looking for patterns in the churn

One of the projects I work with has very high churn in the backlog. There are repeated and frequent seismic shifts in the project’s priorities, and each time that happens a whole slew of twenty or more cards instantly appears at the top of the pile. And a couple of times the entire backlog has been junked – sorry, archived. Naturally this all has side-effects, some good and some not so good.

It’s important to note that this project is considered a roaring success. The team has achieved amazing things in a short time. And its ability to cope with the backlog churn – while maintaining velocity – is remarkable. And yet the churn is disorienting: the team’s retrospectives regularly discover that individuals feel the project is out of control, or directionless.

So the team has decided to graph the churn, by charting the average age of backlog items each week. I’m not sure what to expect or predict from the exercise – even whether or not it will be useful. For example, if the line levels off showing an average age of 4 weeks for backlog items, does that mean that the team’s SIP is 4 weeks? And if so, might publication of that “fact” itself have a dampening effect on the churn, as people realise that they only have to wait 4 weeks for any feature request?

Hopefully, time will reveal all…

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