Episode 75 - Team Size
Nisha, Dan, and Prateek discuss the dogma that agile teams have to be small. They discuss their own experiences of working on large teams that were very effective, efficient, and predictable.

Nisha, Dan, and Prateek discuss the dogma that agile teams have to be small. They discuss their own experiences of working on large teams that were very effective, efficient, and predictable.
Dan and Prateek stick to the same whiskey and the same topic from the previous episode - Timeboxes. This time though we go deeper into the risk and complexity associated with them. Particularly in #Agile environments. #Nisha is as active as ever, on this latest episode of #DrunkAgile.
#Nisha, Dan, and Prateek are back after a bit of a break. The latest episode of #DrunkAgile is brought to you by the word 'Arbitrary'. From the OED: 'arbitrary, adj. & n. - Derived from mere opinion or preference; not based on the nature of things'
Does variability directly imply that your data is distributed in some manner? How important is it to figure out which distribution your data follows? Are you even analyzing your data if you are not using a cubic spline? Nisha, Dan, and Prateek discuss.
Does your team get done with work early? Or does your team get done with work a few days late? Are these problems to be addressed? Is it really a problem that your #scrum team got done with the #Sprint goal on day 10 of a 14-day sprint? What if they look like they will get done on day 16? Do you know what are the 'Two Mistakes' according to Shewhart? #Nisha, Daniel Vacanti and I talk about these in this week's episode of #DrunkAgile.
Episode 70 of #DrunkAgile is a start of a 3-part(at least) series on Variability. #Nisha, Dan, and Prateek start off with a high-level introduction to what the word variability means in our context.
In episode 69 of #DrunkAgile, #Nisha, Dan, and Prateek talk through a replacement for INVEST as work-item splitting criteria. Re-emphasizing our preference for right-sizing and feedback over value and estimation.
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?
Nisha, Dan, and Prateek talk through the effect weekends have on cycle time. Why they are included and what would be the effect if we did exclude them? Note : This episode includes the use of visuals and might be better experienced on Youtube.
Nisha, Dan, and Prateek talk through the applicability of Flow Metrics in Scrum. Also, they are 'Flow Metrics', not 'Kanban Metrics'. ProKanabn.Org's new AFMS course covers this as well.
In this episode, Dan and Prateek inspect the results of Monte-Carlo forecasts for when in the season would LeBron James break Kareem Abdul-Jabbar's record. Nisha does her best airbud impression.
The last episode in the Flaw of Averages series. Nisha, Dan, and Prateek answer a distillation of multiple audience questions - What is the problem with single-point answers?
The Flaw of Averages Part II. In this sequel, Dan and Prateek take on data distributions and shapes. #Nisha calls out the bull in Weibull. It is normal for your data to not be normal.
Nisha, Dan, and Prateek discuss Dr. Sam Savage's Flaw of Averages in this episode of #DrunkAgile. Have you been thinking of the world in terms of averages? If yes, hopefully, this episode will cure that ailment.
ProKanban.org hosts Nisha, Dan, and Prateek for a live edition of Drunk Agile. In this Holiday Special, the Drunk Agile crew takes on a variety of questions from the audience.
Nisha, Dan, and Prateek cover an audience question - I have a new feature, not yet started, when will that feature be completed? Come find out the answer.
Everything you have been told about Scaling is wrong! Nisha, Dan, and Prateek cover what is so different about Applying Scaled Portfolio Kanban.
#Nisha, Dan, and Prateek talk about being Agile when implementing Agile. They discuss the pitfalls of Maturity Models and more...
Nisha, Dan, and Prateek are back with some whisky and revisiting thoughts around Ex-Ante vs Ex-Post in order to answer an audience question looking for "guidance on finding the sweet spot to achieve meaningful metrics without too much waste in the form of ex-Ante Analysis."
Should #Kanban boards have a "Ready" column? If they do, what's the difference between that column and a "Backlog"? Dan, Prateek, and #Nisha discuss this on this week's episode. A special thanks to Andy for the question!
Dan and Prateek (with a Nisha slideshow) take the stage at Lean Agile London to answer questions from a live audience.
Is each pull request a separate work item? How do Dan, Prateek and Nisha suggest we correlate the two? Hint - It is all about your flow policies.
Nisha, Dan and Prateek talk about observations of flow outside of product development. They discuss how flow management policies have a great effect on the performance of any kind of process.
Nisha, Dan and Prateek discuss the merits and drawbacks of Product Backlogs. Can a team or and organization actually be successful and achieve results without a Product Backlog?
Dan and Prateek answer an audience question - Is Kanban Better Suited For Mature Agile Teams? Nisha helps as usual.
James Scrimshire joins Nisha, Dan and Prateek to discuss the problems with trying to follow a big scaling recipe. Are you seeing problems with one of the popular scaling frameworks? You are not alone. Listen in to find out what might be causing those issues.