1. Data Visualization

2. Overview Introducing Data Visualization
Perceptual Issues / Limitations
Problematic Design Choices
Appropriate Design Choices

3. Data Visualization Convert data into information
Present that information effectively (use the right display approach for desired outcome)
Play to the strengths of human perception
Avoid the weaknesses of human perception
Behave consistently with our expectations, which are based on previous exposure to those ways of visualizing data

4. Perceptual Issues / Limitations
We have a difficult time determining values shown in 3-D due to the perspective
Avoid 3-D display, especially if the third dimension does not convey any information
A 3-D display invariably obscures some other data, due to the bars overlapping / stacking
Sexy graphics only impress the first time and data visualizations are used repeatedly
Remove any unnecessary graphics; they just clutter the space and distract the user

5. Perceptual Issues / Limitations
We also have difficulty comparing areas that are 2-dimensional
We underestimate the size difference
Pie charts often trigger this issue
Circles suffer from the same issue:
Actual Sales Per Region Compared to Estimated Sales
North
South
East
West

6. Perceptual Issues / Limitations
Red and green are often used to indicate an alert (red) or that all is well (green)
8-12% of males and 1-2% of females have color perception issues
If these colors must be used then use heavily saturated versions to further distinguish them
Consider adding a texture as well as the color

7. Problematic Design Choices
Using vertical text, which is hard to read
Using a bar graph with only a single bar in it
Use regular text instead; it is faster and uses less space
Using separate bar graphs for actual vs. expected; combine them and show the bars side-by-side
Trying to show changes over time in a table
Line graphs are much better for this purpose

8. Problematic Design Choices
Using pie charts when the pieces do not add up to 100%
Users assume that pie charts represent parts of a whole
Use a bar graph instead (humans are better at comparing lengths than comparing areas)
Pie chart areas may also not be to scale
One slice is 30% and looks like half the pie
The user acts based upon the visual and does not consult the actual data value

9. Problematic Design Choices
Treemaps: Not a good fit for dashboards
We cannot determine the actual values
Accurate comparisons require a baseline for cell height and width and most do not have that (it is not a normal grid)

10. Problematic Design Choices
Various names
Star chart
Radar chart
Spider chart
Bad for dashboards
Takes time to track down a specific value
Interpretation not automatic

11. Problematic Design Choices
Using a bar graph with a non-0 baseline
Assume that the two bars had values of 20 and 22, respectively, yet your y-axis began at 19 and progressed to 23
In that scenario is looks like the two bars are significantly different
If the y-axis began at 0 and progressed to 40 the difference between the two would appear almost trivial (which is the truth)

12. Problematic Design Choices
Changing the scale between two graphs that the user will be comparing
For example, the x-axis of one bar graph is expressed in months, while the x-axis of the other is expressed in years
Sequencing the data in a manner that runs counter to our expectations
We expect time to progress along the x-axis from the past to the present and you reverse it
Avoid y-axis time; we don’t expect time to run from top to bottom or vice versa

13. Problematic Design Choices
Refreshing data too often
In some cases (air traffic control, web server traffic) you need real-time data
In other cases (quarterly reports) you need it to update every quarter
Showing data at too low of a granularity
Showing hourly trends may be all that is needed; tracking by the minute is overkill
Summaries are often more useful than transaction-level data

14. Problematic Design Choices
Providing data in the wrong units
If the manager needs to know the state of the budget it may be better to note the variance as a percentage rather than a dollar amount: 3.5% rather than: $11,702.33
By the same token, don’t show the budget amount and the current expenditures and expect the user to do the math; do it for them!

15. Problematic Design Choices
Too much data
It is possible to overwhelm the user with too much information
Scope creep impacts dashboards as much as anything else in information technology
What makes this worse is if no attempt is made to highlight the most important data, resulting in an undifferentiated mass of data
Or highlighting everything, which is also bad

16. Problematic Design Choices
Obscuring the actual data
Use of gridlines is not always necessary
They can help with scatter plots, but lighten the lines
They are definitely not needed if values are estimates
Background images behind the data in charts is almost always a detractor
Gradients can confuse color interpretation; the same color at different places on the gradient can look very different (it is an optical illusion)
Transparency is not helpful

17. Bullet Graphs
Replace gauges with bullet graphs

18. Matching Data to Display
Use text for discrete values:
YTD Sales
If that discrete value is evaluated against a qualitative and/or quantitative scale:
Use a bullet graph
If the goal is looking up a value:
Use a table
If the goal is comparing values:
Use a bar graph

19. Sparklines
Created by Edward Tufte. Shows the ‘shape’ of data over time:

20. Scatter Plots
Scatter plots are used to see correlations between two variables
Do they go up at the same time?
Down at the same time?
Needs a line of best fit to pinpoint direction and strength of correlation

21. Scatter Plots vs. Sparklines
For determining relationships between variables:
Use a scatter plot
For seeing trends (the ‘shape’ of data):
Use a line graph or sparkline
The line graph allows for comparison because numerous lines are shown together
Sparklines track a specific item across time and do not include other items, so comparison is not as easy

22. Bar Graphs
If a table has the same text repeated multiple times across various rows, consider converting it into a bar graph
The repeated text becomes one of the axes
If there is a criterion you are trying to match (or avoid) always show that in a bar graph with a line across the bars
If you need to compare multiple bar graphs across the same y-axis values / rows, organize the graphs horizontally

23. Icon Complexity
Select simple icons that pre-attentive processing will locate:
* X ▲ ▼ ↑ ↓ ◊ ! ‼ ♦
Avoid complex icons; they take too long to visually process

24. Icon Usage
Use icons to represent alerts, up/down trends, or whether something is available/unavailable or is on/off
Use the Gestalt principles to visually group related items
If a legend is used, sequence its values in the same order they appear in the chart or graph
If there are 3 bars for each item on the x-axis, the legend should have that sequence too

25. Relationships Within Data Set
Show relationships between data
Highlighting/selecting data in one area can cause that same information to highlight in another area of the dashboard
Allow users to ‘hide’ aspects of the data
If you are showing sales of x-large, large, medium, and small equipment allow the user to ‘hide’ any of those groupings
This is especially helpful with bar graphs and line graphs and can reveal new patterns

26. Proper Data Sequencing
Sequence data by a key metric rather than always using alphabetical sequence
For example, a table showing financial figures could be organized with the strongest performing areas at the top of the table and the weakest performing areas at the bottom
Or a bar graph with performance in different areas of the country could organize the data so that the far left bar is the best performing area
This doesn’t work for all data

27. Pie Chart Best Practices
If you do decide to use a pie chart:
Definitely order the slices by size
Don’t use colors that are too bright
Avoid having too many shades of the same color for the various slices (hard to tell them apart)
Include the label for each slice in the chart itself, rather than in a legend

28. Color Selection Grayscale works best for printing and photocopying
Save the vibrant, rich colors for the areas that need visual focus (use sparingly)
Use white space to separate areas of the dashboard

29. Improving Dense Data Scanning
In cases where the data is very close together (such as a table):
Use border lines to identify where summary columns / rows are located
Alternating each row with a light gray background color is also beneficial

30. Emphasizing Data To make data stand out you can also:
Increase line thickness
Put a box around the items of interest
Add a simple icon (as noted previously)
Alter brightness
Use a visual flicker effect
Ideally a shift between two color values, although a blink effect (the item disappears and then reappears) is also a possibility
Slow it down if you decide to go the blink route