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A quick trip to the future land of no estimates

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Why do we estimate? What are the benefits we want to obtain with that practice? In this talk we'll explore the nature of estimates and offer an alternative: #NoEstimates. We'll look at some examples of how we can predict a release date of a project without any estimates, only relying on easily available data. Finally, we'll see how we can follow progress on a project at all times without having to rely on guesswork, and we will review how large, very large as well as small projects have already benefited from this in the past. At the end of the session you will be ready to start your own #NoEstimates journey, the next step in the #Agile journey.

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A quick trip to the future land of no estimates

  1. 1. A quick trip to the future Vasco Duarte @duarte_vasco
  2. 2. #NoEstimates: how you can predict the release date of your project without estimating
  3. 3. I am proud to be part of this community!
  4. 4. Kent Beck – Extreme Programming
  5. 5. Ken Schwaber - Scrum
  6. 6. Taiichi Ohno – Toyota Production System
  7. 7. Edwards W. Deming – Everything above...
  8. 8. “If I have seen further it is by standing on the shoulders of giants” - Isaac Newton
  9. 9. #NoEstimates Just Google it
  10. 10. Customer Collaboration over Contract Negotiation Responding to Change over Following a Plan
  11. 11. #NoEstimates is easy!
  12. 12. 1.Select the most important piece of work you need to do 2.Break that work down into risk- neutral chunks of work 3.Develop each piece of work 4.Iterate and refactor #NoEstimates How-to
  13. 13. Is the system of development stable? (ref: SPC)
  14. 14. I AM GOING TO GO AHEAD AND ASK YOU TO DELIVER 10 STORIES NEXT SPRINT...
  15. 15. 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 velocity Average= LCL UCL Target Actual, measured throughput over 21 sprints
  16. 16. WTF!!!!! !#%&!
  17. 17. Can we use the data we observe to predict the system throughput and detect changes that affect system stability?
  18. 18. 1.Velocity outside limits 3 times in a row (“outside limits”) 2.There are 5 or more points in sequence (“run test”) System stability rules
  19. 19. 0 2 4 6 8 10 12 1 3 5 7 9 11 13 15 17 19 21 # of items/stories delivered LCL UCL average Team: AT 0 2 4 6 8 10 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 LCL UCL average # of items/stories delivered Team: RF 0 2 4 6 8 10 12 14 16 18 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 LCL UCL average # of items/stories delivered Team: RF2 0 5 10 15 20 25 1 3 5 7 9 11 13 15 17 19 21 # of items/stories delivered LCL UCL average Team: SH
  20. 20. -1 1 3 5 7 9 11 13 15 17 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 LCL UCL average # of items/stories delivered Team: K 0 2 4 6 8 10 12 14 16 1 2 3 4 5 6 7 8 # of items/stories delivered LCL UCL average Team: MPC 0 2 4 6 8 10 12 14 16 1 2 3 4 5 6 7 8 9 1011121314151617181920 # of items/stories delivered LCL UCL average Team: AS 0 5 10 15 20 25 30 1 2 3 4 5 6 7 8 9 10 # of items/stories delivered LCL UCL average Team: FC
  21. 21. #NoEstimates delivers!
  22. 22. Counting Stories vs. Estimated Story Points Q: Which ”metric” is more accurate when compared to what actually happened in the project?
  23. 23. A long project 24Sprints
  24. 24. Which metric predicted most accurately the output of the whole project? a) After only the first 3 Sprints b) After only the first 5 Sprints
  25. 25. Disclaimer... This is only one project! Find 21 more at: http://bit.ly/NoEstimatesProjectsDB
  26. 26. After just 3 sprints # of Stories predictive powerStory Points predictive power The true output: 349,5 SPs completed The predicted output: 418 SPs completed +20% The true output: 228 Stories The predicted output: 220 Stories -4%!
  27. 27. After just 5 sprints # of Stories predictive powerStory Points predictive power The true output: 349,5 SPs completed The predicted output: 396 SPs completed +13% The true output: 228 Stories The predicted output: 220 Stories -4%!
  28. 28. Q: Which ”metric” is more accurate when compared to what actually happened in the project?
  29. 29. 80% Late or Failed Source: Software Estimation by Steve McConnell
  30. 30. The larger the project, the bigger the problem Source: Software Estimation by Steve McConnell
  31. 31. Source: Software Estimation by Steve McConnell
  32. 32. #ofprojects More lateEarly Likelihood of pa projecty being on time On Time
  33. 33. Comparison of 17 projects ending between 2001 and 2003. (Average: 62%)
  34. 34. Take #NoEstimates and experiment! Learn, Be Agile!
  35. 35. Click here! Sign-up and get the paper today! Sign-up and receive this paper which explains why we need #NoEstimates and how to get started! Includes: • Why estimates should not be used, and how they fail • An example of how #NoEstimates can reach a 4% accuracy to actuals • How to apply #NoEstimates: Vasco’s recipe!

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