Agile: Re-estimating cards
Chris Johnston has another interesting post in which he writes about the practice of re-estimating cards after they have been completed.
I think this somewhat misses the point that the estimate is indeed supposed to be an estimate. It might turn out to be too optimistic or too pessimistic, the theory being that overall we will end up with a reasonable balance that will allow us to make a prediction on how much work we believe we can complete in a certain time period.
I’ve always seen estimates of story cards to be a relative thing - on all the teams I’ve worked on after initial cards have been estimated we’ve looked at the difficulty of upcoming cards and tried to score them relative to the ones already estimated.
Re-estimating cards (presumably) individually means that you lose the benefits of this approach and end up with a fairly meaningless value.
We are also ignoring the inherent uncertainty involved in estimating if we do so afterwards and I don’t really see it helping us that much for future predictions since there is still going to be uncertainty with regards to the implementation of future cards no matter what we try to do.
If data needs to be collected after a card has been played then I would say the time it took to complete a card might be a more useful metric although there are more than likely going to be some cards that take longer than expected and some that take less than expected, so I’m not sure what extra value this data would provide.
As Chris points out if there are times, when we come to play a card or just after we start work on it, when we realise that some of our assumptions are incorrect therefore meaning that the estimate is inaccurate.
In this case I think it’s reasonable to talk through the new assumptions and re-estimate the card so that we have a more accurate estimation of its difficulty.
When trying to work out how much work we can do in a certain time box, averaging the data we have from the original estimates is likely to provide the most accurate prediction in my opinion.
Apart from times when we have story cards overflowing from one iteration to the next, meaning that we end up with one high scoring and then one low scoring iteration, I have found on my projects that the amount of work points wise doesn’t tend to vary very much which lends credence to the theory that the estimates eventually balance out.
About the author
I'm currently working on real-time user-facing analytics with Apache Pinot at StarTree. I publish short 5 minute videos showing how to solve data problems on YouTube @LearnDataWithMark. I previously worked on graph analytics at Neo4j, where I also I co-authored the O'Reilly Graph Algorithms Book with Amy Hodler.