how can metrics help in improving our effectiveness?
TRANSCRIPT
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Metrics Workshop
Gaetano Mazzanti
@mgaewsj
agile42
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Expectations
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Q:
Which metrics are you using?
What is the goal?
Problems, anyone?
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Exercise
Penny Game
How large (and valuable) are your batches?
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Velocity, oh my
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Velocity, oh my
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Velocity, oh my
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Correlation between Story Points and Time?
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Correlation between Story Points and Time?
Ian Carroll collected data from 25 teams across 4 different organizations, 12500 work items in total – here are data from one of those teams
https://iancarroll.com/2016/04/18/no-correlation-between-estimated-size-and-actual-time-taken/
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Story Points vs # of Stories
time
SP
# of Stories
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Exercise
Throughput Forecast
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Throughput Forecast
Trial1
Trial2 Trial3 Trial4 Trial5 Trial6 Trial7 Trial8 Trial9 Trial10
Trial11
7 11 7 5 17 5 10 16 19 7 10 5 13 13 5 7 6 5 5 3 5 16 6 5 6 19 5 3 5 3 6 3 5 7 10 5 6 8 8 6 5 7 19 10 16 8 10 16
48 56 56 31 62 53 45 53 40+ 50+ 50+ 30+ 60+ 50+ 40+ 50+
Sum of all samples for each trial by column (upper) / Nearest “tens” grouping rounded down (lower)
by Troy Magennis https://creativecommons.org/licenses/by-nc-sa/2.0/
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Throughput Forecast
by Troy Magennis https://creativecommons.org/licenses/by-nc-sa/2.0/
1. MinimumSum(a): 31 MaximumSum(b): 70
2. MinimumSumwithlowestremoved(c): 39 MaximumSumwithhighest
removed(d): 68
3. Sumalltrials(e): 581 Averagealltrials(e/11):
53
4. Sumalltrialswithextremesremoved(f): 480 Averagewithextremes
removed(f/9): 53
Actualdataaverage6weekthroughput=57.75.Howclosewasyouraverage?
5. Probabilitiesofachievingatleastnstoriesforasix-weektimespan
SixWeek
Throughput
Counttrialsumgroupsat
least30,40,50,etc.stories
(Count/11)
Likelihood
Atleast30stories 11 1 Atleast40stories 9 0.82 Atleast50stories 7 0.63 Atleast60stories 3 0.28 Atleast70stories 1 0.09 Atleast80stories 0 0 Atleast90stories 0 0
Thisvalueis0to1
Multiplyitby100togetapercentage.0%=nochance,
100%meanseverytrialachievedatleastthislevel.
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but… beware
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Exercise
Simpson’s Paradox
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Simpson’s paradox Global Natural Treat
Live 108 153
Die 123 120
Natural 47% live Treat 56% live
Women Natural Treat
Live 57 32
Die 100 57
Natural 36,3% live Treat 36,0% live
Men Natural Treat
Live
Die
Natural live Treat live
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Simpson’s paradox
Global Natural Treat
Live 108 153
Die 123 120
Natural 47% live Treat 56% live
Women Natural Treat
Live 57 32
Die 100 57
Natural 36,3% live Treat 36,0% live
Men Natural Treat
Live 51 121
Die 23 63
Natural 69% live Treat 66% live
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Q: why do we need metrics?
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really, why do we need metrics?
key reason: to improve
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really, why do we need metrics?
decisions
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why do we need metrics?
predictability
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why?
key questions about your metrics
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what do you want to improve?
key questions about your metrics
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what do you want to learn?
key questions about your metrics
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which metrics?
matter to customers no yes
end-to-end (global)
functional (local)
typical
ideal
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pizza delivery
fast delivery
accuracy and quality
predictability
what matters to customers
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just three metrics?
Lead Time
Throughput
WIP
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Little’s Law
Items In Queue = Arrival Rate * Waiting Time
Lead Time = Work In Progress / Throughput
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ideas elaborate do delivered validate
B C
D F
E A
H
G
I
Lead Time
3 2 2
Lead Time
commitment point
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0
1
2
3
4
5
6
7
8
1-Feb 3-Feb 5-Feb 7-Feb 9-Feb 11-Feb 13-Feb 15-Feb 17-Feb 19-Feb 21-Feb
scatterplot
54%
71%
88%
96%
days average
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CFD
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Backlog To Do In Progress Done
Backlog To Do In Progress Done
throughput
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more about CFDs
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Backlog To Do In Progress Done
arrivals
departures
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CFD
arrivals
departures
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CFD
arrivals
departures
average lead time
WIP
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CFD
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Backlog To Do In Progress Done
average lead time
WIP
leading
lagging
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some CFD issues
flat line
growing area
staircase
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scatterplot
source ActionableMetrics
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scatterplot (only bugs)
source ActionableMetrics
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frequency distribution the shape of uncertainty*
source ActionableMetrics *Dominica DeGrandis
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Weibull distribution
Weibull(1, 0.5)
Weibull(2, 0.5)
Weibull(3, 1)
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___
efficiency
process efficiency = total time active time ________
20 5 = 25%
!1!!2!!3!!4!!5!!6!!7!!8!!9!10!11!12!13!14!15!16!17!18!19!20!
elaborate do validate deliver
waitingactive
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correlations…
Productivity (throughput)
Quality (# of bugs)
Predictability (variance in throughput)
Responsiveness (Time in Process)
WIP
Focus
Stability
&
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correlation with WIP
source Larry Maccherone
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correlation with Team Focus
source Larry Maccherone
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correlation with Team Stability
source Larry Maccherone
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Exercise
FeatureBan
by Mike Burrows of Positive Incline http://creativecommons.org/licenses/by-sa/4.0/ https://blog.agendashift.com/2016/06/02/featureban-2-0/
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tempus fugit
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focus on trends
(not on absolute values)
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"If you torture the data long enough, it will confess to anything you'd like"
Ronald Coase, economist
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Gaetano Mazzanti
@mgaewsj
agile42