What is the story with agile data keynote agile 2018 (Magennis)
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This document discusses using data to improve agile practices and outcomes. It argues that agile has lost the "data war" by not capturing and utilizing data from teams effectively. It suggests that data needs to be handled safely to avoid embarrassing people and destroying the utility of historical data. Better ways are needed to measure outcomes rather than just output, and to balance predictability with creativity. The document also discusses visualizing and managing dependencies, comparing performance across teams, and using the right metrics depending on a team's characteristics and challenges. The overarching message is that data needs to be used carefully and conversationally to drive the right actions and improve agile practices.
What is the story with agile data keynote agile 2018 (Magennis)
- 1. What’s the Story About Agile Data How Agile lost the data war, and what we need to do about it @t_magennis or troy.magennis@focusedobjective.com
- 2. Dirty Data Done Dirt Cheap My Album Nah, But I am from Australia like AC/DC
- 3. Using Data Data TROY MAGENNIS TROY’S MOM My Book Nah, but I did work on Navy electronics equipment Apologies to: L. David Marquet
- 4. Make Data Awesome Use Data Safely Chart Data Continuously Experiment Using Data DATA My Circular Quadrant Nah, but I am a consultant Apologies to: Joshua Kerievsky
- 5. I’m really just known for SPREADSHEETS
- 6. Let’s start with something we all agree about #NoEstimates
- 8. Data is beautiful Data is a people problem Price we pay for Predictability How we got Forecasting wrong Story points vs Throughput throwdown Devastating Impact of Dependencies Teams could/should be larger
- 9. Attribution: Emeldil at English Wikipedia https://commons.wikimedia.org/wiki/File:Campfire_Pinecone.png Boring -> Beautiful -> Action
- 11. Windy.com
- 12. Hans Rosling - 27 July 1948 – 7 February 2017
- 13. NOTE: Replace with embedded video in case of network issues
- 14. Having the data isn’t enough You have to tell the story that leads to the right action Agile Community Call to Action
- 16. “We learn from history, that we do not learn from history.” Friedrich Hegel
- 17. @t_magennis
- 18. @t_magennis
- 19. Use experience alone and we might be wrong due to cognitive bias Use historical data alone and we might be wrong due to context
- 21. Fastest way to get crappy data is to embarrass people
- 23. Fastest way to destroy the utility of historical data is to make people unsafe
- 24. 87% teams could be performing better 75% people feel ignored at times
- 25. Agile Community Call to Action We need to find safer ways to capture and utilize data from teams and organizations. safer = better data and happier people
- 26. We pay too high a price for “predictability”
- 27. Where is our “analytical” effort spent? Now Goal “Which one moves the needle most” Status / In-progress Selection / Prioritization Customer Validation (Impact) Lots Little
- 28. Why are “we” last to know? Get status more often Insist on More planning Cherry pick senior staff Yell during meetings ME
- 29. “You can’t do anything with status except worry” Tony Grout
- 30. “Watermelon Status – Green on the outside, Red on the inside” Blockbuster extinction
- 32. https://pxhere.com/en/photo/1105723 …the price we pay for the potential of true novelty and creativity is uncertainty. — Alicia Juarrero
- 33. 2x to create for 10x customer value (potentially) Would you trade…
- 34. Agile Community Call to Action We need to find better ways to measure outcome rather than output We need to balance our predictability expectations with creativity and novelty
- 35. Uncertainty New information New information New information Done Start
- 36. Agile Community Call to Action We need to incorporate New Information faster Deployment ENDS speculation (do this more often)
- 37. Yeah, but how long will THIS feature take?
- 38. Multiple options Duration, not ETA Until you start, then continuously updated with Time Remaining
- 39. Contrast Software Planning to Google Maps If you currently… • Give one forecast even though multiple approaches considered • Give a calendar date for undefined “complete” & “start” • If the original date is in doubt we find out near the end Consider doing… • Give multiple options of investment and implementation • Give a duration and define what started & complete means • If the original date is in doubt, know earlier and react faster
- 40. Forecasting is about knowing when to START #1 Reason we miss deadlines We START too late
- 41. @t_magennis You don't do forecasts and estimates to know you are right; you do forecasts and estimates to detect you are wrong
- 46. https://pxhere.com/en/photo/775063 13 points 1 point Impediment / dependency Impediment / dependency
- 47. https://pxhere.com/en/photo/775063 13 points 1 point Impediment / dependency Impediment / dependency
- 49. Story Points or Item Count Forecast Better? Story Points Work Better… • Development Time > Delays • Few Dependencies • Flexible capacity / Dedicated staff Item Count Works Better… • Delays > Development Time • Many Dependencies • Constrained capacity / Shared staff Why? • Development time dominates the lead time to release • Error introduced by assuming all similar sized items can be huge Why? • Delay time(s) dominates the lead time to release • Error introduced by story point size estimates can be huge
- 50. Experiment Hide last “months” data See if you can predict where you are now
- 51. Yeah, but How much data do I need?
- 52. 0% 20% 40% 60% 80% 100% 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 What is the chance the next sample tells me something new about the actual data? At 10 samples, 18% (or 9% for “worse”) At 20 samples, 10% (or 5% for “worse”)
- 53. Never marry before 7 Marry the best of the first 11
- 54. When forecasting using historical velocity or throughput data Use the most recent 10 samples and delete the rest Stale data erodes context
- 55. Dep ende ncies
- 56. ON-TIME DELAYED 1 in 16 Joe Joy Jack Janet 15 in 16 (Seated on-time) (Seated late)
- 57. 1 2 3 4 5 6 7 Team Dependency Diagram @greening
- 58. Chances at least one team not delayed 1 in 2n or 1 in 27 or 1 in 128
- 59. 7 dependencies 1 chance in 128
- 60. 61 6 dependencies 1 chance in 64 @t_magennis
- 61. 5 dependencies 1 chance in 32
- 65. Agile Community Call to Action We need to visualize and manage dependencies We need to minimize dependencies We need to talk about tactical larger teams
- 66. OK, Brace Yourself We NEED to compare teams Common Cause vs Special Cause Everyone vs Just Us
- 67. 0 200 400 600 800 1000 1200 1400 1600 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 103 106 109 112 115 118 121 124 127 Series1 Throughput per week for 100 teams Oops Oops
- 68. All Teams Trend Our Teams Trend
- 70. Thank you…. Stay sandy San Diego……. @t_magennis Troy.Magennis@focusedobjective.com Bit.ly/SimReources