Applying Metrics for Predictability

“When will it be done?” “How many items will I get in the next release?” “When will all of the items in the backlog be completed?” Those are some of the first questions that your customers will ask you once you start work for them.

This hands-on workshop will provide you with the tools you need to answer those questions predictably. In this tutorial, attendees will learn what metrics are necessary for accurate forecasting, how to visualize those metrics in appropriate analytics, how to use those analytics to make reliable forecasts and understand risk, and, finally, how to make meaningful interventions for overall process improvement.

Course Details

Make meaningful data driven decisions, improve your ability to deliver value, and be more effective. After taking the course, you will have an opportunity to validate your understanding of Metrics for Predictability with our online assessment that evaluates your knowledge and provides guidance on areas to improve. Upon passing, you will receive the “Professional Applied Metrics” (PAM) certification and badge through Credly.


Understanding of what metrics are required for predictability

Ability to make accurate forecasting for single items

Ability to make accurate forecasts for multiple items

Ability to communicate probability and delivery risk

Understanding of how to achieve a stable process and why it matters


Flow Metrics: a deep dive into WIP, Cycle Time, and Throughput

Flow Analytics: Cumulative Flow Diagrams (CFDs), Scatterplots, and Monte Carlo

Quantifying Risk and Risk Management:  how to quantify risk and develop an overall risk profile

How to Get Started:  what data to collect, how to mine your data, and how much data you need to begin


Anyone who has been asked to give an estimate for a User Story, Epic, Feature, Project, and/or Release

Executives, managers, or team members who want better understanding and transparency into the health and performance of their process

Anyone who currently uses Agile or Lean Methodologies and is interested in how to improve the overall predictability and efficiency of their current practices

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Applying Metrics for Predictability