Agile2014

Metrics help forecast and manage Agile projects and teams. Used properly, simple statistical techniques add insight and encourage better actions to be taken. Velocity is the general go to metric, but how well it forecasts the future is the elephant in the Agile community's room. This session talks about how to capture metrics (other than velocity), how to identify which metrics cause the most impact when changed (carry the most information), and how to use metrics to probabilistically (statistically) forecast a future. We briefly discuss #NoEstimates, and this session touches on the need to move beyond story point estimation in order to improve analytically.



This session will give attendees practical techniques to apply on their own teams and projects, and confidence to talk about forecasts and uncertainty to others. It debunks the common myths that you need lots of data, a Phd in Mathematics and that you need historical data to begin with.



Topics explored include



* Understanding Uncertainty: example problems to help understand how poorly humans assess uncertainty

* Common Errors Traps: why you need to forget standard deviation, and common data mistakes in our industry

* Capturing Data: how to capture data cleanly and prepare it for analysis

* Statistical Forecasting: how we use the data we capture to predict and assess

* Sensitivity Testing: finding what matters most, and why its not story point estimates!

* Waterfall, Scrum and Lean/Kanban Metric Profiles: history of project processes and how they impact forecast-ability and uncertainty (the dreaded cone of uncertainty debunked)



Showing data from over forty teams, this session will show how common patterns (Scrum versus Lean, Operations versus Development, Waterfall versus Scrum) are evident based on team structure and style, and how understanding these assumptions can make managing teams more productive.



Why Software Moneyball? I call a quantitative approach to software project management, “Software Moneyball.” The book, “Moneyball” describes how the Oakland A’s baseball team used analytics to beat better funded competitors ($40M versus $120M), and transformed the business of baseball. If you compete against better funded rivals, you must outsmart them to succeed and better use of analytics is often the sharpest tool in the toolbox. If baseball Managers can do it, so can you.