A common scenario
Teams tend to work on features from the entry point in, pushing the behaviour through the system. In my experience this is almost always a bad idea, and I’m going to explain why, and offer some alternative approaches.
Let’s consider a fictional business: Sangeeta’s Sheet Music.
Sangeeta has focussed hitherto on selling piano music through her website and Android and iOS apps, but she’s now keen to corner the string quartet market. Now, string quartet music is sold in both score format and as parts, so there will be a certain amount of work to store information on available stock and to allow customers to choose which format they want to purchase.
Like in many successful startups, Sangeeta’s tech team has grown to some fifteen nmembers, who have been split into Catalogue, Payment, Frontend and Apps teams. She sits down with representatives from these teams, and they agree that Charlie and his team will start work on storing available formats in the Catalogue.
Two-and-a-half months later, Charlie’s team have updated the database and API schemata, and Penny and her team have enabled the shopping basket and checkout process to process different formats at different price points. At this point they approach Fatimeh from Frontend and Alejandro from Apps: “We’re nearly ready to go live with our new string quartet catalogue: we’ve got the data schema in place, our stock room is filling up with catalogue, so we just need to you to update the front end and apps, and we’re ready to start selling!”
Alejandro and his team are busy fixing a long list of bugs from the new iPhone, and won’t be able to start work for at least six weeks, but fortunately Fatimeh’s team have some capacity, so they start work. It soon becomes clear that some of the initial assumptions don’t work out on in real life: use case research shows that many orders are likely to be for all four string parts, and the shopping basket API doesn’t support this easily. Furthermore, they quickly realise that some of the catalogue doesn’t fit into the expected model: Schönberg’s String Quartet №2 has a part for soprano voice, and George Crumb’s *Black Angels* is only available in score format.
After some tedious toing and froing between the Catalogue, Payment and Frontend teams, they are happy with the functionality, but Alejandro and his team still aren’t ready implement this functionality in the apps, and Sangeeta doesn’t want to launch functionality on just the website. The feature has now been in progress for four months, yet it’s still vapourware, and the stock is sitting unsold in the stockroom.
On the face of it, there are two problems here: cross-team coordination and poorly specified behaviour. I contend that these are both results of a deeper problem: starting work in the wrong place.
At 7digital I worked with Neil Kidd on a feature rather like this. We had observed how frequently these two problems arise, and decided we needed a different approach: we would start from the exit point and work backwards.
To do this we scripted some end-to-end User Journeys to describe the expected behaviours, stubbing out interim steps. We then worked with the responsible team to implement the final step in the process against canned data. We were then able to work backwards, working on each component in the chain to enable it to provide real rather than canned data. At each step in the process, we of course ensured that existing behaviour was unchanged, and that the new behaviour only kicked in when the previous component was ready to support it.
By starting with end-to-end User Journeys and considering the data output, we avoided nasty surprises, and our by triggering the new behaviour with data, rather than feature switches, we enabled the functionality to go live as soon as all the pieces were in place, which helps avoid producing vapourware.
I refer to this approach as Feature Pull, acknowledging the Kanban approach of pulling inventory through a manufacturing process, or work-in-progress through a development process, and I’ve been fairly vocal in encouraging colleagues to embrace it.
This concept tallies well with BDD and TDD principles, as it favours thinking about user journeys as an initial step, and testing from the desired outcome backwards.
I wanted to find a way to illustrate this principle to colleagues outside the context of a feature, and felt a game might be the answer. I attempted to run such a game at 7digital, but the clearest lesson we drew from it was the benefit of communication, rather than anything specific to the flow of feature work.
And so at Socrates BE I ran a session in which I asked for help in coming up with a such a game.
As it happens, the interesting part of this discussion was not the proposals for the game itself, but the last few minutes, in which we started to examine *why* a pull system is often most appropriate. We alighted upon the notion of Entropy and the idea that features in a system should flow from the point of least to the point of most entropy.
If we consider that interactions with a system can often be modelled as a decision tree that branches over time, it is not surprising that the point of most entropy is at the end of a user journey, and it makes sense to start at that point.
Theory of Constraints
This felt like a nice take-away point, but I felt there was something missing. This morning I was reflecting on these ideas again, and the answer came to me: the point of most entropy is the bottleneck in the system, and by starting at this point we are subordinating our work to the bottleneck.
Think back to Sangeeta’s Sheet Music. The most obvious bottleneck in this system is clearly Alejandro and the Apps team, as they have so much other work to do. It therefore makes sense to start work by subordinating to this bottleneck, not building up inventory by implementing features that depend on this team, but rather finding ways to enable this team to overcome its workload problems.
Once the Apps team bottleneck has been tackled, then another bottleneck appears, this time one of planning: the team needs to understand and implement all the scenarios that are needed before the minimum viable feature can go live. This may involve a fair amount of planning and conceptual work, but again there is no point in doing upstream development work until this has been tackled.
So here we have three metaphors for understanding how to approach features. If you like, you can focus on Pull, and start at the exit point of your system; if your mind turns to theoretical physics, then consider the entropy of your system, and start at the point where it is greatest; and if the Theory of Constraints gets you excited, then look for the bottleneck and subordinate to it. But whatever you do, please don’t just start coding!