This entry continues from two earlier ones:
Well two things, at least. First, when you average the data out it is no where near as variable - thats what averages do. I’m reminded of the old economists warning: “Do not pay to much attention to one month’s figures [GDP/GNP/Inflaction/etc]. Look at the trend.”
So yes, velocity iteration to iteration changes but over a longer period it is meaningful.
Second, this is a team I regard as stable. I learned a long time ago that if you don’t have a stable team your velocity data is meaningless. The velocity is delivered by the team members, if you change the team you can’t get a meaningful velocity.
While I regard this team as stable when I looked close, looked at the data and dredged my memory, this was not a stable team. One member retired, one member joined, the team was joined by another person to tackle a specific sub-set of work, the team adopted TDD a few months after moving to iterations, later still they tried doing pair-programming.
Somewhere along the line the hardware team joined the iterations, added to the velocity, then, after a while left. It didn’t work as well as hoped. When you look at the data you can see this: alone the software team can have a standard deviation as low as 3.6 on an average velocity of 62 (over 5 iterations) , with the hardware team added that goes to over 17 on a velocity of 60.
(Velocity falls after team expansion is a phenomenon I’ve seen in two other data sets I’ve got. Brooks’ Law doesn’t completely explain this, other factors are at work which I will discuss another day (i.e. when I understand them more fully!))
In other words, the team wasn’t stable. In fact, given all that change I’m surprised velocity was as stable as it was!
I think a third factor was at work. Once a team have put a point score on a card, say they point it to 5. Then there is a mild incentive to finish the card in something that feels like 5 points. Not the strong commitment of Scrum mythology, more a pride in ones own skills, and perhaps, a desire to score points at the end of the iteration.
Fourth: its not just development estimates that are helping the team hit dates. Armed with this data scope can be fine tuned, teams can take decisions on when to do refactorings and so on.
Fifth: once a team has velocity data and can forecast dates it it can negotiate on features and deliveries. This echo’s Duarte’s story but is more fine grained. Of course this won’t help if the end customers/users/clients/stakeholders aren’t prepared to engage.
Given all this I believe abstract points and graphs are helpful, not harmful.
Perhaps one day I’ll be able to publish this data. Its just one team but it shows that velocity and point scoring can work.