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Is data visualisation bullshit?

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

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Is data visualisation bullshit?

  1. 1. Is data visualisation bullshit? Alban Gérôme MeasureCamp Brussels 21 October 2017
  2. 2. “I am always ready to learn,
  3. 3. “I am always ready to learn, … although I do not always like being taught.”
  4. 4. “I am always ready to learn, … although I do not always like being taught.” Sir Winston Churchill
  5. 5. Data visualisation
  6. 6. Data visualisation is a great solution to a problem you should not have in the first place
  7. 7. Having too much data to analyse as a result of tracking everything
  8. 8. Some complex implementations actually work
  9. 9. Some complex implementations actually work but building a complex one from scratch never works
  10. 10. Some complex implementations actually work but building a complex one from scratch never works and cannot be patched to make it work
  11. 11. You have to start over
  12. 12. You have to start over, beginning with a working simple implementation
  13. 13. You have to start over, beginning with a working simple implementation Adapted from a quote by John Gall
  14. 14. Four main risks
  15. 15. Four main risks Absorptive capacity
  16. 16. Four main risks Absorptive capacity Data literacy
  17. 17. Four main risks Absorptive capacity Data literacy Biases
  18. 18. Four main risks Absorptive capacity Data literacy Biases Spurious correlations
  19. 19. The cast
  20. 20. The cast Arnaud Web Analyst
  21. 21. The cast Arnaud Web Analyst Isabelle Published Author, Speaker and Entrepreneur
  22. 22. The cast Arnaud Web Analyst Marit Stakeholder Isabelle Published Author, Speaker and Entrepreneur
  23. 23. The cast Arnaud Web Analyst Marit Stakeholder Wim Chief Operating Officer Isabelle Published Author, Speaker and Entrepreneur
  24. 24. The network chart
  25. 25. The network chart Influences
  26. 26. The network chart Influences Reports to
  27. 27. The network chart Influences Reports to Gives credit
  28. 28. The network chart Influences Reports to Gives credit Actionable insight
  29. 29. Marit loves learning from Isabelle
  30. 30. Wim gives the credit to Marit
  31. 31. Arnaud finds actionable insight
  32. 32. Marit ignores or rejects it
  33. 33. Marit requests data extracts
  34. 34. Arnaud bypasses Marit
  35. 35. Wim thinks they are data-driven
  36. 36. Cannot Claim Credit
  37. 37. Cannot Claim Credit
  38. 38. Cannot Claim Credit
  39. 39. Cannot Claim Credit
  40. 40. By playing a game of superficial compliance, your stakeholders can continue claiming credit for ideas from outside thought-leaders whilst looking data-driven
  41. 41. 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
  42. 42. 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
  43. 43. That’s why I believe that data visualisation can get you stakeholder buy-in
  44. 44. That’s why I believe that data visualisation can get you stakeholder buy-in is bullshit
  45. 45. 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
  46. 46. 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
  47. 47. 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
  48. 48. 7 steps to no change
  49. 49. 7 steps to no change 1. IT goes for a big bang implementation, delays, budget overruns
  50. 50. 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?
  51. 51. 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
  52. 52. 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
  53. 53. 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
  54. 54. 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
  55. 55. 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
  56. 56. The C-suite must lead the data transformation by example, the stakeholders will follow
  57. 57. 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
  58. 58. 7 steps to real change
  59. 59. 7 steps to real change 1. The C-suite must lead the data transformation by example
  60. 60. 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
  61. 61. 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
  62. 62. 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
  63. 63. 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
  64. 64. 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
  65. 65. 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
  66. 66. Thank you! http://www.albangerome.com @albangerome
  67. 67. 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

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