Week 3: Effective Data Visualizations

Objectives

1. Explain the importance of data visualizations. [CO 6]

2. Identify effective data visualizations. [CO 5]

3. Describe the five guidelines for effective data visualizations. [CO 5]

4. Next time… Demonstrate an ability to produce effective data visualizations to inform business and policy solutions. [CO 8]

Review Question 1

Which of the following is NOT one of the 5 steps in the D³M process?

1. Define objective

2. Analyze data

3. Apply intuition

4. Communicate insights

Review Question 2

Which of the following is one of the reasons it is important to use data when making (important) decisions?

1. Gut instincts are often correct

2. Data analysis is less susceptible to biases

3. Data analysis is more susceptible to biases

4. Anecdotal evidence is more convincing when communicating insights

Agenda

Data Storytelling

Why use data visualizations?

Five guidelines for effective data visualizations

Data storytelling

Importance of storytelling

Storytelling is fundamental to human existence. We connect, share, teach and learn through stories.

Statistics provide evidence but we need stories to truly communicate the insights to the audience.

Our brains are predisposed to remember stories.

3 aspects of a data story

Source: Dykes, B. (2019). Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals (1st edition). Wiley.

• Explain: narrative and data help explain what is happening. Context and commentary guide the audience through the meaning of what is being shared.

• Enlighten: a picture is worth a thousand words or numbers. Graphs and charts help you see trends and patterns. We will discuss some of the theory and best practices for making effective graphics. You want to make it as simple as possible for the audience to see your insight.

• Engage: narrative and visuals help engage the audience. We spend billions on visual media entertainment. The best movies may have good effects but definitely have a good story.

Data story ≠ data visualization

While clear and appealing data visualizations are part of a data story, they are not data stories by themselves.

Data stories bring your analytical insights to life.

Six elements of a data story

1. Data foundation

2. Main point

3. Explanatory focus (vs exploratory)

4. Linear sequence

5. Dramatic elements

6. Visual anchors

1. Story is grounded in data - something real that is observable

2. The story needs to have a main point. Have something to say.

3. Your goal is to explain some insight. What is going on? Who are the actors? What are the mechanisms? This relates to what we will discuss about causal inference.

4. Your story will consist of a series of supporting points or information that are tied together to build to some larger point. The supporting information should build: a) consumer prefs are shifting to favor Kale, b) we offer more Kale products and sales increase.

5. Dramatic elements include characters, setting, plots. These elements help humanize the data story and make it relatable. Set some context: sales are $200k. Is that good or bad? It depends on previous sales. Be selective about the information - only include information that helps to advance the narrative.

6. Visual anchors are the charts and graphs or other visuals you rely on to tell the story. We will spend considerable time on developing effective visuals to help tell your story.

Example

https://pudding.cool/2020/07/gendered-descriptions/

Data Visualizations

Why use data visualizations?

Suppose I do some research on US government spending on a variety of public programs, including health care, Social Security, and others.

I find that Federal spending on health care and Social Security programs increased, but this was not the case for other programs.

Which of the following slides best tells this story?

Which slide best tells our story?

 

      A.           B.

C.    D.    E.

Why use data visualizations?

 

Which of the slides best tells this story?

Why?

How else could you improve these slides?

Why use data visualizations?

 

What is the most important consideration when showcasing your work?

People will read, listen to, or see your findings.

Yet most of us spend very little time thinking about how to communicate our findings.

What are effective data visualizations?

We often spend days, weeks, months, or (sadly) years compiling and analyzing information for reports.

But most people spend minutes or hours designing the figures to showcase the data.

We might think people will just “get it” or that “the numbers will speak for themselves”

What are effective data visualizations?

Which of the visualizations best tells our story about federal spending?

Recall: Federal spending on health care and Social Security increased, but this was not the case for other programs.

Which is the most effective vis?

       A.            B.

       C.            D.

What is an effective data vis?

Which vis best tells this story?

Why?

How else could you improve these visualizations?

5 guidelines for a better data vis

0. Identify your objective

1. Show the data

2. Reduce the clutter

3. Integrate the graphics and text

4. Avoid the spaghetti chart

5. Start with gray

Adapted from Better Data Visualizations by Jonathan Schwabish

0. Identify your objective

Before designing your visualization, answer these questions:

• What do I want my figure to convey?

• What do I want my audience to do with this message?

What does this figure convey?

Do you come away from this figure with an idea of what you should do in response to this information?

What if we retitled it - Federal Spending on non-health care related social welfare programs is lagging?

What if we retitled it - The Federal Government is over-spending on Health Care?

1. Show (me!) the Data

Your audience will only grasp your point, argument, or story if they can see the data supporting it.

As visualization creators, our first challenge is deciding how much data to show and the best way to show it.

1. Show (me!) the Data

 

2. Reduce the clutter

The use of unnecessary visual elements distracts your audience from the central data and clutters the page.

Some examples?

• Heavy tick marks and grid lines

To Grid or Not to Grid

 

 

Source: https://medium.com/biovinci/good-chart-bad-chart-b6a6725f8795

2. Reduce the clutter

The use of unnecessary visual elements distracts your audience from the central data and clutters the page.

Some examples?

• Heavy tick marks and grid lines

• Overlapping and overwhelming data markers

Why tho?

Source: https://dvia.samizdat.cc/2017/line-graph/

2. Reduce the clutter

The use of unnecessary visual elements distracts your audience from the central data and clutters the page.

Some examples?

• Heavy tick marks and grid lines

• Overlapping and overwhelming data markers

• Gradient colors or patterns, adding dimensions

No Thanks, Excel.

2. Reduce the clutter

The use of unnecessary visual elements distracts your audience from the central data and clutters the page.

Some examples?

• Heavy tick marks and grid lines

• Overlapping and overwhelming data markers

• Gradient colors or patterns, adding dimensions

• Too much text, too many labels, overkill!

Too Much.

Source: https://getdolphins.com/blog/the-worst-graphs-of-2017/

2. Reduce the clutter

The use of unnecessary visual elements distracts your audience from the central data and clutters the page.

Some examples?

• Heavy tick marks and grid lines

• Overlapping and overwhelming data markers

• Gradient colors or patterns, adding dimensions

• Too much text, too many labels, overkill!

No thanks, Excel.

Simpler = Better

What changes would you make?

Source: https://getdolphins.com/blog/the-worst-graphs-of-2017/

3. Integrate graphics and text

Our main focus when creating a visualization is on the graphical elements - bars, points, lines, etc.

But the text we include is often just as important. In fact, the included annotations are vitally important to audience comprehension.

3 ways to integrate text into graphs and visuals:

1. Remove legends

2. Create active titles

3. Add detail

Recall the exercise on slide titles from earlier.

Which vis do you like best?

Which vis do you like best?

Which vis do you like best?

Which vis do you like best?

Which vis do you like best?

       A.            B.

       C.            D.

Removing legends

 

Legends take up valuable space.

When possible, remove them.

Adding data labels directly on the chart makes it faster and easier to interpret and gives our data more space.

Active titles

 

Write your vis titles like a newspaper headline.

Your title should tell the audience what they should learn from your data.

Which title is more useful?

 

Add detail

 

Annotations allows audience members - especially those with less data vis experience - to grasp and understand the content quickly.

Colors draw the readers’ attention to key points and can differentiate between “good” and “bad.”

 

                   

3. Integrate graphics and text

 

1. Remove legends

2. Create active titles

3. Add detail - annotations and color

4. Avoid the spaghetti chart

 

Avoid figures that contain too much information - line charts that look like spaghetti.

One solution is to convey less information - what is vital?

Another is to break one chart into smaller parts, ideally with identical formatting.

Spaghetti!

Better

Even better - what’s your point?

5. Start with gray

 

One last piece of advice - whenever you make your graph, start with all gray data elements.

This will force you to be purposeful and strategic in your use of color, labels, and other elements.

When using R, Tableau, Excel, or really any program literally starting in gray would take some effort. But consider this a mental process…

Starting with color vs. gray

 

When you start with all gray, it is pretty clear that this cannot be your finished version of the graph. This forces you to put some thought into what the finished version should look like.

Strategically adding color

 

Here you might have chosen to highlight the top two lines. Once you do that, you think… why did I choose these two? Maybe my take-away is not currently what I have as my slide title.

Objectives

1. Explain the importance of data visualizations. [CO 6]

2. Identify effective data visualizations. [CO 5]

3. Describe the five guidelines for effective data visualizations. [CO 5]

4. Next time… Demonstrate an ability to produce effective data visualizations to inform business and policy solutions. [CO 8]

Objective 1 Review Question

 

Why use data visualizations instead of text?

Objective 2 Review Question

 

Which vis?

Which vis do you like best?

Which vis do you like best?

Which vis do you like best?

Obj. 2: Which vis?

                                     A.

           B.        C.

Objective 3 Review Question

Which of the following steps should you accomplish before even starting your data vis?

1. Start with gray

2. Show the data

3. Identify your objective

4. Construct an active title

References

Brynjolfsson, Erik, and Kristina McElheran. “The rapid adoption of data-driven decision-making.” American Economic Review 106.5 (2016): 133-39.

Dykes, B. (2019). Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals (1st edition). Wiley.

Harford, T. (2022). The data detective: Ten easy rules to make sense of statistics. Penguin.

Schwabish, J. (2016). Better Presentations: A Guide for Scholars, Researchers, and Wonks. Columbia University Press. https://policyviz.com/pv_books/better-presentations-a-guide-for-scholars-researchers-and-wonks/

Schwabish, J. (2021). Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks. Columbia University Press. https://policyviz.com/pv_books/better-data-visualizations-a-guide-for-scholars-researchers-and-wonks/