6.1.2.Design data visualizations - sj50179/Google-Data-Analytics-Professional-Certificate GitHub Wiki

Principles of design

In this reading, you are going to learn more about using the elements of art and principles of design to create effective visualizations. So far, we have learned that communicating data visually is a form of art. Now, it's time to explore the nine design principles for creating beautiful and effective data visualizations that can be informative and appeal to all audiences.

After we go through the various design principles, spend some time examining the visual examples to ensure that you have a thorough understanding of how the principle is put into practice. Let’s get into it!

Nine basic principles of design

There are nine basic principles of design that data analysts should think about when building their visualizations.

1. Balance: The design of a data visualization is balanced when the key visual elements, like color and shape, are distributed evenly. This doesn’t mean that you need complete symmetry, but your visualization shouldn’t have one side distracting from the other. If your data visualization is balanced, this could mean that the lines used to create the graphics are similar in length on both sides, or that the space between objects is equal. For example, this column chart (also shown below) is balanced; even though the columns are different heights and the chart isn’t symmetrical, the colors, width, and spacing of the columns keep this data visualization balanced. The colors provide sufficient contrast to each other so that you can pay attention to both the motivation level and the energy level displayed.

2. Emphasis: Your data visualization should have a focal point, so that your audience knows where to concentrate. In other words, your visualizations should emphasize the most important data so that users recognize it first. Using color and value is one effective way to make this happen. By using contrasting colors, you can make certain that graphic elements—and the data shown in those elements—stand out.

For example, you will notice a heat map data visualization below from The Pudding’s “Where Slang Comes From" article. This heat map uses colors and value intensity to emphasize the states where search interest is highest. You can visually identify the increase in the search over time from low interest to high interest. This way, you are able to quickly grasp the key idea being presented without knowing the specific data values.

3. Movement: Movement can refer to the path the viewer’s eye travels as they look at a data visualization, or literal movement created by animations. Movement in data visualization should mimic the way people usually read. You can use lines and colors to pull the viewer’s attention across the page.

For example, notice how the average line in this combo chart (also shown below) draws your attention from left to right. Even though this example isn’t moving, it still uses the movement principle to guide viewers’ understanding of the data.

4. Pattern: You can use similar shapes and colors to create patterns in your data visualization. This can be useful in a lot of different ways. For example, you can use patterns to highlight similarities between different data sets, or break up a pattern with a unique shape, color, or line to create more emphasis.

In the example below, the different colored categories of this stacked column chart (also shown below) ****are a consistent pattern that makes it easier to compare book sales by genre in each column. Notice in the chart that the Fantasy & Sci Fi category (royal blue) is increasing over time even as the general category (green) is staying about the same.

5. Repetition: Repeating chart types, shapes, or colors adds to the effectiveness of your visualization. Think about the book sales chart from the previous example: the repetition of the colors helps the audience understand that there are distinct sets of data. You may notice this repetition in all of the examples we have reviewed so far. Take some time to review each of the previous examples and notice the elements that are repeated to create a meaningful visual story.

6. Proportion: Proportion is another way that you can demonstrate the importance of certain data. Using various colors and sizes helps demonstrate that you are calling attention to a specific visual over others. If you make one chart in a dashboard larger than the others, then you are calling attention to it. It is important to make sure that each chart accurately reflects and visualizes the relationship among the values in it. In this dashboard (also shown below), the slice sizes and colors of the pie chart compared to the data in the table help make the number of donuts eaten by each person the focal point.

These first six principles of design are key considerations that you can make while you are creating your data visualization. These next three principles are useful checks once your data visualization is finished. If you have applied the initial six principles thoughtfully, then you will probably recognize these next three principles within your visualizations already.

7. Rhythm: This refers to creating a sense of movement or flow in your visualization. Rhythm is closely tied to the movement principle. If your finished design doesn’t successfully create a flow, you might want to rearrange some of the elements to improve the rhythm.

8. Variety: Your visualizations should have some variety in the chart types, lines, shapes, colors, and values you use. Variety keeps the audience engaged. But it is good to find balance since too much variety can confuse people. The variety you include should make your dashboards and other visualizations feel interesting and unified.

9. Unity: The last principle is unity. This means that your final data visualization should be cohesive. If the visual is disjointed or not well organized, it will be confusing and overwhelming.

Being a data analyst means learning to think in a lot of different ways. These nine principles of design can help guide you as you create effective and interesting visualizations.

Elements for effective visuals

• Clear meaning: good visualizations clearly communicate their intended insight
• Sophisticated use of contrast: helps separate the most important data from the rest using visual context that our brains naturally look for
• Refined execution: deep attention to detail, using visual elements like lines, shapes, colors, value, space and movement

Question

Data visualizations have three essential elements: clear meaning, a sophisticated use of contrast, and refined execution. What is refined execution?

• Separating the most important data from the rest
• Providing visual context
• Clearly communicating insights
• Deep attention to detail

Correct. Refined execution means paying deep attention to detail. This is done by using visual elements such as lines, shapes, colors, value, space, and movement.

Data is beautiful

At this point, you might be asking yourself: What makes a good visualization? Is it the data you use? Or maybe it is the story that it tells? In this reading, you are going to learn more about what makes data visualizations successful by exploring David McCandless’ elements of successful data visualization and evaluating three examples based on those elements. Data visualization can change our perspective and allow us to notice data in new, beautiful ways. A picture is worth a thousand words—that’s true in data too! You will have the option to save all of the data visualization examples that are used throughout this reading; these are great examples of successful data visualization that you can use for future inspiration.

Four elements of successful visualizations

The Venn diagram by David McCandless identifies four elements of successful visualizations:

• Information (data): The information or data that you are trying to convey is a key building block for your data visualization. Without information or data, you cannot communicate your findings successfully.
• Story (concept): Story allows you to share your data in meaningful and interesting ways. Without a story, your visualization is informative, but not really inspiring.
• Goal (function): The goal of your data visualization makes the data useful and usable. This is what you are trying to achieve with your visualization. Without a goal, your visualization might still be informative, but can’t generate actionable insights.
• Visual form (metaphor): The visual form element is what gives your data visualization structure and makes it beautiful. Without visual form, your data is not visualized yet.

All four of these elements are important on their own, but a successful data visualization balances all four. For example, if your data visualization has only two elements, like the information and story, you have a rough outline. This can be really useful in your early planning stages, but is not polished or informative enough to share. Even three elements are not quite enough— you need to consider all four to create a successful data visualization.

In the next part of this reading, you will use these elements to examine three data visualization examples and evaluate why they are successful.

Example 1: visualization of lifespan data

View the data

The Live Long visualization uses data from multiple sources to examine what choices can add or subtract years to our lifespans. The data has been compiled for public access in a spreadsheet. Click the link below and select "Use Template" to view the data.

Link to the template: KIB - Live Long & Prosper

Examine the four elements

This data visualization uses all four elements that were previously introduced. Here is how they are all working together to make the visualization successful:

• Information (data): The data is organized and outlined in the visualization, so this visualization clearly is based on plenty of information or data.
• Story (concept): The concept behind this data visualization is clearly stated in the title: finding out what really extends a lifespan. But, the visualization also uses the data gathered and presented earlier to put together the ultimate recipe for a long life, which is a nice ending to the overarching story being told in this visualization.
• Goal (function): The goal of this data visualization is also pretty clear from the title: It is trying to find out what can extend someone’s lifespan, which might help people make better choices about their own lives.
• Visual form (metaphor): The data visualization uses a lot of visual components to help structure the information. First, it has been organized as a horizontal bar chart and has both years lost and years gained as a result of particular choices. This makes it easier to read each choice and the commentary. It also draws your eye downward so that you have to read all of the elements before you get to the ultimate recipe at the end. This data visualization also uses color to communicate how strong the science behind each finding was, which makes this data visualization beautiful and informative.

Example 2: visualization of dog breed comparison

View the data

The Best in Show visualization uses data about different dog breeds from the American Kennel Club. The data has been compiled in a spreadsheet. Click the link below and select "Use Template" to view the data.

Link to the template: KIB - Best in Show

Examine the four elements

This visualization compares the popularity of different dog breeds to a more objective data score. Consider how it uses the elements of successful data visualization:

• Information (data): If you view the data, you can explore the metrics being illustrated in the visualization.
• Story (concept): The visualization shows which dogs are overrated, which are rightly ignored, and those that are really hot dogs! And, the visualization reveals some overlooked treasures you may not have known about previously.
• Goal (function): The visualization is interested in exploring the relationship between popularity and the objective data scores for different dog breeds. By comparing these data points, you can learn more about how different dog breeds are perceived.
• Visual form (metaphor): In addition to the actual four-square structure of this visualization, other visual cues are used to communicate information about the dataset. The most obvious is that the data points are represented as dog symbols. Further, the size of a dog symbol and the direction the dog symbol faces communicate other details about the data.

Example 3: visualization of rising sea levels

Examine the four elements

This When Sea Levels Attack visualization illustrates how much sea levels are projected to rise over the course of 8,000 years. The silhouettes of different cities with different sea levels, rising from right to left, helps to drive home how much of the world will be affected as sea levels continue to rise. Here is how this data visualization stacks up using the four elements of successful visualization:

• Information (data): This visualization uses climate data on rising sea levels from a variety of sources, including NASA and the Intergovernmental Panel on Climate Change. In addition to that data, it also uses recorded sea levels from around the world to help illustrate how much rising sea levels will affect the world.
• Story (concept): The visualization tells a very clear story: Over the course of 8,000 years, much of the world as we know it will be underwater.
• Goal (function): The goal of this project is to demonstrate how soon rising sea levels are going to affect us on a global scale. Using both data and the visual form, this visualization makes rising sea levels feel more real to the audience.
• Visual form (metaphor): The city silhouettes in this visualization are a beautiful way to drive home the point of the visualization. It gives the audience a metaphor for how rising sea levels will affect the world around them in a way that showing just the raw numbers can’t do. And for a more global perspective, the visualization also uses inset maps.

Key takeaways

Notice how all of these visualizations balance all four elements of successful visualization. They clearly incorporate data, use storytelling to make that data meaningful, focus on a specific goal, and structure the data with visual forms to make it beautiful and communicative. The more you practice thinking about these elements, the more you will be able to include them in your own data visualizations.

Design thinking and visualizations

Design thinking

• A process used to solve complex problems in a user-centric way

Design thinking for visualization improvement

Design thinking for data visualization involves five phases:

1. Empathize: Thinking about the emotions and needs of the target audience for the data visualization
2. Define: Figuring out exactly what your audience needs from the data
3. Ideate: Generating ideas for data visualization
4. Prototype: Putting visualizations together for testing and feedback
5. Test: Showing prototype visualizations to people before stakeholders see them

As interactive dashboards become more popular for data visualization, new importance has been placed on efficiency and user-friendliness. In this reading, you will learn how design thinking can improve an interactive dashboard. As a junior analyst, you wouldn’t be expected to create an interactive dashboard on your own, but you can use design thinking to suggest ways that developers can improve data visualizations and dashboards.

An example: online banking dashboard

Suppose you are an analyst at a bank that has just released a new dashboard in their online banking application. This section describes how you might explore this dashboard like a new user would, consider a user’s needs, and come up with ideas to improve data visualization in the dashboard. The dashboard in the banking application has the following data visualization elements:

• Monthly spending is shown as a donut chart that reflects different categories like utilities, housing, transportation, education, and groceries.
• When customers set a budget for a category, the donut chart shows filled and unfilled portions in the same view.
• Customers can also set an overall spending limit, and the dashboard will automatically assign the budgeted amounts (unfilled areas of the donut chart) to each category based on past spending trends.

Empathize

First, empathize by putting yourself in the shoes of a customer who has a checking account with the bank.

• Do the colors and labels make sense in the visualization?
• How easy is it to set or change a budget?
• When you click on a spending category in the donut chart, are the transactions in the category displayed?

What is the main purpose of the data visualization? If you answered that it was to help customers stay within budget or to save money, you are right! Saving money was a top customer need for the dashboard.

Define

Now, imagine that you are helping dashboard designers define other things that customers might want to achieve besides saving money.

What other data visualizations might be needed?

• Track income (in addition to spending)
• Track other spending that doesn’t neatly fit into the set categories (this is sometimes called discretionary spending)
• Pay off debt

Can you think of anything else?

Ideate

Next, ideate additional features for the dashboard and share them with the software development team.

• What new data visualizations would help customers?
• Would you recommend bar charts or line charts in addition to the standard donut chart?
• Would you recommend allowing users to create their own (custom) categories?

Can you think of anything else?

Prototype

Finally, developers can prototype the next version of the dashboard with new and improved data visualizations.

Test

Developers can close the cycle by having you (and others) test the prototype before it is sent to stakeholders for review and approval.

Key takeaways

This design thinking example showed how important it is to:

• Understand the needs of users
• Generate new ideas for data visualizations
• Make incremental improvements to data visualizations over time

You can refer to the following articles for more information about design thinking:

• Three Critical Aspects of Design Thinking for Big Data Solutions
• Data and Design Thinking: Why Use Data in the Design Process?

Test your knowledge on designing data visualizations

TOTAL POINTS 4

Question 1

Which element of design can add visual form to your data and help build the structure for your visualization?

• Movement
• Shape
• Space
• Line

Correct. Lines add visual form to your data and help build the structure for your visualization.

Question 2

Which of the following are elements for effective visuals? Select all that apply.

• Clear meaning
• Clear goal
• Refined execution
• Sophisticated use of contrast

Correct. The elements for effective visuals are clear meaning, sophisticated use of contrast, and refined execution.

Question 3

Fill in the blank: Design thinking is a process used to solve complex problems in a _____ way.

• step-by-step
• pre-attentive
• action-oriented
• user-centric

Correct. Design thinking is a process used to solve complex problems in a user-centric way. It enables data analysts to identify alternative strategies for visualizations.

Question 4

While creating a data visualization for your stakeholders, you realize certain colors might make it more difficult for your audience to understand the data. So, you choose colors that are more accessible. What phase of the design process does this represent?

• Test
• Empathize
• Prototype
• Define

Correct. Considering appropriate colors for a visualization is part of the empathize design phase. During the empathize phase, you consider the emotions and needs of the target audience for your data visualization.