Is data visualisation bullshit?
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The document discusses some of the risks and challenges of data visualization and analytics programs in organizations. It argues that while complex data visualizations can work, they are difficult to implement successfully from scratch. Additionally, stakeholders may claim the benefits from outside ideas while only superficially complying with analytics recommendations. The document provides steps for organizations to truly realize change through data-driven insights, such as having leadership buy-in and starting with small, test-based implementations.
Is data visualisation bullshit?
- 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
- 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
- 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
- 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
- 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
- 39. Wim thinks they are data-driven
- 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