This morning I saw a discussion on twitter about “Estimates being wrong”. This struck me as really odd. I’m a huge fan of Todd Little and Troy Magennis on the subject of estimation. They have taught me that the relationship between actual and estimate follows a log-normal ( or rather Weibel ) distribution.
When we make an estimate, it occurs at a time t based on an information set ( I0…It) with a filtration funtion F(t). There is a huge amount of uncertainty involved in the estimate. It is obviously going to be wrong. A better question is, is the estimate useful?
We estimate in order to make decisions. Do we want to do this thing? Do we want to do something else?
In Capacity Planning we use estimates to help us work out the approximate demand on a team during the quarter. We can use this information to identify which teams are constrained and this helps with creating an organisational backlog. Our process asks the Product Owner for the estimate, and NOT the engineering team. This means the engineering team cannot be held accountable for the estimate if it turns out to be WRONG. It also means the team do not try to get it RIGHT. The process allows the Product Owner to ask the engineering team if they choose, it just does not require it. The executive in charge of the process was given Todd Little’s paper to read so they understand the nature of the estimates. The goal is not that they are right, it is that they are consistent in approach. The estimates are good enough to be useful to help identify constraints and form the backlog. No one cares if they are right or wrong.
Something about this made me stop and think about Cynefin. Much of my work recently has been assisted by understanding whether the person I’m dealing with operates in a “Complicated’ world where outcomes are knowable or whether they believe the work is unknowable/emergent in a “Complex” world. I’ve noted that certain words are useful cultural markers to help you spot which. Right and Wrong indicate certainty which implies a belief in expertise. Hypothesis, Useful, Better or Worse indicate an appreciation of the uncertainty inherent in the process. People are not computers, so it is common to find someone struggling to find the appropriate words to use.
So if you hear someone referring to estimates as right or wrong, then you know they are thinking about expertise. If they focus on the usefulness of the estimate and the context, they are thinking about complexity.
Am I right or Am I wrong? And is this useful?