Michael Gantz was kind enough to comment on my previous post about Early is On Time. He put forth the notion that we should be more honest about our estimates and what they mean. For example, "This project will be done in 3 weeks with a 75% confidence" or alternately "This project will be done in 3 to 5 weeks".
I like the idea of making honest estimates and I like the idea of providing confidence levels or ranges of time. Years ago I read a book from Microsoft on project management that provided a estimation confidence guide. I don't recall all the details any more but it suggested something like, at the beginning of a project our estimates are +/- 65%, near the end we are within +/- 1%. I think thats interesting, but I think we put a lot of effort into managing that information when we don't need to. Rather we should be working together, business and technology, to find an optimal solution.
The Old Saw
Traditional agile practices (is that a thing?) tell us to use yesterdays weather to forecast the future. So if we have a know quantity of work and some data about how fast we typically burn through work, we should be able to provide a number in some reasonable range. Thats great. So now we can talk to the business about when we might deliver a quantity of work.
Imagine a World
Disregarding all the unknowns that tend to throw a project off schedule, let pretend we can forecast work with some reasonable expectation of matching our estimate. 75% of the time we hit the mark on the head, when we are late it is typically by 5% of the work. We won't bother with early.
Early is On Time and On Time is Too Late, part 2
Software development is supposed to be a collaboration. If a development team tells you that they can deliver the product in 5 weeks with a 75% confidence, it is necessary to cooperate with that estimate. That is, throwing more bodies, mandatory overtime, or free pizza at a deadline almost never changes the delivery schedule. Rather, it leads to burn out, cut corners, and tech debt.
In my time I've seen entire departments halted to re-estimate work because the business didn't like the forecast. There is a lack of trust for you. I've also seen 'Saturday Bug Hunt Parties' with all the pizza and pop you can consume in order to bring down the backlog. A fine example of exploitation leading to burn out.
We might suck at estimating while a team is still forming, but a team that is someplace in the norming arc has a pretty good idea of what they can do. If what they can do doesn't satisfy the business objective there isn't much in the way of effective tactics to solve that problem.
The Brutal Truth
Facing a missed objective there are really only three avenues left. Eliminate all obstacles to between the team and the goal, change the goal, or replace the team with more reliable/accurate resources. Most any other suggestion is a variation on these three.
Never mind that we should eliminate obstacles in the first place, this is probably our best hope for success. Given enough latitude a disciplined team can burn down a ton of work if we take everything else out of their way. But, if that isn't going to be enough, cutting scope is your next best option. Take away all non-essential functionality, all the bells and whistles, and get to market. You can always deploy again the next week (or day even).
Nobody likes to talk about replacing the team, but sometimes that is the answer. Though often times it doesn't help as much as we'd like. I'll write about that situation some other day in more detail, but keep it in the back of your mind, sometimes organization overreach or bad politics or other human issues force you into this situation.
Back on Point
In the end, business and technology are much better off when they work together to find shared belief about what they can do. That requires getting the actual doers involved in the estimation process. It also requires the people asking for the features to be reasonable and accommodating. It should be a collaboration though, and if it isn't, Michael' suggestion isn't going to help, nor would the most precise estimate in the world. Estimates, after all, are just educated guesses and prone to error.
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