In technical communication, the main thing is to keep the main thing the main thing. There are multiple ways to ensure this principle. Some of these ways require careful chart fine-tuning. However, there is one tool that is easy to master, fast to apply, and that provides a high return on the investment rate. I refer to chart titles. In this talk, I had two main theses. My first thesis is that most of you suck in communication (and not only data visualization). My second thesis is that you can quickly improve your graphs by merely adding a good title. The importance of good titles is not new to my preaching, but I thought it was an excellent thing to formalize this thesis a bit, and I'm thankful to the NDR organizers for giving me this opportunity. This is the slide stack from my NDR presentation. See my post https://gorelik.net/2020/07/28/how-to-suck-less-in-data-visualization-and-professional-communication/

1. Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net
The biggest missed opportunity
in data visualization

2. Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net
The biggest missed opportunity
in data communication
visualization

3. Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net
The biggest missed opportunity
in professional communication
data communication
visualization

4. The main thing is to keep the
main thing the main thing

5. Most of you suck.

6. Most of you suck,
but you don’t have to.

7. Who am I?
The biggest missed opportunity in data visualization
Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net
2001

8. The main thing is to keep the
main thing the main thing

9. Is this a good
graph?

10. What’s the main
thing here?

11. What’s the main
thing here?

12. What’s the main
thing here?

13. The biggest missed opportunity in
data visualization
Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net
Why most of you suck?
What do you usually do?
What should you do?

14. Why most of you suck?

15. Becoming a
data scientist
is
easy

16. Becoming a
data engineer
is
easy

17. Becoming a
data analyst
is
easy

18. Becoming a
programmer
is
easy

19. Becoming a
statistician
is
easy

20. Becoming a
data scientist/programmer/statistician
is
easy

23. “Your analyses are fascinating, although
I don’t understand 95% of them”
My co-worker in an anonymous feedback

24. If a tree falls in a forest and no one is
around to hear it, does it make a sound?

25. If you work hard and no one is around who
understands you, do you actually work?

27. Communication?

28. Most of you suck
(relatively)

29. What’s the main
thing here?

30. “Your analyses are fascinating, although
I don’t understand 95% of them”
My co-worker in an anonymous feedback

31. “Storytelling isn’t a soft skill;
storytelling is fundamental”
Geoff Ralston, CEO Y-Combinator

32. Most of you suck

33. When running away, I don’t need to
outrun the lion, I need to outrun you

34. Why most of you suck?
What do you usually do?
What should you do?
The biggest missed opportunity in
data visualization
Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net

35. What you usually do

36. The typical workflow

37. results = do_stuff()
visualize(results)

38. results = do_stuff()
visualize_with_diagnostics(results)

39. results = do_stuff()
visualize_with_diagnostics(
results, metrics=True
)

40. for normalization in (
'none', 'minmax', 'to_mean', ...
):
for method in (
'LR', 'SVM', 'RNN', ...
):
results = do_stuff(
normalization=normalization, method=method
)
fig = visualize_with_diagnostics(
results, metrics=True,
title=f'{normalization} {method}.'
)
fig.savefig(f'figs/{normalization}_{method}.png')

41. Where’s that
presentation?

48. What’s the main
thing here?

49. Why most of you suck?
What do you usually do?
What should you do?
The biggest missed opportunity in
data visualization
Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net

50. What should you do?

51. Reading is easier
Thinking is hard

52. Text

53. Subvocalization

54. Short

55. ‫טקסט‬
‫نص‬
⽂本

59. “What”
vs.
“So what?”

60. Data dynamics

61. Our data is highly seasonal

62. ECG

63. No signs of arrhythmia or Q-T prolongation

64. You’re OK

65. Prediction performance

67. Why writing conclusions?
• Convince
• Discipline
• Endanger

68. Subvocalization

69. I’ll tell you
what to think

70. Boris Gorelik
is the best

72. Why writing conclusions?
• Convince
• Discipline
• Endanger

73. Discipline

74. Does the graph say what I say that it says?

79. Why writing conclusions?
• Convince
• Discipline
• Endanger

80. Make claims that future observations
might show to be false
Karl Popper (not a citation)

81. Why writing conclusions?
• Convince
• Discipline
• Endanger

82. It’sFAST

83. It’sFAST

84. Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net
Why most of you suck?
What do you usually do?
What should you do?

85. Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net
Why most of you suck?
What do you usually do?
What should you do?

86. Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net
Why most of you suck?
What do you usually do?
What should you do?

87. Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net
Why most of you suck?
What do you usually do?
What should you do?

88. Boris Gorelik, Ph.D
Freelance consultant: data science & visualization | boris@gorelik.net
https://gorelik.net