The document outlines key principles and guidelines across various data-related roles, including data janitor, data engineer, data scientist, machine learning specialist, and head of data. It emphasizes the importance of clear definitions, data quality, actionable metrics, and pragmatic approaches in analytics, while warning against common pitfalls such as overengineering and blind trust in technology. Overall, it serves as a guide for effectively managing and utilizing data within organizations.

1. 1

2. Data Janitor 101
Daniel Molnar, Microsoft
Data Natives 2016
2

3. tl;dr
3

4. tl;dr
4 KISS is the philosophy,
3

5. tl;dr
4 KISS is the philosophy,
4 take the long view, invest in durable knowledge,
3

6. tl;dr
4 KISS is the philosophy,
4 take the long view, invest in durable knowledge,
4 strive for fast and good enough,
3

7. tl;dr
4 KISS is the philosophy,
4 take the long view, invest in durable knowledge,
4 strive for fast and good enough,
4 just because you can doesn't mean you should.
3

8. CAP #1
BUSINESS ANALYST
4

9. "... American MBA? ... if
you don’t understand
something it must be simple
and only take five
minutes."
1
Sean Murphy, PingThings
5

10. Don't
6

11. Don't
4 unicorn my a**,
6

12. Don't
4 unicorn my a**,
4 hockey stick here for me,
6

13. Don't
4 unicorn my a**,
4 hockey stick here for me,
4 skip leg day.
6

14. Do
7

15. Do
4 make definitions,
7

16. Do
4 make definitions,
4 show direction,
7

17. Do
4 make definitions,
4 show direction,
4 care about data quality,
7

18. Do
4 make definitions,
4 show direction,
4 care about data quality,
4 rule dashboards.
7

19. KPIs that matter
8

20. KPIs that matter
4 DAU, WAU, MAU, LTV, churn,
8

21. KPIs that matter
4 DAU, WAU, MAU, LTV, churn,
4 cohorts, segments, funnels,
8

22. KPIs that matter
4 DAU, WAU, MAU, LTV, churn,
4 cohorts, segments, funnels,
4 first hour, first day.
8

23. Approach
9

24. Approach
4 KPIs must hurt (aka no feelgood metrics),
9

25. Approach
4 KPIs must hurt (aka no feelgood metrics),
4 you are what you measure,
9

26. Approach
4 KPIs must hurt (aka no feelgood metrics),
4 you are what you measure,
4 you can run in one direction,
9

27. Approach
4 KPIs must hurt (aka no feelgood metrics),
4 you are what you measure,
4 you can run in one direction,
4 is it actionable (the Friday 1700 test).
9

28. Toolset
10

29. Toolset
4 Excel,
10

30. Toolset
4 Excel,
4 SQL,
10

31. Toolset
4 Excel,
4 SQL,
4 Metabase.
10

32. Heroes of the day
Joel Spolsky: You Suck at Excel
Dan McKinley: Data Driven Products Now!
11

33. CAP #2
DATA ENGINEER
12

34. "Don't reinvent
the flat tyre."
1
Alan Kay
13

35. Don't
14

36. Don't
4 just Apache it,
14

37. Don't
4 just Apache it,
4 build a Hadoop JENGA (10x-235x slow),
14

38. Don't
4 just Apache it,
4 build a Hadoop JENGA (10x-235x slow),
4 real-time it,
14

39. Don't
4 just Apache it,
4 build a Hadoop JENGA (10x-235x slow),
4 real-time it,
4 stream it,
14

40. Don't
4 just Apache it,
4 build a Hadoop JENGA (10x-235x slow),
4 real-time it,
4 stream it,
4 overengineer it.
14

41. Do
15

42. Do
4 embrace dirty reality
(entity recognition makes a data engineer),
15

43. Do
4 embrace dirty reality
(entity recognition makes a data engineer),
4 ETL, events and DWH,
15

44. Do
4 embrace dirty reality
(entity recognition makes a data engineer),
4 ETL, events and DWH,
4 data quality (know your leakage),
15

45. Do
4 embrace dirty reality
(entity recognition makes a data engineer),
4 ETL, events and DWH,
4 data quality (know your leakage),
4 testing (yes, you can even unit test data).
15

46. Approach
16

47. Approach
4 avoid GIGO,
16

48. Approach
4 avoid GIGO,
4 pedal to the metal, skip the overhead,
16

49. Approach
4 avoid GIGO,
4 pedal to the metal, skip the overhead,
4 know that big RAM is eating big data,
16

50. Approach
4 avoid GIGO,
4 pedal to the metal, skip the overhead,
4 know that big RAM is eating big data,
4 use open source, pragmatic, cloud service agnostic
tools.
16

51. Toolset
17

52. Toolset
4 UNIX (bash, make),
17

53. Toolset
4 UNIX (bash, make),
4 Python,
17

54. Toolset
4 UNIX (bash, make),
4 Python,
4 SQL,
17

55. Toolset
4 UNIX (bash, make),
4 Python,
4 SQL,
4 ETL in batch (mETL, night-shift)
17

56. Toolset
4 UNIX (bash, make),
4 Python,
4 SQL,
4 ETL in batch (mETL, night-shift)
4 event tracking (Hamustro, logsanitizer, RPi?),
17

57. Toolset
4 UNIX (bash, make),
4 Python,
4 SQL,
4 ETL in batch (mETL, night-shift)
4 event tracking (Hamustro, logsanitizer, RPi?),
4 DWH = MPP SQL (Azure DWH, Redshift, Vertica...).
17

58. Heroes of the day
James Mickens: Computers are a Sadness, I am the Cure
Dan McKinley: Choose Boring Technology
David Beazley: Discovering Python
18

59. CAP #3
DATA SCIENTIST
19

60. "Friends don’t let friends
calculate p-values
(without fully
understanding them)."
1
Scott Weingart
20

61. Don't
21

62. Don't
4 expect CSVs and produce models whatever it takes,
21

63. Don't
4 expect CSVs and produce models whatever it takes,
4 expect that you have to explore the laws of Universe,
21

64. Don't
4 expect CSVs and produce models whatever it takes,
4 expect that you have to explore the laws of Universe,
4 forget about Occam's razor,
21

65. Don't
4 expect CSVs and produce models whatever it takes,
4 expect that you have to explore the laws of Universe,
4 forget about Occam's razor,
4 A/B test (only if it REALLY REALLY makes sense).
21

66. Do
22

67. Do
4 user testing to define context (usertesting.com),
22

68. Do
4 user testing to define context (usertesting.com),
4 talk to users via surveys,
22

69. Do
4 user testing to define context (usertesting.com),
4 talk to users via surveys,
4 embed yourself in departments (personas),
22

70. Do
4 user testing to define context (usertesting.com),
4 talk to users via surveys,
4 embed yourself in departments (personas),
4 have common sense.
22

71. Approach
23

72. Approach
4 you mostly tell what not to do,
23

73. Approach
4 you mostly tell what not to do,
4 it's hard, but still the only way,
23

74. Approach
4 you mostly tell what not to do,
4 it's hard, but still the only way,
4 persist when not finding anything or trivialities,
23

75. Approach
4 you mostly tell what not to do,
4 it's hard, but still the only way,
4 persist when not finding anything or trivialities,
4 kill teh lurking causation.
23

76. A/B
24

77. A/B
4 think twice about TCO,
24

78. A/B
4 think twice about TCO,
4 the world isn’t identically distributed,
24

79. A/B
4 think twice about TCO,
4 the world isn’t identically distributed,
4 random variation will cheat you in small samples,
24

80. A/B
4 think twice about TCO,
4 the world isn’t identically distributed,
4 random variation will cheat you in small samples,
4 most A/B test results are illusory,
24

81. A/B
4 think twice about TCO,
4 the world isn’t identically distributed,
4 random variation will cheat you in small samples,
4 most A/B test results are illusory,
4 small data -> go Bayesian = less certainty.
24

82. Toolset
25

83. Toolset
4 SQL,
25

84. Toolset
4 SQL,
4 Wizard,
25

85. Toolset
4 SQL,
4 Wizard,
4 Python,
25

86. Toolset
4 SQL,
4 Wizard,
4 Python,
4 R (only to anger CS peeps).
25

87. Heroes of the day
Evan Miller: Wizard Statistical Analyzer
Chris Stucchio talks and posts on testing
26

88. Machine Learning
CAP #4
27

89. Don't
28

90. Don't
4 need a PhD,
28

91. Don't
4 need a PhD,
4 develop new unique matrix algos, please,
28

92. Don't
4 need a PhD,
4 develop new unique matrix algos, please,
4 need more than Excel,
28

93. Don't
4 need a PhD,
4 develop new unique matrix algos, please,
4 need more than Excel,
4 give false hope.
28

94. Do
29

95. Do
4 deploy good enough fast,
29

96. Do
4 deploy good enough fast,
4 copy Kaggle (ensembles, random forest, XGBoost),
29

97. Do
4 deploy good enough fast,
4 copy Kaggle (ensembles, random forest, XGBoost),
4 feature engineer,
29

98. Do
4 deploy good enough fast,
4 copy Kaggle (ensembles, random forest, XGBoost),
4 feature engineer,
4 build core data/feature (augment and enhance).
29

99. Approach
30

100. Approach
4 the Mailchimp way
(offline built model redeployed each quarter),
30

101. Approach
4 the Mailchimp way
(offline built model redeployed each quarter),
4 hybrid approaches (domain expert, vanilla ML),
30

102. Approach
4 the Mailchimp way
(offline built model redeployed each quarter),
4 hybrid approaches (domain expert, vanilla ML),
4 you are a machine instructor,
30

103. Approach
4 the Mailchimp way
(offline built model redeployed each quarter),
4 hybrid approaches (domain expert, vanilla ML),
4 you are a machine instructor,
4 Tensorflow (logic to clients, handle models).
30

104. Toolset
31

105. Toolset
4 Excel,
31

106. Toolset
4 Excel,
4 Wizard,
31

107. Toolset
4 Excel,
4 Wizard,
4 BigML,
31

108. Toolset
4 Excel,
4 Wizard,
4 BigML,
4 Python.
31

109. Heroes of the day
John Foreman: Data Smart
Jeroen Janssen: Data Science at the Command Line
32

110. CAP #5
HEAD OF DATA
33

111. "In god we trust
everybody else bring
data to the table."
1
W. Edwards Deming
34

112. Don't
35

113. Don't
4 believe the hype,
35

114. Don't
4 believe the hype,
4 trust no-one, just benchmarks,
35

115. Don't
4 believe the hype,
4 trust no-one, just benchmarks,
4 let black box take over,
35

116. Don't
4 believe the hype,
4 trust no-one, just benchmarks,
4 let black box take over,
4 expect hiring to be easy.
35

117. Do
36

118. Do
4 maintain data mythology,
36

119. Do
4 maintain data mythology,
4 keep the view backwards straight,
36

120. Do
4 maintain data mythology,
4 keep the view backwards straight,
4 expect emotions,
36

121. Do
4 maintain data mythology,
4 keep the view backwards straight,
4 expect emotions,
4 see the future.
36

122. Approach
37

123. Approach
4 train to be the bearer of the bad news,
37

124. Approach
4 train to be the bearer of the bad news,
4 laugh at endless growth without saturation,
37

125. Approach
4 train to be the bearer of the bad news,
4 laugh at endless growth without saturation,
4 handle the cargo cult (inverse causality).
37

126. Marketing
38

127. Marketing
4 Google Analytics (sampling, off by 20%, no user
granularity, no raw, 150k per year),
38

128. Marketing
4 Google Analytics (sampling, off by 20%, no user
granularity, no raw, 150k per year),
4 CPA, FB CPA, mobile CPA, conversion, attribution,
38

129. Marketing
4 Google Analytics (sampling, off by 20%, no user
granularity, no raw, 150k per year),
4 CPA, FB CPA, mobile CPA, conversion, attribution,
4 Net Promoter Score.
38

130. Heroes of the day
Dan Lyons: Disrupted
Venkatesh Rao: The Gervais Principle
39

131. Thank you!
@soobrosa
visuals: @xkcd, @DorsaAmir, ˙Cаvin 〄,
thelearningcurvedotca, JD Hancock, Thomas Hawk, jonolist,
Kalexanderson
40