This document discusses how companies are increasingly generating value from data as a byproduct of digital engagement and software usage, rather than directly from software sales. It outlines the four stages of software production and how the value has shifted from hardware to software to data. Companies are encouraged to view data generated from systems of engagement as a new kind of asset and data "store" rather than just a byproduct. Examples of data usage at companies like Facebook and the Bank of England are provided.

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
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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
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8. Source: PwC
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9. What else do they
have in common?
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10. All were founded
before 1989
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11. 1989 was 22 years ago
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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
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13. The average age of the
Top 20 Software Companies is
47 years
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14. That's partially skewed by the likes
of IBM (1911) NEC (1899!)
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15. Fewer outliers? 31 years
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16. 16

17. 17

18. 18

19. 19

20. 20

21. Growth through software sales
is slowing
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22. Growth through data is not
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23. The Four Stages of
Software Production
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24. STAGE 1
“The money is in the hardware,
not the software”
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25. STAGE 2
“Actually, the money is in the software”
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26. STAGE 3
“The money is not in the software,
but it is differentiating”
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27. STAGE 4
“Software is not even differentiating,
the value is the data”
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28. Microsoft's Share Price
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29. The Age of Software The Age of Data
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30. Software is a means
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31. Engagement with customers,
partners, employees,
and other stakeholders
generates data
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32. Systems of Record to
Systems of Engagement
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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
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34. • Location, location, location
• Influence patterns
• Customer service feedback
• Sentiment
• Geographical trends.
• Search Findings
• Predicting retail and property sales
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35. Right now, if you think of the data at
all, you probably consider it a by-
product
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36. “In contrast to most traditional survey
methods, they [search data] are collected as a
by-product of normal activity”
- the Bank of England
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37. W not use it?
hy
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38. Who’s already doing this?
38

39. 39

40. 40

41. 41

42. 42

43. What you Share Changes What You Drink
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44. RedMonk’s Developer Intelligence portal
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45. 45

46. A New Kind of Data “Store”
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47. Hadoop at Facebook
http://hadoopblog.blogspot.com/2010/05/facebook-has-worlds-largest-hadoop.html
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48. So if you're in business
and you're ignoring
Implicit and social data
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49. You're doing it wrong
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50. 50

51. QUESTIONS
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52. LinkedIn Members
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53. GitHub Rankings
1. Java (5)
2. Scala (18)
3. Clojure (22)
4. Groovy (23)
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54. GitHub Change
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55. Ohloh Monthly Contributors
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56. Language Tiers
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57. For Example
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58. Photo credits
Breadcrumb – diongillard on Flickr
Pathway by ^riza^ on Flickr
SF in Cloud – SF Chronicle
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