How can metrics help in improving our effectiveness?

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Metrics Workshop
Gaetano Mazzanti
@mgaewsj
agile42

Expectations

Q:
Which metrics are you using?
What is the goal?
Problems, anyone?

Exercise
Penny Game
How large (and valuable) are your batches?

Velocity, oh my

Correlation between Story Points and Time?

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/

Story Points vs # of Stories
time
SP
# of Stories

Exercise
Throughput Forecast

Throughput Forecast
Trial
1
Trial 2 Trial 3 Trial 4 Trial 5 Trial 6 Trial 7 Trial 8 Trial 9 Trial
10
Trial
11
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/

Throughput Forecast
by Troy Magennis https://creativecommons.org/licenses/by-nc-sa/2.0/
1. Minimum Sum (a): 31 Maximum Sum (b): 70
2. Minimum Sum with lowest removed (c): 39 Maximum Sum with highest
removed (d): 68
3. Sum all trials (e): 581 Average all trials (e/11):
53
4. Sum all trials with extremes removed (f): 480 Average with extremes
removed (f/9): 53
Actual data average 6 week throughput = 57.75. How close was your average?
5. Probabilities of achieving at least n stories for a six-week timespan
Six Week
Throughput
Count trial sum groups at
least 30,40, 50, etc. stories
(Count / 11)
Likelihood
At least 30 stories 11 1
At least 40 stories 9 0.82

This value is 0 to 1
Multiply it by 100
to get a percentage.
0% = no chance,
100% means every
trial achieved at
least this level.

but…
beware

Exercise
Simpson’s Paradox

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

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

Q:
why do we need metrics?

really, why do we need metrics?
key reason: to improve

really, why do we need metrics?
decisions

why do we need metrics?
predictability

why?
key questions about your metrics

what do you want
to improve?

what do you want
to learn?

which metrics?
matter to customers
no yes
end-to-end
(global)

functional
(local)

typical
ideal

pizza delivery
fast delivery
accuracy and quality
predictability
what matters
to customers

just three metrics?
Lead Time
Throughput
WIP

Little’s Law
Items In Queue = Arrival Rate * Waiting Time
Lead Time = Work In Progress / Throughput

ideas elaborate do deliveredvalidate
B
C
D
F
E
A
H
G
I
Lead Time
3 2 2
Lead Time
commitment point

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

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

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

CFD
arrivals

departures

CFD
arrivals

departures

average
lead time

WIP

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

some CFD issues
ﬂat line
growing area

staircase

scatterplot
source ActionableMetrics

scatterplot (only bugs)
source ActionableMetrics

frequency distribution
the shape of uncertainty*
source ActionableMetrics*Dominica DeGrandis

Weibull distribution
Weibull(1, 0.5)
Weibull(2, 0.5)
Weibull(3, 1)

___
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
waiting
active

correlations…
Productivity (throughput)
Quality (# of bugs)
Predictability (variance in throughput)
Responsiveness (Time in Process)
WIP
Focus
Stability
&

correlation with WIP
source Larry Maccherone

correlation with Team Focus

correlation with Team Stability

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/

tempus fugit

focus on trends
(not on absolute values)

"If you torture the data long enough, it will
confess to anything you'd like"

Ronald Coase, economist

Gaetano Mazzanti
@mgaewsj
gaetano.mazzanti@agile42.com
agile42

How can metrics help in improving our effectiveness?

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