my deck for Digital London. Not sure I nailed the data science angle, but i got good feedback. i look at the economics of valuation based on data, for example.

1. Digital Paths and Digital Breadcrumbs:
Social, Data Science and New Business Models
10.20.2005
Digital London March 2012

2. Cloud is eating the World
2

3. Source: John C. McCallum
3

4. Relentless
"@stoweboyd: Today’s basic Kindle costs
80% less, weighs 40% less, 2/3 less space
and holds 7x as many books as the
original"
4

5. What do these companies
have in common?
5

6. 6

7. They make up PwC's
Global Software Top 20
7

8. Source: PwC
8

9. What else do they
have in common?
9

10. All were founded
before 1989
10

11. 1989 was 22 years ago
11

12. Things that happened in 1989

The Soviet Union left Afghanistan

Time and Warner Merge

The Exxon Valdez oil spill

Tiananmen Square

Rain Man won best picture
12

13. The average age of the
Top 20 Software Companies is
47 years
13

14. That's partially skewed by the likes
of IBM (1911) NEC (1899!)
14

15. Fewer outliers? 31 years
15

16. 16

17. 17

18. 18

19. 19

20. 20

21. Growth through software sales
is slowing
21

22. Growth through data is not
22

23. The Four Stages of
Software Production
23

24. STAGE 1
“The money is in the hardware,
not the software”
24

25. STAGE 2
“Actually, the money is in the software”
25

26. STAGE 3
“The money is not in the software,
but it is differentiating”
26

27. STAGE 4
“Software is not even differentiating,
the value is the data”
27

28. Microsoft's Share Price
28

29. The Age of Software The Age of Data
29

30. Software is a means
30

31. Engagement with customers,
partners, employees,
and other stakeholders
generates data
31

32. Systems of Record to
Systems of Engagement
32

33. “The lumber industry sells what used to be
waste — sawdust, chips, and shredded wood
— for a pretty profit. Today you’ll find these
by-products in synthetic fireplace logs,
concrete, mulch, particle board, fuel, livestock
and pet bedding, winter road traction, weed
killing and more.”
Jason Fried, 37signals
33

34. • Location, location, location
• Influence patterns
• Customer service feedback
• Sentiment
• Geographical trends.
• Search Findings
• Predicting retail and property sales
34

35. Right now, if you think of the data at
all, you probably consider it a by-
product
35

36. “In contrast to most traditional survey
methods, they [search data] are collected as a
by-product of normal activity”
- the Bank of England
36

37. W not use it?
hy
37

38. Who’s already doing this?
38

39. 39

40. 40

41. 41

42. 42

43. What you Share Changes What You Drink
43

44. RedMonk’s Developer Intelligence portal
44

45. 45

46. A New Kind of Data “Store”
46

47. Hadoop at Facebook
http://hadoopblog.blogspot.com/2010/05/facebook-has-worlds-largest-hadoop.html
47

48. So if you're in business
and you're ignoring
Implicit and social data
48

49. You're doing it wrong
49

50. 50

51. QUESTIONS
51

52. LinkedIn Members
52

53. GitHub Rankings
1. Java (5)
2. Scala (18)
3. Clojure (22)
4. Groovy (23)
53

54. GitHub Change
54

55. Ohloh Monthly Contributors
55

56. Language Tiers
56

57. For Example
57

58. Photo credits
Breadcrumb – diongillard on Flickr
Pathway by ^riza^ on Flickr
SF in Cloud – SF Chronicle
58