A few thoughts on estimating. I had a conversation with someone yesterday who asked me how I worked with the engineers on estimates. My answer shocked him, I think. I wanted to expand here on what was a throwaway conversation:
- My favorite story about estimates is about the Sydney Opera House, as told by Nicholas Nassim Taleb in The Black Swan. First, you should know that construction is incredibly well-understood and for some types of projects builders can repeatedly complete them within 5% of the estimated time.
The Sydney Opera House, started in 1959, was scheduled to be completed in 1963 for $7M (Aus). Actual construction took nearly four times the original estimate – it actually finished in 1973 (10 years late!) – and it cost more than 12 times the original budget at $104M (Aus). And of course, the Opera House was only 1/3 of the original project. If builders can be that far off, simply because it’s never been done before, why should we think that we should be able to estimate software, which always by definition has never been done before?
- There is a fundamental disconnect between estimates and interesting things. Interesting things are unpredictable. User stories are estimatable, therefore not interesting.
- Estimates are not a standard distribution. They are really screwed up distribution where the likely value is way the heck out there beyond the value you think it should be. (And very occasionally, extremely rarely, things go a lot faster than you expect.)
- I prefer timeboxes, and for interesting things, we get done what we get done in the timebox. The art of product management is figuring out what to do in the timebox. Note: this works much better in software than in construction. Buildings have to obey the laws of physics, but software doesn’t. There is no such thing as a Minimum Viable Product in construction – you can’t build a fancy roof until you build the structure to support it. But you can do that in software. There’s a lot of software out there that is essentially fancy roofs floating in the air.
- Think about failure, which is so important in innovation. Failure is of course immune to estimates, by definition.
For example, let’s assume I can get a decent estimate for doing something interesting (which we know I can’t, but hang on). Then we do it. It only takes twice as long as we estimated! (That’s a great result.) Unfortunately, given reality, it’s wrong, and has to be done again. It was a failure, but it was a productive failure. We learned a lot. We didn’t get the feature to market when we expected to, but if we’d put that version into the market, it would have been bad in oh so many ways.
So we start doing it again, and mostly we have to start from scratch, but we did learn some things in version 1. We also realize we can get a little bit of version 2 out to early adopters. It’s definitely not a full feature – they have to do manual work to get the value, but they are willing because it’s so useful to them. And we learn some stuff, and we end up building version 3, instead of version 2, because we got some great feedback that makes it even better. Versions 1 and 2 are sunk costs, and they are PAINFUL, but because we did them, we have version 3, and it’s beautiful. And it only took us four times as long to get the feature out as originally estimated, which is actually a pretty good result.
The title of this post might have been a little misleading. I suspect I may have created a firestorm. I can’t wait to hear what you think!