What’s Inside

• 0:00 Intro
• 2:26 Behind the Scenes
• 3:12 Single Contiguous Dataset
• 3:50 Pivot Tables
• 4:15 Pivot Charts
• 4:24 Slicers
• 4:26 Helper Tables
• 5:35 Step-by-Step Demo

The post How to Make Interactive Dashboards in Excel: Colonoscopy Screening Example 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.

Catch the replay here!

Materials

Follow along with the spreadsheet: https://depictdatastudio.gumroad.com/…

You’ll have to watch the video to get the coupon code.

There are codes available for the first 500 people.

What’s Inside

0:00 Intro

1:14 The 4 Steps

4:28 (1) Excel Table

12:36 (2) Pivot Tables

18:14 (3) Pivot Charts

20:47 (4) Slicer(s)

24:33 A Gift for You

25:55 Q&A

The post How to Make Interactive Dashboards in Excel (30-Min Demo & Files to Download) appeared first on Depict Data Studio.

Interactive (a.k.a. dynamic) dashboards are a great option for technical audiences that have the time and interest to explore the data for themselves.

They’ll look something like this:

Interactive dashboards are easy to create — sort of. It depends on your existing skill level.

You’ll need four pieces:

1. A Clean, Contiguous Dataset (maybe stored as an Excel Table)
2. Pivot Tables
3. Pivot Charts
4. Slicers

Are you already using these four features regularly? Great! Linking them together in a dashboard will be easy for you.

Are you new to Excel Tables, pivot tables, pivot charts, or slicers? Be patient with yourself. You’ll need to be fluent in the building blocks before you can put them together seamlessly.

Let’s walk through each of the four pieces in more detail.

Step 1: Build the Clean, Contiguous Dataset

From previous blog posts, you know that table is a tricky term.

There are several different types of tables, like datasets vs. tabulations. In short, a dataset is the underlying numbers, and the tabulation is the summary table.

The Raw Dataset

To build our interactive dashboard, we’ll start with our raw dataset.

This is semi-fictional data. We’re pretending that we’ve downloaded YouTube stats directly from YouTube.

The raw dataset would look something like this, with one entry per date and traffic source.

It’s raw because this is exactly what it looks like when downloaded from YouTube. We haven’t made any changes (yet!).

The Clean Dataset

Next, we’d clean the dataset.

We might check for and deal with duplicates.

We might check for and deal with missing data.

We might add lots of new columns of recoded data. For example, if I want to make a donut chart comparing the internal and external traffic sources, then I’ll need a column (a variable) that categorizes each traffic source as being internal or external.

If I want to make a graph that compares YouTube visits by day of the week (Monday vs. Tuesday views), then I’ll need a column that turns MM/DD/YYYY into weekday. And so on.

(You can learn more about cleaning, recoding, and transforming datasets inside Simple Spreadsheets, my prerequisite course. Again, you’ll need to be 100% fluent in these skills. Otherwise, dashboards will feel daunting.)

Years ago, a coworker taught me to turn all my new variables red so my Future Self could find them. As you can see, I still follow that advice today.

The clean dataset would look something like this:

Required: A Contiguous Dataset

As usual, my clean dataset is contiguous.

In other words, all the cells are touching or sharing a border.

I don’t have dozens of mini datasets (like one per month, or one per traffic source).

You can learn more about contiguous datasets and why they’re necessary for dataviz in this blog post.

Optional: An Excel Table

Next, we might transform our clean dataset into an Excel Table. This step is optional.

As explained in this blog post, Excel Tables are helpful when we need to append tables (that is, when we’ll be adding more rows over time).

Alright, that’s it for the first piece! We’ve got a single, clean, contiguous dataset as our base. We might store it as a regular ol’ table/dataset. Or, we might turn it into an Excel Table for easy appending.

Step 2: Tabulate the Dataset with Pivot Tables

We can tabulate our dataset with either (1) formulas or (2) pivot tables. You can learn more about the pros and cons of each approach in this blog post.

In short, if we’re aiming to build an interactive dashboards… which has to involve slicers… which have to involve pivot charts… then we simply have to use pivot tables.

Again, interactive dashboards are easy — sort of. You have to understand all the nuances of when to use regular ol’ tables vs. Excel Tables, and when to formulas vs. pivot tables, in order to work both backwards and forwards and put everything together quickly and correctly.

Our pivot tables will look something like this:

Interactive dashboards involve pivot tables — plural.

We’ll need one pivot table for each of our charts.

In the finished example, there were four charts + a sum of the total views. That means there are five separate pivot tables behind the scenes.

If you’re familiar with pivot tables, great! Building a few pivot tables for your dashboard will be easy.

If you’re brand new to pivot tables, no worries! I’ve got plenty of beginner-level blog posts to get you started.

Step 3: Build (and Format) the Pivot Charts

Next, we’ll simply add a pivot chart to each of our pivot tables.

In case you’re brand new to pivot charts, here’s how you add them:

• Click on the pivot table to activate it.
• Go to the Insert tab.
• Choose which chart type you’d like (bar, line, donut, etc.).
• That’s it!

Please, don’t forget the formatting!!!

Our unformatted chart — which doesn’t pass 508/ADA compliance guidelines — would look like this:

The formatted chart would look like this.

Do you notice the binary color-coding, white outlines around touching shapes, and the direct labels?

Once we’ve built and formatted each of the charts, we’ll simply cut and paste them together into a new sheet. That’s where our soon-to-be-completed dashboard will live.

Step 4. Add a Slicer(s)

Finally, we’ll add a slicer(s) to the first pivot chart.

A slicer is just a fancy name for a filter. They’ve existed in Excel since 2010 (!!!). But, don’t worry if you haven’t seem them or used them before. It takes years for new features to be widely adopted. (Hence the point of blog posts like these — to introduce you to features you might not have discovered before.)

Connect the Slicer to the First Chart

In case you’re brand new to slicers, here’s how you add them:

• Click on one of the pivot charts to activate it.
• Go to the Insert tab.
• Click on the Slicer option.
• You’ll see a list of all the variables. In this example, our variables from the clean dataset are Date, Weekday – Number, Weekday – Name, Month – Number, Month – Name, Traffic Source, Traffic Source – Internal or External and Views. If I want viewers to be able to slice and dice by month, then I’d select Month – Name to feed into the slicer.
• That’s it!

Connect the Slicer to the Rest of the Charts

The slicer won’t automatically be connected to all of our charts.

We’ll need one more step:

• Click on the slicer to activate it.
• Go to the Slicer tab.
• Click on the Report Connections button.
• We’ll see a list of all our pivot tables. Check all the boxes.
• That’s it! Now, when we filter data with the slicer, all the charts will correctly filter and change, too.

Final Formatting

As usual, we’ll make sure to follow dataviz best practices.

We’ll need to:

• Add words. We’ll need a title, date, subtitles, and explanatory text. Yes, there’s concatenation behind the scenes that automatically writes the sentences for me.
• Use brand colors and brand fonts.
• Color-code by category. (One brand color per category/section/chart.)
• Leave plenty of white space between the charts. My rule of thumb: A thumb’s width (a half-inch or inch of white space between each chart).

Now it’s time to sit back, relax, and let our colleagues have fun exploring the dashboard for themselves.

Learn More

If this tutorial is easy for you, then congrats!!! You’re all set. Go forth and build magnificent, accessible, interactive dashboards for your technical-minded colleagues.

If this tutorial was jargony for you, don’t worry!!! You can walk through each of the steps in more detail, and download the spreadsheets to follow, and come to live Office Hours inside the Dashboard Design course.

The post How to Make Interactive Dashboards in Excel appeared first on Depict Data Studio.