1. Midnight
January 28, 1986
Lives are on the line
Thanks to Edward Tufte
Night before the Flight
Jan 27,1986
Importance of Data Visualization

2. Estimated launch
temperature 29º

3. 13 Pages Faxed

4. 13 Pages Faxed
3 different types of names

5. Damage (in overwhelming detail) but No Temperatures
13 Pages Faxed

6. 13 Pages Faxed
Test engines, not launches,
fired horizontally
Missing temperatures for 5 erosion damage flights
Temperatures but limited Damage

7. 13 Pages Faxed
“blow by”, not more important “erosion”, (at hottest and coldest launches)

8. 13 Pages Faxed
Predict
Temperature
Recommendation

9. 55 65 7560 70 80
1
Original Engineering data
2
3
damages atdamages at
the hottestthe hottest
and coldestand coldest
temperaturetemperature
-- managementmanagement
Would you launch?

11. Congressional Hearings
Evidence
No Damage Legend
Damage hard to read

12. Congressional Hearings
Evidence
Temperature
correlation difficult

13. 55 65 7560 70 80
1
Original Data
2
3

14. Clearer
1. Y-Axis amount of damage (not number of damage)
55 65 7560 70 80
4
8
12

15. 1. Y-Axis amount of damage (not number of damage)
2. Include successes
55 65 7560 70 80
4
8
12
Clearer

16. 1. Y-Axis amount of damage (not number of damage)
2. Include successes
3. Mark Differences
55 65 7560 70 80
4
8
12
Clearer

17. 1. Y-Axis amount of damage (not number of damage)
2. Include successes
3. Mark Differences
4. Normalize same temp
55 65 7560 70 80
4
8
12
Clearer

18. 1. Y-Axis amount of damage (not number of damage)
2. Include successes
3. Mark Differences
4. 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

19. 1. Y-Axis amount of damage (not number of damage)
2. Include successes
3. Mark Differences
4. Normalize same temp
55 65 7560 70 80
4
8
12
Clearer

20. 1. Y-Axis amount of damage (not number of damage)
2. Include successes
3. Mark Differences
4. Normalize same temp
5. Scale known vs unknown
55 65 7560 70 80
4
8
12
4
8
12
30 40 5035 45
XX
Clearer

21. Difficult
 NASA Engineers Fail
 Congressional Investigators Fail
 Data Visualization is Difficult
But …
Lack of Clarity can be devastating

22. 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)

23. “If I can't picture it, I can't understand it”
Anscombe's Quartet
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
Average 9 7.5 9 7.5 9 7.5 9 7.5
Standard Deviation 3.31 2.03 3.31 2.03 3.31 2.03 3.31 2.03
Linear Regression 1.33 1.33 1.33 1.33
- Albert Einstein- Albert Einstein

24. Graphics for Anscombe’s Quartet

25. Do You Want?
Engineering Data?Engineering Data?

26. Pretty PicturesPretty Pictures
Do You Want?

27. Clean and ClearClean and Clear
? ? ? ?? ? ? ?
? ?? ?
Do You Want?

28. What is a day in the life lookWhat is a day in the life look
like for a DBA who haslike for a DBA who has
performance issues?performance issues?
Tuning the Database
Anscombe's Quartet
I II III IV
x y x y x y x y
Average 9 7.5 9 7.5 9 7.5 9 7.5
Standard Deviation 3.31 2.03 3.31 2.03 3.31 2.03 3.31 2.03
Linear Regression 1.33 1.33 1.33 1.33
ComplexComplex
AveragesAverages

29. Imagine Trying to Drive your
Car
And is updated once and hourAnd is updated once and hour
Or would you like it toOr would you like it to
look …look …
Would you want your dashboard to look like :Would you want your dashboard to look like :

30. How Can We Open the Black Box?
Max CPU
(yard stick)
Top ActivityTop Activity
SQLSQL
SessionsSessions
LOADLOAD