The document discusses the importance of providing context and narrative along with data analysis in order to create value for clients. It argues that data analysis alone is not sufficient and that stories must be told to explain the meaning, value and implications of the data as well as recommend next steps. The document provides tips for effective communication such as answering questions in order, understanding the client's perspective and constraints, and presenting findings in a clear and visually appealing way.

1. Numbers
need
words.

2. By Barton Poulson
Founder, datalab.cc
Intermountain Big Data Conference
Salt Lake City, UT
21 November 2015

3. Or, how narrative
takes your analysis
from irrelevant to
irreplaceable.

4. You’ve
got
data mojo!

6. {but…}

7. Data mojo
≠
value.

8. Data mojo
× story =
value.

9. That’s
multiplicative,
not additive.

10. {mojo} × 0 = 0

11. No story?
No value.

12. What
clients
want.

13. The
meaning.

14. The
value.

15. The
plan.

16. The
problems.

17. Egocentrism.

18. Jean Piaget.

19. Perspective test.

20. False consensus.

21. “Everybody drinks.”

22. Even in 1640.

23. Anchoring.

24. Broncos FTW!

25. Seahawks 43-8.

26. Probability.

27. Bayes’Theorem.

28. Client
!=
you.

29. Can’t crawl in.

30. What
to
do?

31. 1.Answer the questions.
2.Know the context.
3.Give the next steps.
4.Make it look good.

32. Answer
the
questions.

33. -State the question.
-Give your answer.
-Qualify as needed.
-Go in order.

34. Should you
explain process?

35. Analysis is
always about
simplification.

37. – Ludwig Mies van der Rohe,
after Robert Browning
Less is more.

39. – Albert Einstein
If you can’t explain it simply,
you don’t understand it
well enough.

40. Know
the
context.

41. Code Stats
Domain
Data
Science

42. Code Stats
Domain
Data
Science
Social

44. – Yogi Berra
We’re lost,
but we’re making
good time.

45. -Unstated constraints?
-Contradictory goals?
-Client’s ability?
-Social effects?

46. You have correlation.
They want causation.

47. Give the
next
steps.

49. – William James
My thinking is first and last
and always for the sake
of my doing.

50. -Recommend actions.
-Goal oriented.
-Be specific.

52. -Baby steps.
-Doable by client.
-Build on each step.

53. Make it
look
good.

57. -Medium.com
-PLOS.org
-Royal Society
Open Science

58. E
^
> <

60. Sorry, Mitt.

61. Make it flow.
Make it
make sense.

62. Robert Sternberg.

63. Well-presented good ideas
are better than
well-presented bad ideas.

64. Poorly-presented good ideas
are indistiguishable from
poorly-presented bad ideas.

65. Be creative.
Be original.
Even in data science.
Especially then.

66. Superstar
graphics
are nice.

67. Myst.

68. Portal.

69. But so are
simple
ones.

70. Asteroids.

71. Dwarf Fortress.

72. Canabalt.

73. Minecraft.

74. A
tiny
example.

75. Was
Berkeley
biased?

76. The first
impression.

78. Looks like bias.
But wait…
break it down.

80. But data isn’t
enough.
Look at context.

81. Why do programs
vary in class size?

82. Why do
acceptance rates
differ?

83. Why do men and
women apply to
different programs?

84. Possible next
steps?

85. -Identify departments.
-Check admissions criteria.
-Check prior education.
-Check funding.
-Adjust or raise funds?

86. Fire it up!

87. linkedin.com/company/datalab.cc
facebook.com/datalab.cc
@datalabcc
bartonpoulson.com
linkedin.com/in/bartonpoulson