Drunk Agile

The hosts discuss the concept of signal versus noise in a scrum team, the arbitrariness of timeboxing in Agile and Scrum methodologies, the importance of aligning customer and process voices, and the difference between service level expectation and service level agreement.

The hosts discuss the dangers of relying on normal distributions and statistical shortcuts in data analysis. They emphasize the importance of understanding variability without assuming any specific distribution. They also explore the relationship between cubic spline and common agile concepts.

Exploring the nature of variation in processes and measurements. Distinguishing between special and common cause variation. Addressing misconceptions about early completion in a scrum team. Discussing the impact of variability on sprint length and the benefits of a flow perspective. Recapping the two mistakes related to variability and expressing uncertainty about the next episode.

A 3-part series on variability is introduced in Episode 70 of Drunk Agile. The hosts discuss the importance of variation in data and its relationship to flow. They explore the impact of variation on measurements, estimation, and task management in knowledge work. The concept of acceptable levels of variability is mentioned, as well as the existence of two mistakes related to variability. The hosts promise to delve deeper into the topic in future episodes.

The podcast discusses a replacement for INVEST in agile work-item splitting criteria, emphasizing the preference for the FIRST acronym. They explore the importance of right sizing and challenges with estimation, and delve into the characteristics of 'first' in Agile. They also discuss right sizing, challenging Agile myths, and compare the 'invest' and 'first' approaches in project development.

Nisha, Dan, and Prateek answer an audience question about how to deal with blockers that are outside the team's control. They take a little detour into Little's Law as well.

What do WIP Limits have to do with a team's capacity... turns out, everything. #Nisha, Dan, and Prateek dive deeper into this question in the latest #DrunkAgile episode. Enjoy!

You asked for Blocker policies and #Nisha delivered. In this week's #DrunkAgile episode, Dan, and Prateek discuss how to set some policies around dealing with blocked work.

Should you collect Blocker data? If yes, how do you use it? If not, what is an alternative? #Nisha, Dan, and Prateek discuss this in the third episode recorded on the same night. #DrunkAgile tending towards #PlasteredAgile.

Nisha, Dan, and Prateek answer an audience question about #MonteCarlo and the predicted date ending on a weekend. What does this mean in terms of #risk? Does the risk change based on how far out the forecast is?