Jirametrics 2.0

Jirametrics version 2.0 has been released. What is it? A tool for extracting metrics, and generating reports from Jira.

Forecasting projects

Weather predictions are probabilistic, not deterministic. That means there isn’t a single right answer that we can calculate. We can’t say it will rain at 11:05 but we can say that there’s an 80% chance of rain today. Forecasting when we’ll be done is also probabilistic, in exactly the same way. We can say based on past throughput data that we have an 85% chance of being done on or before May 12.

Data Accuracy

We have a tendency to believe that anything we see in a chart is 100% accurate, although that’s often not true. To understand the accuracy of the chart, we have to understand a couple of things:

  1. How accurate the initial data was.
  2. How much of the original data set was used in the chart.
  3. How good the chart is at communicating the right message.

Stalled work

As I talked about in this earlier video on standups, the work on our board can loosely be grouped into three categories. It’s either active, blocked, or stalled. We tend to spend a lot of time talking about the active and blocked work and have a tendency to forget about the rest, which results in stalled work aging unnecessarily. That in turn will make the overall system less effective and less predictable.

Slicing epics

We talk a lot about slicing stories but then when it comes to slicing larger types (epics, features, etc), we tend to wave our hands and say “it’s the same, only bigger”, which while true, is rarely helpful.

Remote work vs in-person: What does the data say?

When the worst effects of COVID had appeared to pass, many companies started implementing return-to-office mandates for their knowledge-workers, which have been controversial at best. The decision to do this was based on gut reactions from managers who, having no actual data, made the best guesses they could with what they knew.

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.