"We use data to help your team see what was previously invisible."
I'm interested in learning how my team can measure quality. After nearly two years of
nagging encouragement from my friend Duncan Nisbet, I finally took the Data-Driven Coaching and Outcomes course by Troy Magennis. It was eye-opening, and there were many takeaways.
The ones I'll talk about in this post are Troy's 5 Golden Metric Rules™️!
Respect Individual Safety: Don't use data to shame and embarrass people.
A couple of things happen if you ignore this rule:
Data gets hidden because
Others will see how people are treated and won't put data out in the open as an act of self-preservation!
Make things safe by measuring at the team level to improve overall performance, not individuals.
Today we have 10 open defects.
How would you act on this data? It's difficult to answer because there is too much hidden context. Without that information, any conclusions you reach or actions you take don't have a solid foundation. Compare these different situations about yesterday's defect count:
Things look somewhat different now, eh?
Compare data in context.
Outliers may not be outliers.
We'll almost certainly have outliers when we compare things like lead time for work items on a scatter plot. But are they? We have to be sure that we're comparing apples with apples. Perhaps that thing that took longer was a deliverable of something built in new technology, implementing something orders of magnitude more complex than a typical story.
If we use this information to start a conversation, that's good.
If we use this information to make automatic, unthinking actions, that isn't good.
Highlight unusual clearly.
Would we recognise unusual if we saw it?
We're helping people spot "unusual" so they can take action to bring themselves back to "usual". We're looking for something to discuss and should avoid automated judgement without comprehension.
Help teams identify "unusual" so that they can act accordingly.
Balanced - avoid over-focusing.
If you measure one thing, you will be successful in that one thing but at what cost?
We're trying to help people make intelligent trades between the competing factors of their work. Here are the factors:
Valuable - Do the right stuff
Consistency - Do it predictably
Quantity - Do lots
Sustainability - Keep doing it
Speed - Do it fast
Quality - Do it right
Since teams are already making these decisions, our job while coaching with data is to make those trade-offs and thought decisions visible.
That's a lot, right!?
Troy is a great instructor who packs a ton of information into a short time.
It reminds me of learning Chess for the first time. I understood how the pieces moved, but I was no grandmaster!
I'll share some more about what I learned and how I use it in later posts.