Estimates as Sensors

Note: this is not an April Fool – honest!

I’ve been watching the #NoEstimates conversations with interest and decided its about time to pitch in with my own perspective. I don’t want to take any ‘side’ because like most things, the answer is not black or white. Estimates can be used for both good and evil. For me they are useful as a sensing mechanism. Put another way, by estimating, I can get a sense of how well I know my actual capability.

Lets take an example. I’m taking part in a 10K run this Sunday (*) and I am estimating that I will complete the distance in 55 minutes. This is based on an understanding of my capability from participating in regular 5K runs, and more general training runs over a 10K distance. I’ve never run an official 10K race, let alone this course, and I don’t know what the conditions will be like, but I’m aiming for 55 minutes. If I run quicker and do better than my estimate, then my actual 10K capability is better than I thought. If I run slower and do worse than my estimate, then my actual 10K capability is worse than I thought. Either way,  I will learn something about how well I know my 10K capability.

What helps even more with that learning is regular feedback! I use MayMyRun on my phone to track  progress and give me feedback every kilometre for total time, average pace and last split. This could be considered a distance-based cadence. I could probably also use a time-based cadence to give me equivalent feedback every few minutes. This feedback on a regular cadence helps me decided whether my estimate was way off, or if I should slow down because my pace is probably unsustainable, or speed up because I feel I can push harder.

How does this relate to product development? Well, we can use estimates in the same way to get a sense of a teams delivery capability, and use regular feedback to learn whether we’re making the progress we thought, and need to re-plan, slow down or speed up. Time-boxing, with Story Point estimates and Velocity can provide this time-based cadence and feedback. Alternatively, how long it takes to complete 10 User Stories can be used as a distance-based cadence and feedback.

To sum up, I recommend using estimates to sense capability and create feedback for yourself. I don’t recommend using them to make promises and give guarantees to others. Or maybe we could just call them sensors instead of estimates?

(*) Of course this post is primarily an excuse to invite readers to sponsor me. If you’re so inclined, or would like to show support for this post in a charitable and financial way, then please head over to my JustGiving page and do so there 🙂

Update: My final time was 49:23 based on the timing chip, or 49:30 from the starting gun. I’ve learned that I’m better than I thought I was, and next time I’ll be estimating 50 minutes!