In part two of this series, we’re going to look at how the must-have priority bucket leads engineering to make bad decisions.
In part one we:
- looked at what bucketed prioritisation is
- defined “must-haves” as the first, and highest-priority, bucket
- divided people’s roles into the rough categories of “builders” and “specifiers”
- demonstrated how the must-have bucket creates perverse incentives for specifiers
A Tale Of Builders And Buckets
Imagine you’re an engineer working in a bucketed priority system. Let’s say that the specifiers have gone and done specified, and produced a set of requirements divided into three buckets: must-haves, nice-to-haves, and things to consider doing in the future. Now it’s time to start building.
Where are you going to start? You’re going to start by looking at the must-haves, and ignoring everything else. This is prioritisation working as intended. So far, so good.
There are a bunch of different things in the must-have bucket, so how do you decided which one to start with? In a system of bucketed prioritisation, the priority of a requirement is determined by the bucket that it sits in. Since all the requirements you’re looking at are in the same bucket, they are all equal in terms of priority, and so therefore the order in which they are built is irrelevant. These are the non-negotiables, right? They all must get done within the given time frame, so what difference does ordering make? Through the office window you see a raven cawing as if it’s laughing at you. In its nest there are several expensive-looking keycaps, and you could have sworn that it was sporting some kind of neck beard.
One of the cards says we should consider using AI for part of it. That sounds like it might be an interesting learning experience. In another card, the designer has included a Figma mock-up with some cool animations. That might be fun to implement. Some cards look boring, or difficult, or both. Your colleague just picked up the animation card. Dang. Better grab the AI card before someone else does.
So you start investigating how you’re going to do the AI, make a proof of concept implementation using a new AWS service, and then start building it into the project properly. After a few cycles it’s done, but it’s not really useable yet because it depends on parts of the project that haven’t been built. You head back to the list of must-haves, and find another interesting card to get started on.
It’s been a few months now, and the specifiers have started becoming antsy in meetings. They are getting worried that the project might not be delivered on time, and say that they’re not seeing much progress. That’s annoying. You’ve been working hard, and you know all the other builders have been working hard too. You point out all the hard work that went into the AI. The specifiers ask where they can see that feature. You tell them that you gave a demo about it two weeks ago. They say they liked the demo, but ask how they can actually use it in the app. You show them the screen, but say that they’re not going to be able to use it properly until a couple of other cards are finished. They ask when those other cards are going to be finished. You say that someone will pick it up soon, and that everyone is working as fast as possible, focusing only on the must-haves.
The specifiers say that they want more confidence around delivery, and suggest that builders take some time to put accurate estimates on each of the remaining cards. You ask why more must-have cards are being added every week if we’re worried about delivery. You’re told that those cards are important, and that they can’t be nice-to-haves because it doesn’t look like there will be any time left for nice-to-haves. You say that if there’s no time to do a card, labelling it a must-have isn’t going to magically create more time for it. They say the must-haves must get done, and that’s why they’re called “must-haves”. The raven is back cawing outside the window — it’s neck beard more prominent than ever.
As the deadline draws near, it has become crystal clear that the must-haves are not going to be done on time. After many hours of estimation meetings, the numbers just don’t add up, and even if they did, a significant proportion of the cards are taking longer than estimated. The builders get criticised for giving inaccurate estimates, so now they add lots of padding to each estimate, which makes the roadmap (a.k.a. the fancy Gantt chart) so incredibly long that the specifiers start updating their resumes. Even with the padding, a couple of cards have unearthed major technical obstacles that only became apparent after they were started recently, and these threaten to blow up the already-disastrous schedule.
The specifiers enter the third stage of grief: bargaining. They are asking you whether you think you could work really hard and meet the deadline if they removed a bunch of the must-have cards. It turns out that the non-negotiables are, in fact, negotiable after all. The specifiers make a new bucket — the must-have must-haves — which contains only the must-haves that are really really important.
But now you’re indignant. You don’t want to cooperate with the specifiers. They told you that the cards were must-haves, and you trusted them, but when you worked on those cards they turned around and said you’ve been wasting time on low-priority work. Someone even said that the AI feature you implemented doesn’t work well, and used it as an example of builders wasting time. They’ve been dragging you away from doing actual work and into pointless estimation meetings, pressuring you to give over-optimistic estimates, and then criticising you for being slow and not estimating accurately. The entire team is demoralised because, even though nobody will say it out loud, they all know that the deadline is impossible, and yet they’re still expected to put in overtime indefinitely. This project is a death march.
The project runs over schedule by a factor of two. The end product is a steaming pile of shit, and it’s questionable whether it’s even fit for purpose. The builders and the specifiers both place the blame on the other’s incompetence. The people with the highest employability — usually the ones with the strongest skill sets — quit the company. Maybe one or two of the specifiers get fired, too.
You morph into a raven, peck the keycaps off your mechanical keyboard, and fly off into the sunset to be one with nature.
Whose Fault Is It Anyway?
Plenty of things went wrong in the story above. The specifiers made some bad decisions, but I think that the responsibility for this situation mostly lands on the builders — specifically engineering management.
The typical specifier does not understand the technical details and technical risks involved in building software. That’s not their job. And if they did understand, engineers would become easily replaceable and their salaries would fall through the floor. For example, part of the job of marketing and sales people is to understand what the customers want, so they are able to provide great input when deciding what to build. It is not, however, part of their job to know how to build those things, which is why it’s not uncommon for them to come up with ideas that are wildly expensive or literally impossible. And that’s fine. Not every idea is feasible.
It’s also often not specifiers’ job to understand delivery risk, although it is highly desirable. Product people often don’t have a good sense for whether building any given feature will be easy peasy lemon squeezy, or stressed depressed lemon zest. That’s because there are knowns and unknowns that are deeply technical in nature, and only the people actually building the software have a hope of identifying where the landmines are. Product people with deep technical knowledge are incredible assets, but also exceedingly hard to find and probably outside of your price range.
So then who is actually responsible for the delivery of quality, working software? Whose job is it to deal with these deeply technical unknowns and the risks they pose? The most practical choice is engineering. Engineering is in the best position to understand and mitigate delivery risk — less so the individual engineers writing the code, and more so engineering management/leadership. Engineering management is the interface between the specifiers and the builders, and it is their job to ensure that delivery happens smoothly.
This is why I believe that non-technical engineering managers are generally a bad idea — but that’s a topic for another time.
How Buckets Are Involved
The main issue with prioritisation by bucket is that each individual bucket is unprioritised. Buckets are prioritised against each other — e.g. must-haves are higher priority than nice-to-haves — but the things within a bucket are not. To the naive observer, all the must-haves appear to be equal in terms of importance, and therefore the order in which they get done should not matter. This is a natural and logical conclusion to arrive at, but it couldn’t be further from the truth, and it’s the source of the problems in the story above.
If you tell a bunch of engineers to complete a collection of tasks, they are going to cherry-pick the things that are easy or interesting.
Easy things, by definition, are low-risk in terms of delivery. If they involved unknowns then they wouldn’t be easy. Cherry-picking the easy things causes all the high-risk tasks to be deferred until near the end of the project. So the point at which everyone discovers the devastating, unforeseen problems that threaten to tank the whole project is just before the deadline. It should be obvious why this is a terrible approach to delivering software. Bucketed prioritisation systems naturally lead people to make this mistake.
Cherry-picking the interesting things causes a different problem. I mentioned earlier that it’s a mistake to assume that the must-haves are all equal in terms of priority. Despite the name, the “must-haves” are going vary in importance, from absolutely critical down to things that should be nice-to-haves. Working in order of decreasing interest means that interesting less-important things are done before boring more-important things. This wouldn’t be an issue if all the things get done in time, but that rarely happens. Usually what happens is that new ideas are added to the bucket faster than the things in the bucket are getting completed, meaning that the bucket is effectively infinite. The consequence of ignoring the boring high-priority stuff is that, as you approach the deadline, the software has a lot of cool features but it doesn’t actually do what it was intended to do. The first 90% of the engineering budget gets squandered on random shit that the engineers think is cool, then the second 90% of the engineering budget is a mad rush to try and make it fit for purpose, resulting in late, poor-quality, ineffective software. This problem is a natural consequence of bucketed prioritisation too.
Hopefully by this point I’ve demonstrated why and how bucketed prioritisation drives poor outcomes. I’ve served up a heapin’ helpin’ o’ problems without even a side of solutions, so in the next article I’m going to suggest some tactics designed to avoid the pitfalls covered so far. Hint: it won’t involve buckets.