Why your estimates don’t matter

Let’s pretend for just a second that we need estimates in order to perform our business. Some of you will say that we do and some will probably say that estimates are a big waste. But for the moment, let’s at least pretend that they have a place.

Usually we do estimation in order to provide some kind of predictability in our deliveries. It’s just that an estimate is not enough on its own. Knowing that something will take 6 man weeks to implement has no value unless we know that we have 6 man weeks at our disposal. We need to combine our estimate with some kind of capacity measure in order to get predictability. There’s a big difference if our team can give the task 6 man weeks worth of attention within the next two week iteration or if they’re overloaded with other work and need 4 calendar months to finish the requested 6 man weeks.

So we need an estimate AND a capacity in order for the estimate to have any value. The thing is that it’s not enough either. When we estimate, we also need to agree on what we’re estimating. We need to have the same view on the expected quality; both external and internal quality. Everyone involved needs to know how the solution is supposed to behave; if the customer expects a Lexus but a developer is about to build a go-cart, the estimate will have no value. Everyone involved needs to have the same view on the level of confidence for the internal quality; if the developer is ok with Windows 95 quality but the tester is expecting a pacemaker level of confidence, the estimate will have no value.

So now we need an estimate AND a capacity AND an understanding of quality in order for the estimate to have any value. The thing is that if we make an estimate and it’s wrong, the effects will fade over time (unless we’re dealing with systematic estimation errors). If a requirement was estimated to take 5 days but actually took 10 days (a 100% estimation error), the effect on a six-month project will be less than 4%. An error in capacity on the other hand will multiply if left to itself. If a team is working in two-week sprints and plans are made with a 10% error in capacity, this error will multiply for each sprint and for a six-month project, we’ll have to add another two sprints to the end in order to finish what we had initially planned. But even worse is the cost of poor quality. These costs tend to rise exponentially with time. The longer time a poor assumption or a bug goes unnoticed, the more code will get built on top of that error and either multiplying the instances of the actual error or at least building dependencies to the error.

In short:
Error in estimate – impact decreasing linearly with time
Error in capacity – impact increasing linearly with time
Error in quality – impact increasing exponentially with time

But where do people put their attention when plans fail? They usually address the estimate and way too often put blame on the teams for not doing good enough estimates. Apart from being unethical since estimates are nothing but guesses, it’s also a waste of time since any deviations from the plan are much more likely to come from errors in capacity measurements (or worse; capacity estimates) or a mismatch in the understanding of what quality (or functionality) was being estimated.

So if predictability is what you’re looking for, don’t invest much in your estimates, instead you should make sure that your capacity is known and that quality (internal as well as external) is well understood. And that’s why your estimates don’t really matter.