Who should look at what metrics?
When we talk about metrics, there is often an assumption that everyone in the company needs the same data to make decisions and this is dangerously incorrect. Different levels of the organization need different kinds of data to make effective decisions. Yet, all too often we use the wrong data at the wrong point.
Quality vs Testing: Solving the wrong problem
In the agile space, we talk a lot about testing. How will we test things? What should we test? How can we automate our testing? Yet, testing isn’t actually the point. What’s important instead, is quality. If we had amazing quality then it wouldn’t matter if we’d tested or not.
Slicing stories
In an agile environment, we split our work down into what we call “stories”, that are the smallest unit of value passing through a workstream. Unfortunately, we have a tendency to over-complicate story writing, making it unnecessarily hard. Done well, it can be a simple process of taking small steps, repeatedly.
Premature optimization
We have a tendency to think that making any one part of the workflow more efficient will make the overall workflow also more efficient and that’s just not true. Part of that is that not all parts of the workflow are on the critical path and improving something that isn’t currently a bottleneck won’t help the overall flow. But there’s a second reason that’s less obvious - sometimes optimizing for simple cases in the workflow, can make other parts of the workflow much worse.
Improving meetings
When we look for opportunities for improvement, at some point every team will bring up meetings as being an ongoing problem for them. When we dig into what the actual problems are, we find they always fall into one of three categories:
Per-story estimates
Per-story estimates were an interesting experiment that failed and it’s time to move on. Today, we have better ways so it’s time to stop putting individual estimates on stories. This is equally true for Scrum and Kanban teams.
What is a Service Level Expectation?
A service level expectation is a probabilistic forecast of how long it will take a single item to pass through the system. For example: “85% chance of completing in four days or less”.
Kanban: Simple, but not always obvious
We meet a lot of teams who say they’re doing Kanban and yet are only scratching the surface and not getting the benefit from Kanban that they could. They’re moving some cards across a board and think that’s all they have to do. Because it appears so simple, it doesn’t occur to them to reach out for assistance. Why would I need training or coaching to move some tickets around?
Keeping people busy
The Kanban Guide talks about optimizing the workflow for three different attributes: effectiveness, efficiency, and predictability. It talks about the fact that any optimizations we perform will be a balance across these three and that over-optimizing on one may make the others worse.
Steps to improving predictability
If you have a need to know when the work will be done or how much you can do in a certain period of time then predictability will be important to you. We have great tools like Monte Carlo for probabilistic forecasting but the truth is that the forecast we generate is only as good as the data we give it. Garbage in yields garbage out. So how do we improve our data to make it inherently more predictable?