got two options in Excel:

1. Formulas (sumifs, countifs, averageifs, lookups, etc.) will play nicely with the quick vizzes (below). They require more skill and practice, though.
2. Pivot tables will play nicely with the interactive dashboard (below). Anyone can learn pivot tables within minutes, so I often recommend them for the beginner/intermediate crowd.

This distinction deserves its own blog post, too. In all my “spare” time, ha! We also talk about the distinctions between formulas and pivot tables in detail inside Simple Spreadsheets.

Tabulations: Can Be the End Product (meh)

The tabulation might be the end product that you share with others.

I suppose you could email the summary table to colleagues. You could post it on a website, or share it on a slide.

Except… meh.

Why not bring those visuals to life?!

Tabulations: Can Feed into Mini-Graphs

Why not add quick vizzes to bring tabulations to life?!

Sparklines, data bars, heat tables, and symbol fonts are my go-to’s.

Visuals make it easier for our brains to spot patterns. It’s obviously faster to look at a viz than to read all the numbers.

Your quick vizzes might look like this:

If you format the sheet for easy printing and PDF’ing, then voila!, you’ve got a static dashboard.

Static dashboards like these are great for internal audiences that (1) need a quick turnaround time and (2) want lots of details from the actual tabulations.

Tabulations: Can Feed into Big Graphs and Dashboards

Tabulations can also feed into larger graphs (for documents and slides).

Or, tabulations can feed into larger graphs for interactive dashboards.

Your interactive dashboard in Excel might look something like this:

The Bottom Line

“Table” is a tricky term. It’s broad and generic. It means different things to different people.

There are two main types of tables:

1. Datasets are the underlying data source. You might have one entry (one row) per person, or per organization, or per project. Datasets should be contiguous because.
2. Tabulations are the summary tables. You might tally-up how many people, or how many organizations, or how many projects. Tabulations might be your end product (yawn!). Or, they might feed into graphs and dashboards (yay!).

We need both datasets and tabulations. But these are different types of tables.

But it doesn’t have to be!!!

Let’s stop hiding important qualitative concepts inside Text Walls that no one reads.

Step 0: Take Pride in Your Report’s Formatting

We’ll use landscape so that it’s easier to see on a landscape computer screen.

We’ll add the organization’s Theme Colors and Theme Fonts so that the document looks professional and enhances branding.

We’ll add a Text Hierarchy. Instead of a single Heading 1, we need to add several Heading 2s. We also need to enlarge all the font sizes. No more puny size 12 or 14 for headings!!

In this example:

• the Heading 1 is size 24 bold in a brand color,
• the Heading 2s are size 16 bold in a brand color, and
• the body font is size 11 in dark gray. (Not black, which causes eye strain and makes people think of funerals, at least according to my graphic design friends.)

Step 1: Choose Your Diagram

I like to scroll through SmartArt for ideas.

You can also browse Diagrammer, which is SmartArt on steroids.

Here are the most common diagrams I’ve used to visualize qualitative concepts in research and evaluation projects:

Processes

Processes are for linear, step-by-step concepts. There’s a defined start and end.

Examples:

• A lot of my own training programs, where I teach how-to instructions for dataviz.
• Logic models.
• Research methods (e.g., we recruited participants, and then they did this, and then they did this).

Cycles

Cycles are for processes that loop around and around until infinity.

Example:

• The program evaluation lifecycle, in which you plan for the evaluation, collect the data, analyze the results, use the data to inform decisions… and then start the process all over again.

Concentric Circles

Concentric circles are for spreading concepts and for inner, middle, and outer layers.

Example:

• An agency made a plan to improve diversity, equity, and inclusion. They identified three layers of changes needed: at the individual level, at the departmental level, and at the agency level.

Components

Components are for pieces of the whole—when you want to show that all these random things aren’t so random; they’re connected. They’re just not connected as a linear process or as a cycle.

Examples:

• In my master’s thesis, I researched how nonprofit organizations were using data to have a bigger impact on the community. In the literature review, I identified ~10 specific examples of data use, which were all related to the broader theme of data use.
• The U.S. Surgeon General’s Framework for Workplace Mental Health & Wellness (scroll down a bit here).

Pyramids

Pyramids, or ladders, are for concepts that build upon one another. The base layer is the foundation, the middle layer builds upon it, and you’re aiming for the pinnacle at the very top.

Example:

• In my Report Redesign classes, I organized the techniques into a pyramid. Participants learn the foundational skills, then the slightly narrower skills, then the nitty-gritty details that pull everything together.

Matrices

Matrices are fancy tables or plots.

Examples:

• This blog post about four types of dashboards.
• Lots of diagrams from McKinsey.

Venn Diagrams

Venn Diagrams are for interwoven, overlapping components.

Example:

• A project involving several groups of people, who all come together to advocate for their issue.

Honeycomb

Honeycombs, meh. I don’t love these. They’re overused, along with the gears. If you’re not sure what else to use, this is still better than a Text Wall.

Step 2: Introduce Your Diagram

Show the fully-colored diagram.

Don’t cram too much text on the diagram itself. In this example, I’m pretending we’re describing three steps, which repeat over and over.

Add a paragraph or two to explain the diagram at a high level.

Make sure there’s plenty of color contrast by using bold white or bold black text against your brand colors. Use this color contrast checker to figure out which font color to use.

Step 3: Repeat Your Diagram

Here’s the important part: Repeat your diagram as you explain each segment in more detail.

Copy and paste the diagram.

Then, gray everything out, and just highlight the segment you’re explaining in a dark brand color.

For bonus points, you can color-code the Heading 2s to match the diagram.

Make sure your colors are consistent with what you introduced earlier!!! You wouldn’t want Step 1 to be purple, and then blue, and then green.

I usually delete the words from the diagram that I’m not currently explaining. For example, when explaining Step 1, I delete the words Step 2 and Step 3 from the diagram. I don’t want any issues with color contrast; the white font wouldn’t be legible against the light gray diagram, so I simply delete it.

Make sure there’s plenty of white space between sections. I use at least 0.5 inches of white space (e.g., between the diagram and its paragraph, between the paragraphs).

For bonus points, break up the paragraphs into points and bold a few key words. Long paragraphs are dated. Readers expect short, skimmable paragraphs these days.

Check out the paragraphs in this blog post, for example. They’re 1-4 sentences long. There are lots of headings. There’s bolding to increase skimmability.

This blog post is also written at a 7th grade reading level.

Peek at the document with the gridlines on. Make sure the diagrams are aligned with each other.

The Final Product: Repeating Diagrams

I don’t care that it takes up two pages instead of one.

Two great pages will beat one lousy page any day of the week.

Yes, your boss might give you a made-up page limit. “Make sure everything fits on a page!” Those limits were created because bosses got tired of Text Walls. And, because we used to print a lot.

Nowadays, people don’t print as much. I think the pandemic was a major turning point. With everyone working remotely, nobody had access to the office printer anymore. Any who wants to pay to print at home??

I’ve never, ever heard complaints about two accessible pages vs. one inaccessible page. The word count is the same. (Well, I added some headings.) But the information is richer because we’ve added a diagram and then explained it piecemeal.

Adapt as Needed

Use can use any diagram you need—a cycle, linear process, pyramid, or concentric circles.

You can do this in Word.

You can do this in PowerPoint.

You can do this in Canva.

You can do this in Publisher.

I’m software-agnostic. I don’t care which software program you use. When formatted well, you’ll get the same high-quality end result regardless of which program you’re using.

In this example:

• The diagram was wide, so when I introduced it, it needed the full width of the page.
• When I repeated the diagram, none of the words (“Phase 1”) fit, so I deleted them.

Adapt as needed!!!

Download My Word Document

Bonus!

Want to see how I arranged everything inside of Word?

You can download the document here: https://depictdatastudio.gumroad.com/l/UseRepeatingDiagrams

I want to talk about something that’s a little controversial in the dashboard space: There are 4 types of dashboards, all of which are correct.

You might need one type.

Or, you might need all four.

Every audience and every project is a little bit different.

Our goal is to deliver the right data, to the right audience, at the right time.

Watch the Video

Here’s a 12-minute video to help you narrow down which type(s) of dashboard you need for your next project.

First, you’ll think about your audience. Are they technical or non-technical? Leaders or doers?

Then, you’ll choose a software program. Sometimes you’ll need spreadsheets (like Excel), and other times you’ll need dashboard programs (like Tableau or PowerBI).

Here’s a summary of what’s inside the video.

Dashboard Mismatches to Avoid

What if the audience has one idea in their mind… and we design something completely different??

Sometimes we get lucky, and we make exactly what they want.

Other times, I see mismatches.

Common Dashboard Mismatch: Static vs. Interactive

Sometimes the audience really wants an interactive dashboard, but we make a static dashboard.

In real projects, that almost never happens. I typically see the opposite issue: We design an interactive dashboard, but they really needed a static dashboard.

Common Dashboard Mismatch: Single or Series

Here’s another dashboard mismatch I see a lot:

Sometimes the audience really wants a single dashboard, but we design a series of matching dashboards.

Or, I see the opposite:

The audience really wants a series of matching dashboards (e.g., one for Project A, one for Project B, and one for Project C), but we give them a single overview showing all three projects combined.

The Four Types of Dashboards

Let’s be proactive and avoid these dashboard mismatches altogether.

Here are some factors to consider at the very beginning of your next dashboard project.

Technical or Non-Technical?

Is your audience technical or non-technical?

Technical audiences love data, details, and decimal places.

Non-technical audiences would rather be doing something else, like leading the project, developing new policies, or managing the team.

Time is another factor: Are they busy? Or, do they have time to explore a dashboard on their own?

Non-technical or busy audiences tend to prefer static dashboards. These short PDFs can be shared as email attachments or as printed meeting handouts.

Technical audiences (or those with plenty of time available) tend to prefer interactive dashboards. They love exploring these clickable, dynamic dashboards and coming up with their own insights.

Leaders or Doers?

Next, figure out whether your audience is mostly leaders or doers.

The leaders need an aggregated overview of the work, e.g., one dashboard for the state as a whole.

The doers need individualized, disaggregated data, e.g., one dashboard for their charter school x dozens of charter schools in the project.

If we give the leaders the disaggregated dashboards, we risk that they’ll get lost in the weeds.

And if we give the doers the aggregated dashboards, we risk that it’s not actionable enough for them to do anything about the data.

Audience First, Software Second

AFTER we narrow down our audience, then we can choose a software program.

Single static dashboards can be made in spreadsheet programs, like Excel, Sheets, or Numbers. We can sorta make them in infographics programs like Canva or Piktochart; those templates are meh but they’re getting better all the time.

Need a series of matching dashboards? Spreadsheet programs can handle those, too. I make one template in Excel and then automatically populate it with all the dozens of dashboards’ data. You can write VBA code, connect everything with drop-downs and lookup formulas, or use Slicers.

Have a technical audience? Interactive dashboards are possible in Excel (via Excel Tables, pivot tables, pivot charts, and slicers). Or, you can make them in dashboard programs like Tableau or PowerBI. Or, you can learn coding (e.g., R).

In the video, you’ll see real-life examples of these dashboards, too.

Download the Diagram

Want to download this diagram? Share it with your team, and discuss it together at the beginning of your next dashboard project. Let’s avoid those mismatches altogether.

Your Turn

Do you currently have any dashboards?

Who are they for? Non-technical or technical audiences? Busy people, or those with time available? Leaders or doers?