SlideShare a Scribd company logo
July 12 2016
LET’S HAVE A QUICK CHAT
In measuring a team’s predictability, here is some
data sprint over sprint:
Units Committed Units Delivered % Predictable
50
50
50
?
50
40
60
?
100%
80%
80% or 120% ?
?
WE NEED SOME DATA
I’VE HEARD THIS BEFORE
“You can’t fix what you can’t measure”
“People fear what they don’t understand”
“You’re being judged no matter what. So be who
you want to be.”
DECISION ENABLERS
MEASURE THE RIGHT
STUFF
Look for trends
Use data to help improve
Build / measure / learn
WHO’S LOOKING AT THIS?
What do we want to measure?
What will it tell us?
Who will consume it?
How will they use it?
Does the metric incent the right behavior?
REMEMBER: 1 data point does not tell the entire
story!
EXERCISE #1
Word pairing
Take the worksheet provided and pair the metric
with the description of each
Discuss at your table
SPRINT BURNDOWN
BURNUP CHART
CFD
VELOCITY
EXERCISE #2
Good Team / Bad Team?
By use of thumbs up/thumbs down, vote whether
the scenario is a good team or a bad team
EXERCISE #2 – SCENARIO 1
Good Team / Bad Team?
Team A has delivered 30 units of work consistently
for 10 sprints in a row.
 Team has done 10 sprints
 Team dynamic is constantly changing
EXERCISE #2 – SCENARIO 2
Good Team / Bad Team?
Team B has an average velocity measure of 75
story points
 Trend is 100, then 50, then 100, then 50
 Last 4 sprints are 76, 72, 78, 74
EXERCISE #2 – SCENARIO 3
Good Team / Bad Team?
Team C has a predictability measure of 50%
 They’ve done 2 sprints
 They have been together for 10 sprints
METRICS ARE YOUR
FRIENDS
Measure to learn and improve
Measure the right stuff with the right intention
Avoid vanity metrics
If you don’t like what you see, make decisions to
change
Agile camp2016 dont fear the data

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Agile camp2016 dont fear the data

  • 2. LET’S HAVE A QUICK CHAT In measuring a team’s predictability, here is some data sprint over sprint: Units Committed Units Delivered % Predictable 50 50 50 ? 50 40 60 ? 100% 80% 80% or 120% ? ?
  • 4. I’VE HEARD THIS BEFORE “You can’t fix what you can’t measure” “People fear what they don’t understand” “You’re being judged no matter what. So be who you want to be.”
  • 6. MEASURE THE RIGHT STUFF Look for trends Use data to help improve Build / measure / learn
  • 7. WHO’S LOOKING AT THIS? What do we want to measure? What will it tell us? Who will consume it? How will they use it? Does the metric incent the right behavior? REMEMBER: 1 data point does not tell the entire story!
  • 8. EXERCISE #1 Word pairing Take the worksheet provided and pair the metric with the description of each Discuss at your table
  • 11. CFD
  • 13. EXERCISE #2 Good Team / Bad Team? By use of thumbs up/thumbs down, vote whether the scenario is a good team or a bad team
  • 14. EXERCISE #2 – SCENARIO 1 Good Team / Bad Team? Team A has delivered 30 units of work consistently for 10 sprints in a row.  Team has done 10 sprints  Team dynamic is constantly changing
  • 15. EXERCISE #2 – SCENARIO 2 Good Team / Bad Team? Team B has an average velocity measure of 75 story points  Trend is 100, then 50, then 100, then 50  Last 4 sprints are 76, 72, 78, 74
  • 16. EXERCISE #2 – SCENARIO 3 Good Team / Bad Team? Team C has a predictability measure of 50%  They’ve done 2 sprints  They have been together for 10 sprints
  • 17. METRICS ARE YOUR FRIENDS Measure to learn and improve Measure the right stuff with the right intention Avoid vanity metrics If you don’t like what you see, make decisions to change

Editor's Notes

  1. Discuss the meaning of “what are you measuring” Here we are measuring predictability Points out a couple of things (possibly): (a single data point doesn’t tell the story) Worried that 80% is viewed poorly when in fact we delivered more than we promised (FEAR drives this conversation) worried of how it is perceived
  2. Measure so we can make adjustments and measure again Did we get the outcome expected? Can we make an adjustment that will potentially give us the desired affect?
  3. If the data we collect does not help us make decisions, then why measure?
  4. Trends – what does this trend depict? Help Improve – based on these trends, what changes do we need to make? Build/Measure/Learn – turn ideas into product, measure how customers respond, learn whether to pivot or persevere Difference b/w vanity metrics and actionable data is VALUE Help us make decisions/adjustments Common vanity metrics Page views New members Total members Facebook friends
  5. Only 10 sprints – they are consistent, no improvements, no decline Dynamic changes – surprised it is consistent