The slides from the Qualtrics Summit. Other assets:
Derren Brown video: https://www.youtube.com/watch?v=vBPG_OBgTWg
Pre-attentive features and tasks: https://www.youtube.com/watch?feature=player_detailpage&v=wnvoZxe95bo
Examples of change blindness: http://www.cs.ubc.ca/~rensink/flicker/download/index.html
6. “Visual analytics is the representation
and presentation of data that exploits our
visual perception abilities in order to
amplify cognition.”
- Andy Kirk, author of “Data Visualization: a
successful design process”
7. LET’S LOOK AT SOME DATA
I II III IV
x y x y x y x y
10 8.04 10 9.14 10 7.46 8 6.58
8 6.95 8 8.14 8 6.77 8 5.76
13 7.58 13 8.74 13 12.74 8 7.71
9 8.81 9 8.77 9 7.11 8 8.84
11 8.33 11 9.26 11 7.81 8 8.47
14 9.96 14 8.1 14 8.84 8 7.04
6 7.24 6 6.13 6 6.08 8 5.25
4 4.26 4 3.1 4 5.39 19 12.5
12 10.84 12 9.13 12 8.15 8 5.56
7 4.82 7 7.26 7 6.42 8 7.91
5 5.68 5 4.74 5 5.73 8 6.89
8. LET’S LOOK AT SOME DATA
I II III IV
x y x y x y x y
10 8.04 10 9.14 10 7.46 8 6.58
8 6.95 8 8.14 8 6.77 8 5.76
13 7.58 13 8.74 13 12.74 8 7.71
9 8.81 9 8.77 9 7.11 8 8.84
11 8.33 11 9.26 11 7.81 8 8.47
14 9.96 14 8.1 14 8.84 8 7.04
6 7.24 6 6.13 6 6.08 8 5.25
4 4.26 4 3.1 4 5.39 19 12.5
12 10.84 12 9.13 12 8.15 8 5.56
7 4.82 7 7.26 7 6.42 8 7.91
5 5.68 5 4.74 5 5.73 8 6.89
Property Value
Mean of x in each case 9 (exact)
Variance of x in each case 11 (exact)
Mean of y in each case 7.50 (to 2 decimal places)
Variance of y in each case 4.122 or 4.127 (to 3 decimal places)
Correlation between x and y
in each case
0.816 (to 3 decimal places)
Linear regression line in each
case
y = 3.00 + 0.500x (to 2 and 3 decimal
places, respectively)
9. LET’S LOOK AT SOME DATA...VISUALLY
“Anscombe’s Quartet”
Source: Wikipedia
23. SUPPORTING THE CYCLE
• Incremental: Allow people to easily and incrementally change the data and how they are
looking at it
• Expressive: There is no single view for all tasks and all data
• Unified: Leverage the revolutionary changes in database technology
• Direct: Make the tool disappear so the user can directly interact with the data
click click
28. HOW DO HUMANS LIKE THEIR DATA?
Position
Color
Size
Shape
More Important
Less Important
29. HOW DO HUMANS LIKE THEIR DATA?
• Time: on an x-axis
• Location: on a map
• Comparing values: bar chart
• Exploring relationships:
scatter plot
• Relative proportions:
treemap
30. HOW DO HUMANS LIKE THEIR DATA?
Orient data so people can read it easily
Good Better
44. DASHBOARDING FOR THE 5-SECOND TEST
• Most important view goes
on top or top-left
• Legends go near their
views
• Avoid using multiple color
schemes on a single
dashboard
• Use 5 views or fewer in
dashboards
• Provide interactivity
45. DASHBOARDING FOR THE 5-SECOND TEST
Use your words!
• Titles
• Axes
• Key facts and figures
• Units
• Remove extra digits in
numbers
• Great tooltips
I like to start these sessions off with a game I call “count the nines.” So, count the nines. Raise your hand when you think you know how many nines there are on here. [pause] In fact, just go ahead and shout it out. If you know how many nines are on here, shout out the answer. [pause]
NOW count the nines. How many nines do you see? Raise your hand when you know.
So what is visual analytics? THAT’s visual analytics.
Those are really fancy words for what you just experienced. Humans can see visual patterns very well, but only when the patterns really play to a human’s strengths.
Let’s see another example. Here’s some data. The are 4 sets of data here, each with 11 sets of x-y coordinates.
For the purposes of this exercise, let’s assume the x data represents, in millions, the net sales of a single retail store over the course of a month. Let’s say the y data represents, in millions, the total profit from that store. So we’re looking here at a set of points that represent profit by sales, where each point is a single store. The four data sets represent regions, say, West, Central, South and East.
Let’s say you’re a manager responsible for maximizing profit at these stores.
What’s your move? [pause for 10 seconds, let people try to say smart things ]
OK, OK, so you’d typically have a bit more information than that when you’re making a decision. So now let’s look at some more information about these data sets. Maybe we can learn something about them from their means, or their variances. When we’re crunching numbers, we rely a lot on things like means and variances. And probably looking at correlation or doing a linear regression would help, too. It turns out that these four data sets all have the same means, the same variances, the same x-y correlations, and even boil down to an identical linear regression.
So … what’s your move? [Pause for 10 seconds or so]
Here are these same four data sets, plotted visually, with trend lines.
Now, what’s your move? [Let the audience make some suggestions. You can chime in with things like, “Yeah, you might want to talk to the manager of the outlier in set 3 and see what she’s doing right” or “You might want to talk to the managers of some of the stores in set 4 and see why their profits are underperforming compared to stores with similar sales.”]
What other pieces of information might you want? [Let them make suggestions, and if necessary you can chime in with things like “You might want to see how many orders each store is producing, or what categories of product they’re selling most, or how frequently they offer discounts.”]
Humans are slow at mental math. We’re not designed to manipulate complex numbers in our heads. Go ahead and try to solve this multiplication problem in your head.
If we give you a pencil and paper, all of a sudden this problem becomes a lot easier to solve. How much easier?
About 5 times easier. It takes educated people an average of 50 seconds to solve this problem in their head. Give them the right tools, and suddenly it’s solved in just under 10 seconds.
Which product subcategory is the most unprofitable?
Which product subcategory is the most unprofitable?
Now … which product subcategory is the most unprofitable?
Preattentive attributes are information we can process visually almost immediately, before sending the information to the attention processing parts of our brain. This is information we process and understand almost unconsciously. These are generally the best ways to present data, because we can see these patterns without thinking too hard.
If you have time/Internet/sound, show this video: http://www.youtube.com/watch?feature=player_detailpage&v=wnvoZxe95bo
Show Jock’s interruption video
Show Jock’s interruption video. If you have time and an internet connection, this YouTube video is great, too: http://www.youtube.com/watch?feature=player_detailpage&v=vBPG_OBgTWg
Visual analysis isn’t just looking at a chart, or using colors – it’s an entire lifecycle that includes identifying and getting your data, establishing the structure of that data, choosing the best way to visualize that data, drawing conclusions or insight from those visualizations, and then getting buy-in around any conclusions supported by that data, which means you have to be able to tell a compelling story succinctly. And to help a team benefit from visual analysis, you need to support the whole cycle of visual analysis.
The key words are are see, understand, and people. Tableau builds software for people, not specialists. We believe anyone should be able to harness the power of data. That’s our mission.