Date post: | 11-Aug-2014 |
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Data & Analytics |
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Midnight January 28, 1986 Lives are on the line
Thanks to Edward Tufte
Night before the Flight
Jan 27,1986
Importance of Data Visualization
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
Test engines, not launches, fired horizontally
Missing temperatures for 5 erosion damage flights
Temperatures but limited Damage
13 Pages Faxed
“blow by”, not more important “erosion”, (at hottest and coldest launches)
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
- - managementmanagement
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
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
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
Counties in US 3101 Counties 50 pages
“The humans … are exceptionally good at parsing visual information.” Knowledge representation in cognitive science. Westbury, C. & Wilensky, U. (1998)
“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
Do You Want?
Engineering Data?Engineering Data?
Pretty PicturesPretty Pictures
Do You Want?
Clean and Clear Clean and Clear
? ? ? ? ? ? ? ? ? ?? ?
Do You Want?
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?
Tuning the Database
Anscombe's QuartetI II III IV
x y x y x y x yAverage 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
ComplexComplexAveragesAverages
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 :
How Can We Open the Black Box?
Max CPU
(yard stick)
Top Activity Top Activity
SQLSQLSessionsSessions
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