1) The document discusses simple data visualization techniques for exploring large datasets ("Big Data"). 2) It recommends starting with basic approaches like color-coding data based on thresholds before moving to more advanced plots of frequencies, regular sampling, and combining multiple data sources. 3) Examples shown include visualizations of medicine costs by age, differences from average costs, samples from Dutch parliament membership over time, and car metadata from the Netherlands.

1. Simple datavisualisation to
unlock Big Data
Stephan Okhuijsen
3 juni 2015
Itude – Datagraver - VOJN
@Steeph - @Datagraver

7. Tools versus humans
• Tools can be too complex and have too
many features
• Tools distract from thinking about what you
really need/want to know
• Start simple
• Start a data safari (dixit Remko Helms)

8. The best tool to explore Big
Data is your curiosity
And it is fun!

9. START WITH A QUESTION

11. MOST BASIC: COLORCODING

12. Create a baseline
• Even distribution for every workday = 20%
• Mark significant deviation in color. For
example >25% = green and <15% is red
• Start your first data safari. Look at the first
line

13. Example pharmacy

14. NEXT: PLOT FREQUENCY

15. Pick a dimension
• Date/time/weekday/season
• Age
• Distance
• Weight
• Height
• Etc…

16. Medicine costs pp/y by age
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Medicijnkosten/jaar 2010

18. Another perspective
-4
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Absolute verschil met voorloper

19. NEXT: PLOT FREQUENCY
VARIATION

21. NEXT: REGULAR SAMPLING

22. Pick a point of reference
• Usefull for streams of data
• For instance compare situation at 8 o’clock
every morning (traffic data)
• Or January 1st for comparing years of
experience for members of parliament

23. Example Dutch Parliament

24. Another visualisation just for fun

25. NEXT: MAKE IT PHYSICAL

26. Physical map + heatmap
• Map data on the physical world
• Colorcode things like frequency or age

28. NOW YOU TRY

29. Example car database NL
• Registration date
• Weight
• Cilinders
• Seats
• Kilometer per liter
• Price
• Color
• Brand
• Serie
• Main fuel
• Type (MPV, Station,
Sedan, etc…)
• Numberplate
• Mass
• Insured

30. Car color

31. NEXT: COMBINE
DATASOURCES

33. LAST: CLEVER DIGGING

34. Meeting notes Parliament
• A bit more content related exercise.
• All meeting notes Dutch Parliament since
1995
• Who was there? What did they say? How
did they vote?

36. 99,998%

38. Thank you!
Stephan Okhuijsen
3 juni 2015
Itude – Datagraver - VOJN
@Steeph - @Datagraver