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Midnight January 28, 1986 Lives are on the line
Importance of rethinking data visualization Successfully Convince People with Data
http://[email protected]
The journey of simplicity
1. Seems simple“When you start looking at a problem and it seems really simple, you don’t really understand the complexity of the problem.” – Steve Jobs
Designing an Interface
The journey of simplicity1. Seems simple
“When you start looking at a problem and it seems really simple, you don’t really understand the complexity of the problem.” – Steve Jobs
2. Realize it’s complex
The journey of simplicity1. Seems simple
“When you start looking at a problem and it seems really simple, you don’t really understand the complexity of the problem.” – Steve Jobs
2. Realize it’s complex 3. Create complex solution
“Then you get into the problem, and you see that it’s really complicated, and you come up with all these convoluted solutions. That’s sort of the middle, and that’s where most people stop.” – Steve Jobs
The journey of simplicity1. Seems simple
“When you start looking at a problem and it seems really simple, you don’t really understand the complexity of the problem.” – Steve Jobs
2. Realize it’s complex 3. Create complex solution
“Then you get into the problem, and you see that it’s really complicated, and you come up with all these convoluted solutions. That’s sort of the middle, and that’s where most people stop.” – Steve Jobs
4. Complex solution is bad
The journey of simplicity1. Seems simple
“When you start looking at a problem and it seems really simple, you don’t really understand the complexity of the problem.” – Steve Jobs
2. Realize it’s complex 3. Create complex solution
“Then you get into the problem, and you see that it’s really complicated, and you come up with all these convoluted solutions. That’s sort of the middle, and that’s where most people stop.” – Steve Jobs
4. Complex solution is bad5. Simple powerful is hard
“But the really great person will keep on going and find the key, the underlying principle of the problem — and come up with an elegant, really beautiful solution that works.” – Steve Jobs
Prototype & Iterate
Example Problem
How so you analyze performance of a system?
What is a day in the life look What is a day in the life look like for a DBA who has like for a DBA who has performance issues?performance issues?
Example: performance data
Linux performance tools
Midnight January 28, 1986 Lives are on the line
Thanks to Edward Tufte
Night before the Flight
Jan 27,1986
Estimated launch temperature 29º
13 Pages Faxed
13 Pages Faxed
3 different types of names
Damage (in overwhelming detail) but No Temperatures
13 Pages Faxed
13 Pages Faxed
Missing Data for 5 erosion damage flights
Blow by Damage
Test engines fired horizontally
13 Pages Faxed
Shows “blow by”, not more important “erosion”
Damage at hottest and coldest launches* (of the flights shown)
Next day’s flight
13 Pages Faxed
Predict Temperature
Recommendation
55 65 7560 70 80
1
Original Engineering data
2
3
““damages atdamages atthe hottest the hottest and coldest and coldest Temperature” Temperature”
Would you launch?
Congressional Hearings Evidence
No Damage LegendDamage hard to read
Congressional Hearings Evidence
Temperature correlation difficult
55 65 7560 70 80
1
Original Data
2
3
Clearer
1. Y-Axis amount of damage (not number of damage)55 65 7560 70 80
4
8
12
1. Y-Axis amount of damage (not number of damage)2. Include successes *
55 65 7560 70 80
4
8
12
Clearer
* Only external temperatures were known not the temperature of the solid rocket boosters
Be accurate enough
1. Y-Axis amount of damage (not number of damage)2. Include successes3. Mark Differences
55 65 7560 70 80
4
8
12
Clearer
1. Y-Axis amount of damage (not number of damage)2. Include successes3. Mark Differences4. Normalize same temp
55 65 7560 70 80
4
8
12
Clearer
1. Y-Axis amount of damage (not number of damage)2. Include successes3. Mark Differences4. Normalize same temp
55 65 7560 70 80
4
8
12
Clearer
Damage on every flight below 65
No damage on every flight above 75
1. Y-Axis amount of damage (not number of damage)2. Include successes3. Mark Differences4. Normalize same temp
55 65 7560 70 80
4
8
12
Clearer
Known World
1. Y-Axis amount of damage (not number of damage)2. Include successes3. Mark Differences4. Normalize same temp5. Scale known vs unknown
55 65 7560 70 80
4
8
12
4
8
12
30 40 5035 45
XX
Clearer
Difficult
NASA Engineers Fail Congressional Investigators Fail Data Visualization is Difficult
But …
Lack of Clarity can be devastating
Visualization can be powerful
“If I can't picture it, I can't understand it”
Anscombe's QuartetI II III IV
x y x y x y x y10 8.04 10 9.14 10 7.46 8 6.588 6.95 8 8.14 8 6.77 8 5.76
13 7.58 13 8.74 13 12.74 8 7.719 8.81 9 8.77 9 7.11 8 8.84
11 8.33 11 9.26 11 7.81 8 8.4714 9.96 14 8.1 14 8.84 8 7.046 7.24 6 6.13 6 6.08 8 5.254 4.26 4 3.1 4 5.39 19 12.5
12 10.84 12 9.13 12 8.15 8 5.567 4.82 7 7.26 7 6.42 8 7.915 5.68 5 4.74 5 5.73 8 6.89
Average 9 7.5 9 7.5 9 7.5 9 7.5Standard Deviation 3.31 2.03 3.31 2.03 3.31 2.03 3.31 2.03Linear Regression 1.33 1.33 1.33 1.33
- Albert Einstein- Albert Einstein
Graphics for Anscombe’s Quartet
Counties in US
> 3000 Counties > 50 pages
“The humans … are exceptionally good at parsing visual information.” Knowledge representation in cognitive science. Westbury, C. & Wilensky, U. (1998)
Visualizations can also obfuscate
Pretty Picture
Spaghetti at the wall
Spaghetti at the wall II
Amazon Cloudwatch
Imagine Trying to Drive your Car
And is updated once and hourAnd is updated once and hour
Or would you like it to Or would you like it to look …look …
Would you want your dashboard to look like :Would you want your dashboard to look like :
If you are not tuning for time, you are wasting time
Max CPU
(yard stick)
Top Activity Top Activity
SQLSQLSessionsSessions
LOADLOAD
Looking at many targets
When Developers say When Developers say
The Database is slowThe Database is slow
AAS ~= 0AAS ~= 0
Do You Want?
Engineering Data?Engineering Data?
Pretty PicturesPretty Pictures
Do You Want?
Clean and Clear Clean and Clear
? ? ? ? ? ? ? ? ? ?? ?
Do You Want?
Summary• Textual statistics – difficult to parse• Pretty pictures misleading• Goal clear graphics powerful
Graphics add power and clarity to quantitative data
but there needs to be domain understanding
[email protected]://kylehailey.com
