Please note: This tutorial may be booked independently of LKCE conference attendance.

Lean Forecasting and Modeling: Metric Capture, Metric Analysis and Reliable Forecasting Lean Projects

With Troy Magennis on nov. 13th, 2014

This one day tutorial deeply discusses how to capture, check, and forecasting using metrics captured or estimated in Lean software projects.

Attendees will leave this tutorial with forecasting tools and techniques that can be used to improve the reliability of forecasting without requiring detailed (or any) up-front developer estimates. It will also discuss when you may need estimates, and if so, which ones matter most. Without requiring heavy mathematic, this tutorial aims to develop the attendees ability to understand how probabilistic thinking can be applied to making informed decisions about their projects.   


This seminar is suited to you if you -

  • Struggle to know what metrics are useful and which ones are misleading
  • Suffer from data quality through poor capture and gamed values
  • Find analyzing and presenting data to get action is harder than it ought to be

Target Audience -

  • Executives or mangers wanting a better understanding of Agile metrics and analysis
  • People responsible for Agile project planning and reporting
  • People interesting in expanding their knowledge on Agile metrics and analytics who are using Scrum, Lean, Kanban or ScrumBan as their IT process

Topic areas -

  • Metric Selection - picking the right metrics that add value and avoid gaming or poor performance
  • Metric Capture - identifying errors and cleaning noisy or gamed data
  • Metric Analysis - probabilistic forecasting and interpreting variation in data that is significant
  • Metric Presentation - presenting data to get action and avoiding common presentation mistakes
  • Making Analytic Decisions / Q & A - wrap-up combining all the practices into a useful management program


Act 1

Metric Capture

- Capturing Data Correctly

- Cleaning Data from Errors and Bias

- How Much Data is Needed

- Capturing Data Context

- Analyzing Data Integrity (error checking)

Act 2

Metric Analysis and Forecasting

- Analyzing and Interpreting Variability

- Forecasting and Prediction

 - Regression and Linear Techniques

 - Monte Carlo and Probabilistic Techniques

- Limits of Certainty - knowing what you may not know

- Sensitivity Analysis - why we really model

- Tracking progress and testing forecast accuracy

Act 3

Metric Presentation

- Presenting accurate and compelling data to get action

- Lying with Statistics and knowing when others have

- Comparing Data Across Teams Safely
- Presenting uncertainty in results without apology

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