Why is it so hard to get stakeholders to implement your recommendations?

1. Is data
visualisation
bullshit?
Alban Gérôme
MeasureCamp Brussels
21 October 2017

3. “I am always ready to learn,

4. “I am always ready to learn,
… although I do not always
like being taught.”

5. “I am always ready to learn,
… although I do not always
like being taught.”
Sir Winston Churchill

7. Data visualisation

8. Data visualisation is a
great solution to a
problem you should not
have in the first place

9. Having too much
data to analyse as
a result of tracking
everything

11. Some complex implementations
actually work

12. Some complex implementations
actually work but building a
complex one from scratch never
works

13. Some complex implementations
actually work but building a
complex one from scratch never
works and cannot be patched to
make it work

14. You have to start over

15. You have to start over, beginning
with a working simple
implementation

16. You have to start over, beginning
with a working simple
implementation
Adapted from a quote by John Gall

18. Four main risks

19. Four main risks
Absorptive capacity

20. Four main risks
Absorptive capacity
Data literacy

21. Four main risks
Absorptive capacity
Data literacy
Biases

22. Four main risks
Absorptive capacity
Data literacy
Biases
Spurious correlations

23. The cast

24. The cast
Arnaud
Web Analyst

25. The cast
Arnaud
Web Analyst
Isabelle
Published Author, Speaker
and Entrepreneur

26. The cast
Arnaud
Web Analyst
Marit
Stakeholder
Isabelle
Published Author, Speaker
and Entrepreneur

27. The cast
Arnaud
Web Analyst
Marit
Stakeholder
Wim
Chief Operating Officer
Isabelle
Published Author, Speaker
and Entrepreneur

28. The network chart

29. The network chart
Influences

30. The network chart
Influences
Reports to

31. The network chart
Influences
Reports to
Gives credit

32. The network chart
Influences
Reports to
Gives credit
Actionable insight

33. Marit loves learning from Isabelle

34. Wim gives the credit to Marit

35. Arnaud finds actionable insight

36. Marit ignores or rejects it

37. Marit requests data extracts

38. Arnaud bypasses Marit

39. Wim thinks they are data-driven

41. Cannot
Claim
Credit

42. Cannot
Claim
Credit

43. Cannot
Claim
Credit

44. Cannot
Claim
Credit

45. By playing a game of superficial
compliance, your stakeholders can
continue claiming credit for ideas from
outside thought-leaders whilst looking
data-driven

46. By playing a game of superficial
compliance, your stakeholders can
continue claiming credit for ideas from
outside thought-leaders whilst looking
data-driven
By implementing your insight, they
cannot claim any credit. If they did, in
the long run, the web analysts runs his
or her team

47. By playing a game of superficial
compliance, your stakeholders can
continue claiming credit for ideas from
outside thought-leaders whilst looking
data-driven
By implementing your insight, they
cannot claim any credit. If they did, in
the long run, the web analysts runs his
or her team. That’s conservatorship

48. That’s why I believe that data visualisation
can get you stakeholder buy-in

49. That’s why I believe that data visualisation
can get you stakeholder buy-in is bullshit

50. That’s why I believe that data visualisation
can get you stakeholder buy-in is bullshit
… and leveraging Daniel Kahneman’s fast
system 1 of thinking

51. That’s why I believe that data visualisation
can get you stakeholder buy-in is bullshit
… and leveraging Daniel Kahneman’s fast
system 1 of thinking
… and trying Robert Cialdini’s seven
principles of influence

52. That’s why I believe that data visualisation
can get you stakeholder buy-in is bullshit
… and leveraging Daniel Kahneman’s fast
system 1 of thinking
… and trying Robert Cialdini’s seven
principles of influence
… and Nancy Duarte’s storytelling formula

54. 7 steps to no change

55. 7 steps to no change
1. IT goes for a big bang implementation, delays, budget overruns

56. 7 steps to no change
1. IT goes for a big bang implementation, delays, budget overruns
2. First reports contain no insight, bad implementation or bad tool?

57. 7 steps to no change
1. IT goes for a big bang implementation, delays, budget overruns
2. First reports contain no insight, bad implementation or bad tool?
3. Analytics team created and under pressure to deliver quick wins

58. 7 steps to no change
1. IT goes for a big bang implementation, delays, budget overruns
2. First reports contain no insight, bad implementation or bad tool?
3. Analytics team created and under pressure to deliver quick wins
4. Insight gets ignored, IT sabotages the implementation by accident

59. 7 steps to no change
1. IT goes for a big bang implementation, delays, budget overruns
2. First reports contain no insight, bad implementation or bad tool?
3. Analytics team created and under pressure to deliver quick wins
4. Insight gets ignored, IT sabotages the implementation by accident
5. Stakeholders demand data extracts and a focus on data quality

60. 7 steps to no change
1. IT goes for a big bang implementation, delays, budget overruns
2. First reports contain no insight, bad implementation or bad tool?
3. Analytics team created and under pressure to deliver quick wins
4. Insight gets ignored, IT sabotages the implementation by accident
5. Stakeholders demand data extracts and a focus on data quality
6. Stakeholders get their ideas from external thought-leaders just like
before and cherry-pick analytics data that support their ideas

61. 7 steps to no change
1. IT goes for a big bang implementation, delays, budget overruns
2. First reports contain no insight, bad implementation or bad tool?
3. Analytics team created and under pressure to deliver quick wins
4. Insight gets ignored, IT sabotages the implementation by accident
5. Stakeholders demand data extracts and a focus on data quality
6. Stakeholders get their ideas from external thought-leaders just like
before and cherry-pick analytics data that support their ideas
7. The company looks data-driven but nothing really changed

63. The C-suite must lead the data
transformation by example, the
stakeholders will follow

64. The C-suite must lead the data
transformation by example, the
stakeholders will follow
When they are ready, the
stakeholders should get all the
credit for their data-driven insight

66. 7 steps to real change

67. 7 steps to real change
1. The C-suite must lead the data transformation by example

68. 7 steps to real change
1. The C-suite must lead the data transformation by example
2. Explain that you will not, cannot track everything

69. 7 steps to real change
1. The C-suite must lead the data transformation by example
2. Explain that you will not, cannot track everything
3. The fastest response to disruption is to start small and grow

70. 7 steps to real change
1. The C-suite must lead the data transformation by example
2. Explain that you will not, cannot track everything
3. The fastest response to disruption is to start small and grow
4. Use VOC tools and run A/B tests

71. 7 steps to real change
1. The C-suite must lead the data transformation by example
2. Explain that you will not, cannot track everything
3. The fastest response to disruption is to start small and grow
4. Use VOC tools and run A/B tests
5. Speak to the testers to include your tests in their testing suites

72. 7 steps to real change
1. The C-suite must lead the data transformation by example
2. Explain that you will not, cannot track everything
3. The fastest response to disruption is to start small and grow
4. Use VOC tools and run A/B tests
5. Speak to the testers to include your tests in their testing suites
6. Delegate all reporting and monitoring to the teams that need
analytics using a hub and spoke model

73. 7 steps to real change
1. The C-suite must lead the data transformation by example
2. Explain that you will not, cannot track everything
3. The fastest response to disruption is to start small and grow
4. Use VOC tools and run A/B tests
5. Speak to the testers to include your tests in their testing suites
6. Delegate all reporting and monitoring to the teams that need
analytics using a hub and spoke model
7. Explain the stakeholders that this is a regency, not a
conservatorship

74. Thank you!
http://www.albangerome.com
@albangerome

75. Further reading
• https://hbr.org/2013/01/why-it-fumbles-analytics
• https://hbr.org/2016/07/how-ceos-can-keep-their-
analytics-programs-from-being-a-waste-of-time
• https://hbr.org/2017/06/how-to-integrate-data-and-
analytics-into-every-part-of-your-organization
• https://assets.kpmg.com/content/dam/kpmg/xx/pdf/
2016/10/building-trust-in-analytics.pdf
• https://en.wikipedia.org/wiki/John_Gall_(author)
• https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slo
w
• https://en.wikipedia.org/wiki/Robert_Cialdini
• https://www.ted.com/talks/nancy_duarte_the_secret
_structure_of_great_talks
• https://hbr.org/2017/06/does-your-company-know-
what-to-do-with-all-its-data
• https://hbr.org/2016/08/the-reason-so-many-
analytics-efforts-fall-short
• “Cult of Analytics” by Steve Jackson for the REAN
framework and the Hub and Spoke model
• Selective Attention Test by Daniel Simmons and
Christopher Chabris:
https://www.youtube.com/watch?v=vJG698U2Mvo
• https://www.slideshare.net/Management-
Thinking/infographic-the-virtuous-circle-of-data-
43900072