Visualizing data in charts, graphs, dashboards, and infographics is one of the most powerful strategies for getting your numbers out of your spreadsheets and into real-world conversations.

But it can be overwhelming to get started with data visualization. Does data visualization leave you feeling like the numbers are about to topple over on you??

If so, this step-by-step data visualization guide is for you!

I’ll walk you through the data visualization design process so you know what to do first, second, and third as you transform your spreadsheets into data stories.

Step 1: Understand Your Audience

Wait! Don’t start making graphs on your computer! First, we have to do some planning. A little bit of up-front planning will save you hours of blood, sweat, and tears in the long run.

First, we need to consider our audience and context. Who, exactly, is going to be using the data to make decisions?

Here are some discussion-starter questions to talk about with your colleagues.

Who is Your Audience?

A chart designed for a group of foundation program officers will not be appropriate for a group of high school principals, and vice versa.

List all your audience types on a piece of paper, or a whiteboard, or in a spreadsheet, or even on the back of a napkin. Share the list with your colleagues and make sure you’re on the same page.

Have you reached consensus about who you’re targeting with your data?

What’s Your Audience’s Numeracy Level?

Do they enjoy or fear data? Unless you’re designing charts for a group of economists or statisticians, you can usually leave out details like the effect size, power analysis, and margin of error. Laypeople are often more interested in practical significance (the “so what?” and implications of findings) than in statistical significance.

What’s Your Audience’s Data Visualization Familiarity Level?

If they’re brand new to dataviz, stick with the traditional charts like pie charts, bar charts, and line charts—otherwise they’ll spend more timing ooh-ing and aah-ing over the chart’s novelty than paying attention to the information contained in the chart.

How Much Time Does Your Audience Have?

Little time or interest: Simple static chart.

Lots of time and interest: Interactive charts.

What Types of Decisions Does Your Audience Make?

What information do they need? What information do they already have? What information are they expecting? How will your chart(s) add value for them?

If you can’t think of how your chart will add value for the readers, don’t make one. Every chart needs a purpose and so what?

How Much Precision is Necessary?

As the data visualization designer, you have the freedom (and responsibility) to select how much precision is necessary. Your selection should be well thought-out and intentional. Your decision plays out in two ways: the chart type you select, and how you label the data points.

When selecting chart types, remember that some charts are better than others in displaying precision. For example, charts that rely on angles and area to show differences, like pie charts, are for communicating general patterns. Charts that rely on length to show differences, like bar charts, are for communicating specific details.

How Many Decimal Places Are Necessary?

A related decision is how exact your data labels will be. Will you include decimal places? How many?

In most scenarios, you can safely round your decimal places to the nearest whole number. Your audience is rarely using the tenths, hundredths, or thousandths place to make decisions.

Are My Viewers Expecting a Story?

Think about whether your audience is expecting a traditional or storytelling graph.

You’ll learn about the distinctions in this video:

Step 2: Choose the Right Chart

It takes a while to understand all the different chart types and to pick the best one for your desired takeaway message. There are tons of great graphs to choose from!

Consult a Chart Chooser

My interactive Chart Chooser includes dozens of chart types, resources, tutorials, and templates.

New to Dataviz? Start with Classic Chart Types

If you’re not sure which chart to use, stick with classics like the bar chart to compare categories and the line chart to visualize how things change over time.

These charts will be “right” most of the time, so they’re a safe bet.

Use Pie Charts Sparingly

Contrary to popular belief, pie charts are not evil and don’t have to be avoided altogether. I have seven guidelines for using pie charts and donuts. In this pie chart makeover, I show you how to transform a 3D pie chart with way too many slices into a storytelling bar chart with icons:

Getting Comfortable with Dataviz? Branch Out and Try Other Chart Types

Once you’ve mastered the classic chart types, you can play around with less-familiar chart types like bubble charts, bullet charts, dot plots, heat maps, scatter plots, slope graphs, social network maps, tree maps, waterfall charts, and more.

Surround Yourself with Positive Inspiration

Surround yourself with great graphs so you can expand your worldview of what’s possible with data visualization. I suggest following top-notch data journalism teams like @PostGraphics, @NYTgraphics, and @WSJgraphics.

You can even create a physical or digital library of great graphs. For example, you might print full-page, full-color charts and tape them near your desk. Surrounding myself with a variety of chart types, all of which have been used in different reports and for different groups of people, helps me create brand new charts easily. All I do is glance up at my gallery, and then I quickly figure out which chart is best for my new situation.

Dive Into Your Dataset with Exploratory Data Visualization Techniques

I also use exploratory computer strategies, like Microsoft Excel’s spark lines, data bars, and conditional formatting, to help me narrow down the focus of my charts.

Spark Lines

Here’s a tutorial that shows you how to get started with spark lines:

Data Bars

And here’s a tutorial that shows you how to get started with data bars:

Conditional Formatting

You can set up rules in your spreadsheet that automatically change the color of certain cells based on their values. I regularly use heat tables to scan my dataset for patterns. You can follow my step-by-step tutorial to make heat tables for your data.

Sketch Rough Drafts on Paper

Step back from your software program. This is especially crucial if you’re using Excel or R (versus Tableau) where you usually need a solid idea of your chart’s design before implementing that design on the computer.

I sketch, draw, and doodle plenty of drafts before I create anything on the computer.

Here’s how it works: First, sketch plenty of rough drafts on paper. Give yourself permission to doodle as many drafts as you need. Share drafts with colleagues early and often. Gather as much feedback as you can. Next, create one or two of those promising drafts on the computer. Finally, edit, edit, edit! Put your easiest-to-follow chart in your final presentation or report. You might sketch five or more drafts. Only the single best chart will survive the editing process.

Step 3: Select a Software Program

Once you’ve got a rough mental idea of what your visualization might look like, sit down and build the first draft of your visualization on the computer.

There are dozens of software programs available for building data visualizations. Some are free. Others are low-cost. And others are quite costly, at least for smaller organizations.

I’m software-agnostic at my core, meaning that I don’t care which program you use. You can create great — or terrible — graphs in any software program.

That being said, 99% of my data visualization consulting is done in Microsoft products: Excel, Word, and PowerPoint. Those are the common denominator for the companies that hire me. I’d never create a dashboard in a specialty software program… if you don’t also have access to it and know how to use it. It would be useless!

Here’s an example of an interactive dashboard made in good ol’ Excel. You can learn how to make these, and many other types, inside my Dashboard Design online course.

Step 4: Declutter

After you’ve got the first draft of your data visualization created on the computer, it’s time to refine your visualization and make your message shine. No computer program is perfect. You’ll have to roll up your sleeves and make intentional edits no matter which software program you’re using. The very first edit I make is to declutter my visualization. Software programs come with way too many borders, lines, and unnecessary ink. Examine each and every speck of ink on the chart. Does it have a specific purpose? If you can’t articulate a reason for that ink, you don’t need it.

Apply the Squint Test

In these before scatter plot on the left, the cluttered appearance distracts us from the data. All these extra lines make the charts look overly scientific—and outdated. In the after version on the right, I removed the background shading and borders. I kept the x and y axes and some of the grid lines, but I intentionally changed the black ink to gray ink.

How do you know when you’re done decluttering? Apply the Squint Test. Here’s how it works: Squint your eyes so that you’re peering at the chart through your eyelashes. Everything should look a little blurry. Can you see the overall shape of the data? For example, you should be able to tell if a line chart is jutting upwards or downwards over time. If not, try removing more clutter.

Outline Shapes in White

You’ve got the gist of decluttering. Now, let’s fine-tune!

Sometimes reducing clutter means outlining shapes in white, rather than black, so that they match the chart’s background color.

Step 5: Clarify Your Message with Color

There are three goals for color:

1. Branding (Using your company’s colors, which saves time and helps you look professional)
2. Accessibility (Making sure your colors pass official guidelines so they’re legible for people with disabilities, like ADA/508 compliance in the United States)
3. accessibility (Using colors to make the graph feel intuitive)

Brand Your Visuals with Custom Colors

I’m begging you! Do not use the default colors from Excel, Tableau, or Google Charts. Nothing screams novice! or 2002! more than default color schemes. If you’re designing charts for a report, handout, or presentation for a client, use their color scheme. Consultants, this means the report will look like it came from the client. It will not have your firm’s look and feel.

In this example, Johanna Morariu and I were designing a slidedoc for the Working Families Success Network. We began by investigating the Working Families Success Network’s logo, website, and publications. Their logo has a distinctive blue, orange, and pink and their publications use dark gray text rather than black. Throughout their website they use color blocks with white text and white outlines. Next, we adapted that layout and color scheme for our slidedoc. The images on the right are separate slides (pages) of the report.

You can locate custom color codes in style guides, with a free eyedropper tool, or even with Microsoft Paint. Then, enter your custom color codes in Microsoft Excel or in Tableau.

Make Sure Your Colors Are Legible for People with Color Vision Deficiencies

Here’s how:

1. First, by proactive and avoid using red-green color combos.
2. Second, make sure you directly label your data.

Although we’re used to seeing legends, we rarely need them. Legends can lead to unnecessary zig-zagging around the screen or page, and legends can also be difficult to interpret if your graph is printed in grayscale.

Instead of using legends, directly label the data. Direct labels mean that you add labels as close as possible to the data. For example, in a line graph, you would delete the separate legend and place the category labels off to the right of each line. For bonus points, color-code the text in the labels to match the line.

This is what direct labels look like:

Then, you can upload your draft to www.color-blindness.com’s Color Vision Deficiency Simulator to get a preview of what it’ll look like for people with protanopia and deuteranopia.

Emphasize the Takeaway Message with the Action Color

When you want to tell a story with data, you can guide your viewer’s attention to your desired takeaway finding by creating a dark/light contrast. This example comes from one of my graduate school projects a decade ago, so I used the exact shade of green from my university’s logo. Then, I used dark green to draw my audience’s attention to a couple key parts of the slide. This slide comes from the fourth section or chapter of the presentation, the Limitations section, so that tab was highlighted in dark green so that it contrasted with the other tabs, which are in gray. The topic of this particular slide was Brevity of open-ended survey responses, so that text is in green so that it stands out against the rest of the text. And the box-and-whisker plot itself also uses dark green.

Step 6: Clarify Your Message with Text

It’s hard to get wording just right, so I usually save my titles, subtitles, and annotations for the end.

Brand Visuals with Custom Fonts

Rather than using Microsoft’s plain ol’ Calibri, make sure your visualization’s fonts match the project’s branding.

Write the Takeaway Finding in the Graph’s Title

Need to tell a story with data? Rather than using a generic title (“Figure 1” or “Number of youth served”), state the takeaway message in the title.

I first learned about this technique through Cole Nussbaumer’s Storytelling with Data workshop back in 2012—but geez, was it tough to apply! This is one of the hardest practices for social scientists to learn because we’re so comfortable with APA formatting and its generic figure titles.

Think Twitter-like and aim for six- to eight-word titles. Look to newspaper articles for inspiration; journalists know how to include the “so what?” in their title. You may or may not read the full newspaper story for additional details. Same thing with charts: your audience may or may not read your full chart, so your title must give them the gist of your findings.

Add Context with Annotations

Annotations are call-out boxes that provide important contextual details. In PowerPoint, Word, or Excel, you can easily create annotations by inserting a text box. No fancy software required!

Here’s a great example from Mother Jones. A generic title would’ve been “Number of children living in poverty” or “Relationship between poverty and geographic location.” This 6-word title, “In Climbing Income Ladder, Location Matters,” ensures that readers grasp the chart’s message instantly. A 2-line caption adds more details underneath the title, and a few cities are annotated. The tweet’s text also reinforces this message.

This is how likely poor kids are to grow up and move out of poverty based on where they live http://t.co/5A5VIZkLBN pic.twitter.com/7BBZQJ9bdg — Mother Jones (@MotherJones) January 31, 2014

Establish a Text Hierarchy

Size your fonts according to their importance. A text hierarchy tells your viewers which information is most important (headings) and which information is least important (the regular ol’ paragraphs). In this example, I transformed a university’s annual report simply by adding an intentional text hierarchy. I call this makeover a two-hour turnaround because these are changes that anyone can make in two hours or less. Before, all the font was the same size, so the headings didn’t stand out. The report looked like a sea of words. After, we made the headings stand out by with larger fonts and by overlaying the text on top of a photograph. We also used a different color for each section to break up the sea of words into manageable chunks.

Lower the Reading Level

The vast majority of reports, handouts, infographics, dashboards, and slideshows that I review with clients are written at a reading grade level that’s so high that reading the documents feels like homework. In this example, we assessed our draft’s reading grade level with a free tool called readable.io. Then, we re-worded the title so that it was a closer match for our intended audience.

Finally, go share your chart!

You’ll need to edit it slightly depending on the medium — a chart for a presentation should look different than a chart for a dashboard. You can learn about presentation-specific, dashboard-specific, and report-specific techniques.

Learn More

Sign up for my free online course called Soar Beyond the Dusty Shelf Report. There are several quick lessons that help you get started with data storytelling.

Or, contact me about online courses, private workshops, and conference keynotes.

The post The Data Visualization Design Process: A Step-by-Step Guide for Beginners appeared first on Depict Data Studio.

She’s sharing examples of slider plots and step-by-step instructions for making them in Excel. Thanks for sharing, Lauren! –Ann

—

Hey everyone! Lauren Fox here from the Research & Evaluation Division (RED) of The University of Arkansas for Medical Sciences. Our group focuses on translating research into practice in fields like early childhood education, child nutrition, abuse prevention, and HIV education.

Much of my job involves working with faculty members and project leads to develop evaluation questions that lead to actionable data.

One of the biggest puzzles we face is how to translate those results (visually and verbally) so that everyone from expert audiences to laypeople can understand our findings and benefit from them.

The data viz world is full of options for visualizing basic data such as change over time, pre/post differences, and percentages/frequencies for a single point in time.

Sometimes however, your data (or your audience) demands a little more.

Case-in-point: When displaying margin of error is important.

The Challenge: Displaying Margin of Error Data

Back before the pandemic, one of our faculty asked for some help in visualizing her data for a conference on childhood nutrition.

I dressed it up the best I could, but it still fell far short of the best practices for data visualization. I figured there had to be a better way to display data with margins of error (a.k.a., the “95% confidence interval”), and set out to find it.

Spoiler alert: I didn’t find anything. So, a little bit at a time, over the course of several months, I built it myself. I call them “slider plots.”

Full disclosure: I didn’t know until after I developed these, but Stephanie Evergreen posted a rough sketch version of this idea using auto-calculated standard error bars back in 2017. Her title used “confidence intervals,” instead of “margin of error” so I missed it in my initial search.

While that means the basic idea behind my “slider plots” isn’t completely new, I’m still excited to build on her work and share this as a small step forward in chart design!

The Old Way

The “old way” involves using column charts with error bars.

The New Way: Slider Plots

Slider plots can be vertical or horizontal. Here’s an example of a vertical slider plot that shows policy ratings from four different neighborhoods.

Here’s a second example of a vertical slider plot that shows teacher ratings in four different schools.

Here’s what a horizontal slider plot of those policy ratings would look like.

And finally, here’s the horizontal version of the teacher ratings.

Download the Excel File with Step-by-Step Instructions

The process to create slider plots follows many of the same steps as creating dot plots and adds a few more to create and customize your margin of error bars.

Start to finish (from creating a data table, to building your dot plot, through creating and customizing your error bars), there are 15 steps, plus a few optional sub-steps.

I’d like to list them all here, but this post would definitely get a TLDR citation from the blog police (Too Long, Didn’t Read).

While many of the steps are similar, vertical slider plots are easier to build so I recommend you start with those first.

The horizontal version may be harder to build, but it has the same readability advantages of classic dot plot we all know and love.

As a bonus, you can download a free Excel file with step-by-step instructions and screenshots, as well as an end-product template you can use to make the process much faster.

Winning Hearts & Minds with Slider Plots

While slider plots do take some time to set up, the payoff for your effort is helping to expand the reach of data viz.

Many in the evaluation community have begun to adopt better data visualization practices to help communicate their work over the last few years, but there are still many spaces (workplaces, conferences, etc.) where we find resistance.

Some of that is fear of judgement; that we won’t be taken seriously as scientists by our colleagues if we present data in non-traditional ways.

If there’s one thing I’ve learned from being an evaluator in the early education space, it’s that if you want to change people’s minds (and then their behavior), you have to meet them where they are.

I’m under no illusions this chart type will suddenly convert all the data viz detractors or revolutionize the field.

However, the changes are small enough and familiar enough that they might be a bridge to expert audiences; a way they can slowly grow more comfortable with the idea that presenting data differently doesn’t make you less scientific.

Know Your Audience

As cool as it is to do something new, it’s important that I leave you with this reminder:

Most of the time, margins of error will not be important enough to visualize unless you’re dealing with an expert audience.

It will most likely confuse or distract less-advanced audiences from the point you’re trying to make.

However, you can try adding a little more explanation in the graph subtitle to bridge the gap (see my slider plots above for examples) if it’s critical for your lay audience to see the margins of error as well.

Connect with Lauren

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

The post How to Visualize Margin of Error Data in Excel with “Slider Plots” appeared first on Depict Data Studio.

Ready to escape the bar chart? I talked about Designs to Start Using Instead at the DataScienceGO conference in April 2021.

Watch the Conference Session

Choose Your Own Adventure

This was a Choose Your Own Adventure presentation, where I gave the conference attendees a chance to vote for the chart families they wanted to learn about.

These non-linear presentations aren’t for everyone. You need to be extremely comfortable with the topic area and with presenting. You can learn more about Choose Your Own Adventure presentations here.

Exploratory Data Visualization

First, we talked about my three favorite techniques for exploratory data visualization:

1. Spark lines
2. Data Bars
3. Heat Tables

Spark Lines

Want to add miniature trend lines to your spreadsheet? Here’s how:

• Highlight the top row of your dataset (the numbers that you want to visualize).
• Go to the Insert tab.
• Click on the Sparklines button.
• Follow the instructions: Choose where you want the sparklines to be placed. I usually position them off to the right side of my dataset.

Data Bars

Want to explore your dataset with miniature horizontal bars? Here’s how:

• Highlight the data you want to visualize.
• Stay on the Home tab.
• Click on the Conditional Formatting button.
• Choose a solid-filled Data Bar.

Heat Tables

We can also explore our dataset with instant color-coding. Here’s how to add a heat map or heat table to your spreadsheet:

• Highlight the data you want to visualize.
• Stay on the Home tab.
• Click on the Conditional Formatting button.
• Choose a Color Scale.

PLEASE avoid the inaccessible options—anything with red and green. Most of the Conditional Formatting options are absolute garbage, to be honest. Here’s a blog post that lists which Conditional Formatting options to avoid altogether—and what to use instead.

Want more info? In the video, you’ll see me open Excel and provide how-to tutorials.

Maps

Next, we discussed a few options for maps.

Choropleth Maps

You’ve seen this one: The regular ol’ color-coded map, or choropleth. Big numbers are dark. These maps are familiar and intuitive.

But, there’s a problem with regular maps: The large places can dwarf the small places. No matter how dark we color-in tiny Delaware, for example, the larger places like Texas and Alaska will always steal the show.

Cartographers have a name for this misleading issue with regular maps. It’s called The Alaska Effect.

Tile Grid Maps

Don’t worry, we’re not doomed by The Alaska Effect! There are a couple alternatives worth mentioning.

Square tile grid maps can help us overcome The Alaska Effect. Every location is the same shape and size, so now our audience only has to look at color. In other words, since Delaware and Texas are the same shape and size, we’re free to focus entirely on color.

BUT.

There’s a learning curve with tile grid maps. They’re almost too novel. Sometimes we spend more time focusing on why our home state isn’t in the right spot than on actually finding patterns in the data.

Tile grid maps have become more and more common over the years. In the video, I show you some real-life examples from the Urban Institute, the Washington Post, Child Trends, CNN, and National Geographic.

Hex Maps

Rather than using squares…. What if we try hexagons?

With six edges, hex maps give us more flexibility in arranging the shapes. That way, the maps can look closer to real-life maps.

In the video, I discuss some additional advantage of hex maps:

• Hex maps combat the Alaska Effect.
• Hex maps include more of the correct neighboring states compared to square maps.
• Hex maps include the correct southern tips.
• Hex maps include more notches for the Great Lakes.
• Hex maps visualize the correct four corners of the U.S.

And of course, hex maps aren’t just for the United States. You can create maps for zip codes, Census tracts, states, provinces, countries, etc. In the video, I show you a waffle map of African countries.

The post Escaping the Bar Chart appeared first on Depict Data Studio.

My friend Jon Schwabish from PolicyViz asked me to partner on his One Chart at a Time project, in which we’re helping you get better-acquainted with common and not-so-common chart types.

I created a tutorial on using colored phrases to visualize qualitative data. In this tutorial, you’ll learn:

• The first time I ever used colored phrases to visualize qualitative data;
• My favorite examples of colored phrases; and
• Practical tips for using colored phrases in your project.

Watch the Tutorial

The First Time I Used Colored Phrases in Data Visualization

In the first part of the video, you’ll see the first time I used colored phrases.

I was coding open-ended survey data during my Master’s thesis, and needed to visualize different themes that I was finding in the data.

Inside good ol’ Word, I added colored rectangles around key phrases.

The outcome wasn’t perfect, but it was better than regular text.

My Favorite Examples of Colored Phrases

In the second part of the video, you’ll see my favorite examples of colored phrases:

• ‘Stronger Together’ and ‘I Am Your Voice’—How the Nominees’ Convention Speeches Compare by the New York Times
• Did Michael Brown Charge? Eyewitnesses Paint a Muddled Picture by the Washington Post

Practical Tips for Using Colored Phrases

In the final section of the video, you’ll learn practical tips for using colored phrases to visualize qualitative data.

We’ll go through seven options:

1. Regular text
2. Bold
3. Italic
4. Underline
5. Color
6. Outline
7. Fill

You’ll learn the pros and cons of each approach, and see why I suggest using bold, colored, or filled text instead of the other options.

Your Turn

Let me know when you’ve applied colored phrases to your own project!

The post How to Visualize Qualitative Data with Colored Phrases appeared first on Depict Data Studio.

Women in Data is an international non-profit organization started in 2015 whose mission is to bring women together for career advancement and an opportunity to uplift one another. They have chapters throughout the world and each hold quarterly symposiums that include enlightening talks, expert panels and networking opportunities. 

Co-presenter Rebecca Pop is the founder of Vizlogue, a data visualization and storytelling lab that offers training and consulting services. 

Watch the Recorded Panel 

What’s Inside 

Here are some of the topics addressed during the panel. 

• Can you share your personal journey and how you got started with data visualization? 
• How do you approach data visualization problems? When you are working on a dataset, do you have standard steps/best practices that you follow every time? Are there any key focus areas one should be mindful of? Ann said, “Something so important to know in advance, is whether your audience is technical or non-technical. Technical audiences are people who like data, who love opening a spreadsheet, and are in a data career on purpose. Non-technical audiences are the opposite. They’d rather hire a consultant or let another staff member handle it. It’s probably the last thing on their to do that they want to tackle (and they probably procrastinate!)” 
• Important aspects to keep in mind while working with data are data integrity and data ethics. What is your take on data integrity and data ethics?  
• For someone just getting started in data visualization, it can be overwhelming with the number of tools and courses available these days, what is your advice for beginners? Can you also share some resources? Ann said, “At first, learn the one-hour version of about 10 different tools, but then take a 10-hour training on just one tool and go deeper and specialize. There’s a lot of great courses out there.”  
• What is the future of data visualization? How do you anticipate data visualization to differ in the coming years? 
• Data visualization is a very competitive field, how can one stand out from the crowd and make an impression? Ann said, “Don’t worry too much about having to be the best at everything, I don’t think it’s even possible. Just pick one and play on the strength that you already have and make that public in some way… For example, if you like Tableau post a lot of visualizations on your Tableau public profile. If you like R, post to your code on Github and connect with other people.” 
• What are the key skills required to be successful in data viz? How important is the tool? Ann said, “Chart choosing [is so important]. Are you going to use a pie chart, bar chart or something else altogether? It’s very difficult to take a table, rows and columns of summary statistics and figure out what chart that is going to be. I think a lot of people go to the standards like pie charts or bar charts.” She added, “One activity that you can try for yourself is find a table of data, set a timer for 10-15 minutes and see how many ideas you can come up with in that time period. When I started doing this, I could only come up with a couple of ideas in a 15-minute brainstorming session. Now I come up with 15 ideas in that same time period.” 

Learn More 

Here are some of the resources we mentioned during the panel: 

• The 3-step process for sharing data with users through data placemats: https://onlinelibrary.wiley.com/doi/full/10.1002/ev.20181  
• Ann’s favorite dataviz podcasts, which were mentioned in the Q&A after the recording ended: Explore Explain with Andy Kirk, Data Stories with Enrico Bertini and Moritz Stefaner, Data + Love with Zach Bowders, Data Viz Today with Alli Torban and Storytelling with Data with Cole Knaflic.

The post Women in Data appeared first on Depict Data Studio.

Benefits of Monthly Reports in Large Organisations

Why not just stick with annual reports? Or quarterly reports?

Monthly reports have at least eight advantages over quarterly and annual reports that outweigh the initial costs of automating them.

Data Literacy

Regular, consistently presented monthly reports enable people to become familiar with the terms and parameters used to produce metrics, thereby enhancing their understanding of where an organisation is heading in relation to its targets.

Everyone is on the Same Page

When the same parameters are used for each report the metrics are directly comparable, and everyone is on the same page.

Advanced Warning of Issues that Require Attention

Monthly reports enable potential problems to be detected and addressed early. 

Progress Towards Targets

Monthly reports show readers progress towards targets throughout the year. If metrics are falling behind targets, action can be taken to address the issue. Moreover, if a particular metric is meeting targets, resources can be diverted to prioritising areas that need attention.

Familiarity with Ebbs and Flows of the Year

Monthly reports can inform general patterns throughout the year.

In the example below, the organisation makes most of its sales in the month of July (around 25%) and in December (around 20%).

Generating reports on a monthly basis enables this organisation to estimate that if they haven’t made 50% of their sales by June (i.e. half way through the year) then there is no need to panic until they see the July report when a large peak in sales is expected based on prior years’ data.

Efficiency

In large organisations there are often multiple committees and multiple analysts talking about the same data.

In some cases, producing a report for one department or area of an organisation can require a similar workload to producing the same report across the organisation.

Monthly reports produced for the organisation results in fewer requests for standard metrics from multiple departments. I’ve also received fewer urgent requests as people often refer to the last month’s report if they are in a pinch for data.  

Data Integrity

With many eyes looking at the data on a regular basis, data integrity issues can be picked up early.

It’s a Way To Celebrate Successes

If an area of an organisation has done particularly well during a month it will show up in the visualisations and be a cause for immediate celebration (rather than waiting a whole year).

Similarly, monthly reports provide a timely way to determine if a strategic initiative, such as a new marketing campaign, is working.

How to Choose the Right Metrics for Monthly Reports

You’re ready to make a monthly report! Now what?

It’s important to select metrics that are relevant over monthly time scales when producing monthly reports.

Using student numbers at a fictional educational institution as an example, the student headcount may not change monthly, but the number of student commencements and student completions likely fluctuate as the year progresses.

In this instance, it would be more informative to present information on the number of student commencements and completions in the monthly reports, and then in the annual report present the headcount of the student population. 

An organisation’s annual report should include the metrics that are important but don’t fluctuate throughout the year.

Good Metrics for Monthly Reports: Metrics that Fluctuate Throughout the Year

Not Good for Monthly Reports: Metrics that Don’t Fluctuate Throughout the Year

Good for Annual Reports: Metrics that are Important But Don’t Fluctuate Throughout the Year

Reducing Your Own Workload through Automation

One of the purposes of monthly reports is to reduce your workload.

You don’t want the monthly reports to take days to produce every month, so I recommend choosing metrics that you can automate and have easy access to that do not require a lot of manual processing to produce results, but are still informative.

How to Choose the Right Visualisations for Monthly Reports

Combining data from multiple areas within organisations can produce complex and highly dynamic data sets.

Provide Enough Context: Compare to a Target and/or Compare to the Same Time in Previous Years

It is therefore important to choose types of data visualisation that give readers enough information and context.

This will enable readers to determine whether the organisation or a department is performing better or worse in relation to a target compared to the same time in previous years.

Cumulative Line Charts: My Preferred Method

Cumulative line charts are my preferred method of data visualisation.

These charts enable readers to easily relate previous years data without having to compare among bar charts.

Cumulative line charts also allow readers to easily see troughs and peaks in previous years and how these influence the results.

Visualising Progress Towards Targets

It is useful to include organisational targets to enable readers to easily compare them to the data.

Progress towards targets can be visualised in several ways.

Pie and donut charts are probably familiar to most readers or reports and can be used in certain circumstances.

See Ann’s post on the seven guidelines for using pie and donut charts and for alternatives to pie charts.

Below is a waffle chart as an example of an alternative to a pie chart.

Ask for Feedback from Your End-Users

I’ve received great feedback, tips and ideas from the end-users of the data. End-users are coming at your report with fresh eyes and they are the ones that have to use the information at the end of the day, so it’s worth incorporating their feedback if you can.

Better yet, take the Great Graphs online course and find out more. I did, and I haven’t looked back.

Share Your Tips for Monthly Reports

Have you got any tips for monthly reports? Does your organisation use them? Share your thoughts below!

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