Category Archives: Perceptual Edge

Stephen Few: Now You See It

Portland

Readers:

Stephen_Few2I was in Portland, Oregon last week attending three data visualization workshops by industry expert, Stephen Few. I was very excited to be sitting at the foot of the master for three days and soak in all of this great dataviz information.

Last Thursday, was the third workshop, Now You See It which is based on Steve’s best-selling book (see photo below).

To not give away too much of what Steve is teaching in the workshops, I have decided to discuss one of our workshop topics, human perceptual and cognitive strengths.

You can find future workshops by Steve on his website, Perceptual Edge.

Best Regards,

Michael

Now You See It

 

Designed for Humans

Good visualizations and good visualization tools are carefully designed to take advantage of human perceptual and cognitive strengths and to augment human abilities that are weak. If the goal is to count the number of circles, this visualization isn’t well designed. It is difficult to remember what you have and have not counted.

Quickly, tell me how many blue circles you see below.

Design for Humans 1

The visualization below, shows the same number of circles, however, is well designed for the counting task. Because the circles are grouped into small sets of five each, it is easy to remember which groups have and have not been counted, easy to quickly count the number of circles in each group, and easy to discover with little effort that each of the five groups contains the same number of circles (i.e., five), resulting in a total count of 25 circles.

Design for Humans 2

The arrangement below is even better yet.

Design for Humans 3

Information visualization makes possible an ideal balance between unconscious perceptual and conscious cognitive processes. With the proper tools, we can shift much of the analytical process from conscious processes in the brain to pre-attentive processes of visual perception, letting our eyes do what they do extremely well.

Stephen Few: Information Dashboard Design

Readers:

Stephen_Few2I am in Portland, Oregon this week attending three data visualization workshops by industry expert, Stephen Few. I am very excited to be sitting at the foot of the master for three days and soak in all of this great dataviz information.

Today, was the second workshop, Information Dashboard Design which is based on Steve’s best-selling book (see photo below).

To not give away too much of what Steve is teaching in the workshops, I have decided to discuss one of the dashboard exercises we did in class. The goal here was to find what we feel is wrong with the dashboard.

I will show you the dashboard first. Then, you can see our critique below.

You can find future workshops by Steve on his website, Perceptual Edge.

Best Regards,

Michael

Information Dashboard Design

 

Dashboard To Critique

CORDA Airlines Dashboard

Critique Key Points

  • Top left chart – Only left hand corner chart has anything to do with flight loading
  • Top left chart – are flight numbers useful?
  • Two Expand/Print buttons – Need more clarity (right-click on chart would be a better choice)
  • Top right chart – Poor use of pie charts – size of pies are telling largest sales channel – use small multiple bar charts, total sales as a fourth bar chart
  • Redundant use of “February” – In the title and in charts
  • Bottom left chart – why does it have a pie chart in it?
  • Bottom right chart – map may be better as a bar chart (geographical display could be useful if we had more information). Current way bubbles are being expressed is not useful (use % cancellations instead). Symbols may have a different meaning every day
  • Bottom right chart – CORDAir Logo – is this necessary?
  • Location of drop-down. Not clear if it applies to top left chart or all charts
  • Backgrounds – heavy colors, gradients
  • Instructions should be in a separate help document. Only need to learn this once.
  • Top left chart: Faint Image in background. Suppose to look like a flight seating map. Do you really want to see this every day? It is a visual distraction.
  • IMPORTANT: Is there visual context offered with any of the graphs? No. This is critical.

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Dashboard Example Source: Website of Corda Technologies Incorporated, which has since been acquired by Domo.

Stephen Few: Show Me The Numbers

Readers:

Stephen_Few2I am in Portland, Oregon this week attending three data visualization workshops by industry expert, Stephen Few. I am very excited to be sitting at the foot of the master for three days and soak in all of this great dataviz information.

Yesterday, was the first workshop, Show Me the Numbers which is based on Steve’s best-selling book (see photo below).

To not give away too much of what Steve is teaching in the workshops, I have decided to give one “before and after” example each day with Steve’s explanation of why he made the changes he did.

You can find future workshops by Steve on his website, Perceptual Edge.

Best Regards,

Michael

Show Me the Numbers

 

“Before” Example

In the example below, the message contained in the titles is not clearly displayed in the graphs. The message deals with the ratio of indirect to total sales – how it is declining domestically, while holding steady internationally. You’d have to work hard to get this message the display as it is currently designed.

Before - Show Me the Numbers

 

“After” Example

The revised example below, however, is designed very specifically to display the intended message. Because this graph, is skillfully designed to communicate, its message is crystal clear. A key feature that makes this so is the choice of percentage for the quantitative scale, rather than dollars.

After - Show Me the Numbers

Additional Thoughts From Steve

The type of graph that is selected and the way it’s designed also have great impact on the message that is communicated. By simply switching from a line graph to a bar graph, the decrease in job satisfaction among those without college degrees in their later years is no longer as obvious.

More Thoughts - Show Me the Numbers

Stephen Few: Why Do We Visualize Quantitative Data?

Readers:

Stephen_FewIt has been a while since I have discussed some of the latest creative thoughts on data visualization from Stephen Few. I have read all of Steve’s books, attended several classes from him, and religiously follow his blog and newsletter on his website, Perceptual Edge.

For those of you who don’t know, Stephen Few is the Founder & Principal of Perceptual Edge. Perceptual Edge, founded in 2003, is a consultancy that was established to help organizations learn to design simple information displays for effective analysis and communication.

Steve has stated that his company will probably always be a company of one or two people, which is the perfect size for him. With 25 years of experience as an innovator, consultant, and educator in the fields of business intelligence and information design, he is now considered the leading expert in data visualization for data sense-making and communication.

Steve writes a quarterly Visual Business Intelligence Newsletter, speaks and teaches internationally, and provides design consulting. In 2004, he wrote the first comprehensive and practical guide to business graphics entitled Show Me the Numbers, now in its second edition. In 2006, he wrote the first and only guide to the visual design of dashboards, entitled Information Dashboard Design, also now in its second edition. In 2009, he wrote the first introduction for non-statisticians to visual data analysis, entitled Now You See It.

Here is his latest thoughts from his newsletter.

Best regards,

Michael

 

Why Do We Visualize Quantitative Data?

Per Stephen Few, we visualize quantitative data to perform three fundamental tasks in an effort to achieve three essential goals:

Web

These three tasks are so fundamental to data visualization, Steve used them to define the term, as follows:

Data visualization is the use of visual representations to explore, make sense of, and communicate data.

Steve poses the question of why is it that we must sometimes use graphical displays to perform these tasks rather than other forms of representation? Why not always express values as numbers in tables? Why express them visually rather than audibly?

Essentially, there is only one good reason to express quantitative data visually: some features of quantitative data can be best perceived and understood, and some quantitative tasks can be best performed, when values are displayed graphically. This is so because of the ways our brains work. Vision is by far our dominant sense. We have evolved to perform many data sensing and processing tasks visually. This has been so since the days of our earliest ancestors who survived and learned to thrive on the African savannah. What visual perception evolved to do especially well, it can do faster and better than the conscious thinking parts of our brains. Data exploration, sensemaking, and communication should always involve an intimate collaboration between seeing and thinking (i.e., visual thinking).

Despite this essential reason for visualizing data, people often do it for reasons that are misguided. Steve dispels a few common myths about data visualization.

Myth #1: We visualize data because some people are visual learners.

While it is true that some people have greater visual thinking abilities than others and that some people have a greater interest in images than others, all people with normal perceptual abilities are predominantly visual. Everyone benefits from data visualization, whether they consider themselves visual learners or not, including those who prefer numbers.

Myth #2: We visualize data for people who have difficulty understanding numbers.

While it is true that some people are more comfortable with quantitative concepts and mathematics than others, even the brightest mathematicians benefit from seeing quantitative information displayed visually. Data visualization is not a dumbed-down expression of quantitative concepts.

Myth #3: We visualize data to grab people’s attention with eye-catching but inevitably less informative displays.

Visualizations don’t need to be dumbed down to be engaging. It isn’t necessary to sacrifice content in lieu of appearance. Data can always be displayed in ways that are optimally informative, pleasing to the eye, and engaging. To engage with a data display without being well-informed of something useful is a waste.

Myth #4: The best data visualizers are those who have been trained in graphic arts.

While training in graphic arts can be useful, it is much more important to understand the data and be trained in visual thinking and communication. Graphic arts training that focuses on marketing (i.e., persuading people to buy or do something through manipulation) and artistry rather than communication can actually get in the way of effective data visualization.

Myth #5: Graphics provide the best means of telling stories contained in data.

While it is true that graphics are often useful and sometimes even essential for data-based storytelling, it isn’t storytelling itself that demands graphics. Much of storytelling is best expressed in words and numbers rather than images. Graphics are useful for storytelling because some features of data are best understood by our brains when they’re presented visually.

We visualize data because the human brain can perceive particular quantitative features and perform particular quantitative tasks most effectively when the data is expressed graphically. Visual data processing provides optimal support for the following:

1. Seeing the big picture

Graphs reveal the big picture: an overview of a data set. An overview summarizes the data’s essential characteristics, from which we can discern what’s routine vs. exceptional.

The series of three bar graphs below provides an overview of the opinions that 15 countries had about America in 2004, not long after the events of 9/11 and the military campaigns that followed.

graph-of-country-opinions

Steve first discovered this information in the following form on the website of PBS:

table-of-country-opinions

Based on this table of numbers, he had to read each value one at a time and, because working memory is limited to three or four simultaneous chunks of information at a time, he couldn’t use this display to construct and hold an overview of these countries’ opinions in his head. To solve this problem, he redisplayed this information as the three bar graphs shown above, which provided the overview that he wanted. Steve was able to use it to quickly get a sense of these countries’ opinions overall and in comparison to one another.

Bonus: Here is a link to where Steve discusses the example above on his website.

2. Easily and rapidly comparing values

Try to quickly compare the magnitudes of values using a table of numbers, such as the one shown above. You can’t, because numbers must be read one at a time and only two numbers can be compared at a time. Graphs, however, such as the bar graphs above, make it possible to see all of the values at once and to easily and rapidly compare them.

3. Seeing patterns among values

Many quantitative messages are revealed in patterns formed by sets of values. These patterns describe the nature of change through time, how values are distributed, and correlations, to name a few.

Try to construct the pattern of monthly change in either domestic or international sales for the entire year using the table below.

table-of-sales-data

Difficult, isn’t it? The line graph below, however, presents the patterns of change in a way that can be perceived immediately, without conscious effort.

graph-of-sales-data

You can thank processes that take place in your visual cortex for this. The visual cortex perceives patterns and then the conscious thinking parts of our brains make sense of them.

4. Comparing patterns

Visual representations of patterns are easy to compare. Not only can the independent patterns of domestic and international sales be easily perceived by viewing the graph above, but they can also be compared to one another to determine how they are similar and different.

In Summary

These four quantitative features and activities require visual displays. This is why we visualize quantitative data.