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Conal Scanlon on Monte Carlo Mapping

| Podcast with Conal Scanlon Follow 0 Followers by Shane Hastie Follow 28 Followers on Dec 19, 2017 |

In this podcast Shane Hastie, Lead Editor for Culture & Methods, spoke to Conal Scanlon about his talk at QCon New York on Monte Carlo Simulation for forecasting feature development.

Key Takeaways

  • Knowledge work is inherently variable, and estimates are inevitably incorrect
  • Monte Carlo simulation projects likely completion based on past history rather than future guesses
  • A small set of real data points is extrapolated to 1000 samples and that is used to produce a probability curve
  • A forecast is a point in time situation – as teams get better at delivery their predictability should improve
  • Everything in the delivery process should be subject to change as it is continuously improved
  • 0:30 Introductions
  • 1:00 The product manager role as an interface between engineering and customers
  • 1:15 Knowledge work is inherently variable, and estimates are inevitably incorrect
  • 1:45 Monte Carlo simulation projects likely completion based on past history rather than future guesses
  • 2:05 The five steps of Monte Carlo simulation
  • 2:12 Gather past data – Cycle time for a feature
  • 2:33 Extrapolating from a small set of real data into 1000 different samples
  • 2:57 The statistical technique of bootstrapping
  • 3:20 Once the sample set is available use the size of the backlog to aggregate how long individual items will take to complete
  • 3:37 Using medians and confidence levels to forecast likely completion times
  • 4:00 How this differs from just counting the number of items in the backlog
  • 4:15 Building in the variability that is inherent in knowledge work
  • 4:45 Developer estimates do not corelate well to reality
  • 5:44 Explaining how the small set of real data is extrapolated to 1000 samples and how that produces a smoother probability curve
  • 7:25 This doesn’t need a statistician to accomplish the maths – it’s relatively straightforward 
  • 7:40 Explaining how to accomplish this on your own data
  • 8:05 The need for clarity about what is being forecasted – what is your definition of cycle time
  • 9:14 Using cumulative probability distribution charts to convey the uncertainty in a forecast to stakeholders
  • 9:54 Using the chart to identify dates you are comfortable publishing internally and externally
  • 10:52 This approach overcomes the resistance and fear of giving estimates as it is forecasting based on trends using historic data
  • 11:08 Using story mapping to identify the relative complexity of features
  • 11:42 Describing the story mapping technique and how conveys the complexity of a feature
  • 12:48 The need to educate and socialise the approach and that the numbers are probabilities and forecasts not guaranteed delivery dates
  • 13:20 Ideas from the book How to Measure Everything
  • 13:40 Techniques to convey uncertainty and the fallibility of estimation
  • 14:55 Using the forecasts and real data to examine the effectiveness of your development process and the underlying assumptions that are being made
  • 15:25 The Product Manager is responsible for the clarity of external communication from the team about the forecasts
  • 16:05 A forecast is a point in time situation – as teams get better at delivery their predictability should improve
  • 16:35 Everything in the delivery process should be subject to change as it is continuously improved

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About QCon

QCon is a practitioner-driven conference designed for technical team leads, architects, and project managers who influence software innovation in their teams. QCon takes place 7 times per year in London, New York, San Francisco, Sao Paolo, Beijing & Shanghai. QCon London is at its 13th Edition and will take place Mar 4-6, 2019. 140+ expert practitioner speakers, 1500+ attendees and 18 tracks will cover topics driving the evolution of software development today. Visit qconlondon.com to get more details.

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