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# Data driven portfolio management agile2017

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Forecasting and portfolio management workshop I delivered at Agile 2017 in Orlando FL

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### Data driven portfolio management agile2017

1. 1. Data-driven Portfolio Management Concrete tools for managing products in an uncertain environment
2. 2. Me Not Allowed to Learn Math
3. 3. 1. How do you decide what product to build? 2. How do you choose which to do first? @AdamYuret
4. 4. Troy Magennis FocusedObjective.com Troy Magennis (@t_magennis) FocusedObjective.com Every spreadsheet and   exercise worksheet is here: Bit.ly/SimResources (gitHub) #LASCOT16@AdamYuret
5. 5. If you can this, you can Monte Carlo.
6. 6. Planning Poker
7. 7. 0 1 0.5 0.5 You are here You could be here Why Forecasting Using Monte Carlo? 0.85 #LASCOT16@AdamYuret
8. 8. Cut to the chase, How do I predict delivery?
9. 9. Statistics!
10. 10. Probability Refresher Sampling Getting Data Forecasting with Data Undo all of the statistics you’ve learned in school Learn how much data we need to forecast Learn how to get historical data and estimates Practice using historical data to forecast
11. 11. Finish the rest of the probability questions 3 minutes
12. 12. Probability Refresher Sampling Undo all of the statistics you learnt in school Learn how much data we need to forecast Defining Value Learn how to define value of product options Capacity Planning Learn how forecast encourages prioritization
13. 13. Sampling A way to use real data we actually have to make predictions & forecasts It helps discover the range of possible values quickly and reliably
14. 14. Q. How quickly do we discover a range of values by sampling? Why? Because as we get data (e.g. story count, story size, velocity, throughput, cycle-time) how confident should we be of having found the full range values.
15. 15. Lowest sample so far Actual Maximum Actual Minimum Highest sample so far Q. What is the chance of the 4th sample being between the range seen after 3 random samples? (hint: The numbers don't matter, only the gaps)  (no duplicates, uniform distribution) A. ?1st 2nd 3rd 4th
16. 16. Lowest sample so far Actual Maximum Actual Minimum 25% chance higher than previous highest seen 25% chance lower than previous lowest seen Highest sample so far Q. What is the chance of the 4th sample being between the range seen after 3 random samples?   (no duplicates, uniform distribution) A. ?1st 2nd 3rd 4th 25% 25%
17. 17. Lowest sample so far Actual Maximum Actual Minimum 25% chance higher than previous highest seen 25% chance lower than previous lowest seen Highest sample so far Q. What is the chance of the 4th sample being between the range seen after 3 random samples?   (no duplicates, uniform distribution) A. 50%  % = (n – 1)/(n+1)  % = (3-1)/(3+1)  % = 2/4 = 1/2  % = 0.5 1st 2nd 3rd 4th 25% 25%
18. 18. Actual Maximum Actual Minimum 8.5% chance higher than previous highest seen 8.5% chance lower than previous lowest seen Highest sample so far Lowest sample so far Q. What is the chance of the 12th sample being between the range seen after 11 random samples?   (no duplicates, uniform distribution) A. 83%  % = (n-1)/(n+1)  % = (11-1)/(11+1)  % = 0.833 1st 2 3 4 5 6 7 8 9 10 11th 12
19. 19. Prediction Intervals • “n” = number of prior samples • % chance next sample in previous range for prior sample count n (n-1)/(n+1) n (n-1)/(n+1) 2 33% 16 88% 3 50% 17 89% 4 60% 18 89% 5 67% 19 90% 6 71% 20 90% 7 75% 21 91% 8 78% 22 91% 9 80% 23 92% 10 82% 24 92% 11 83% 25 92% 12 85% 26 93% 13 86% 27 93% 14 87% 28 93% 15 88% 29 93% 30 94%
20. 20. Experiment From a *known* range of values, take samples at random and see how fast we can determine what the full range *might* be. Compare two ways – 1. From the (n-1)/(n+1) formula 2. By doubling the average (double what you are told)
21. 21. 42 7 99 Sum all rolls up to each row and divide by n 00 & 0 = 100 10 minutes
22. 22. Come to the front when completed. Compare with expected.  How close to 9 samples is range of 80 found? (80% range, 10% above?) Group # samples > range > 80 # samples until   2 x avg > 80 1 2 3 4 5 6 7 Group # samples > range > 80 # samples until   2 x avg > 80 8 9 10 11 12 13 14
23. 23. Cut to the chase, How do I predict delivery?
24. 24. http://bit.ly/ThroughputForecast
25. 25. The German Tank Problem
26. 26. The German Tank Problem96 Bogie Wheels!
27. 27. The German Tank Problem96 Bogie Wheels! 08/1942 Intelligence Estimate 1550 Statistical Estimate 327
28. 28. The German Tank Problem96 Bogie Wheels! 08/1942 Intelligence Estimate 1550 Statistical Estimate 327 ACTUAL POST-WAR NUMBER 342! Statistical Estimate = 96%
29. 29. Probability Refresher Sampling Undo all of the statistics you learnt in school Learn how much data we need to forecast Defining Value Learn how to define value of product options Capacity Planning Learn how forecast encourages prioritization
30. 30. 38
31. 31. 39 September?!
32. 32. 40
33. 33. 41
34. 34. 42 How do I make Veruca Salt Happy?
35. 35. 44 How can I move the work to make the stated goal possible? • Move some of the work to different people with available capacity • Work faster/harder?
36. 36. “Manage the work, not the worker” –W Edwards Deming Deming never said this… but he would’ve #fakeDemingQuote
37. 37. 46 NAILED IT!
38. 38. 47 Multi-feature Forecaster
39. 39. 48 Crunch Mode!
40. 40. 49 http://bit.ly/HicssTroy
41. 41. 50
42. 42. 51
43. 43. 52 How many stories? What Order? How Likely?
44. 44. 53
45. 45. 54 How Short?
46. 46. 55 Weight
47. 47. 56 Weights? Balanced Portfolio of Investments Create Revenue Protect Revenue Reduce Costs Eliminate Costs Exploratory Innovation Exploitative Innovation
48. 48. 57 First!
49. 49. Study This Guy’s Work @AdamYuret #Agile2017