One size does not fit all

©2015 Cutter Consortium
One Size Does Not Fit All
Dr. Murray Cantor, Senior Consultant
mcantor@cutter.com
www.murraycantor.com

Things I have heard from over the years
n “I have no idea.”
• Developers, when asked about how long
will it take?
n “We tried agile, but it didn't work for us.”
• Development Managers
n “Measures are a waste, they are costly,
oppressive, and interfere with the real
work”
• Some Methodologists
n “Trust the (my) process. If the process
is not working for you, you are doing it
wrong.”
• Some (of the same) Methodologists

Does one process every fit all organizations
n Over the years we have seen
many one true processes:
• Water Fall
• Boehm Spiral
• Extreme Programming (XP)
• Controlled Iteration, Rational Unified
Process
• Software Factories
• (Flavors of) Scaled Agile
• DevOps

Each of these have generated lots of heated
disagreements
4

The development leader’s choice
n Follow ‘the one true method’
• Advantage: It is prescriptive
• Disadvantage: It is prescriptive in that
• it may be blindly applied – there is
enough variation in software
development that blindly following even
a sound process will often, but not
always work.
n Roll your own
• You are likely to ask too much of the
practitioners – software developers
want to develop software, not become
experts in all these fields so they can
pick and apply the right principle.
• Relearn the old lessens, e.g .Brooks
law, Conway’s law, iteration
management, role of design, …
5
There is always a process. Is it what you intend?

So What to Do?
Start by understanding the work you do.
6

Choosing your methods needs to align
n With your organization level and
goals
n With the mix of work you do
7
Work item, artifact
completion
Staff member Commits to
Project, product delivery
Project manager, team
lead
Commits to
Efficiency, value deliverySenior manager Commits to
Profit, return on
investment
Line of business executive Commits to
Commitments
Analytics

Achieving goals requires sense and respond loops
n Key principles
– Kelvin’s Principle: “To measure is to
know. If you can not measure it, you can
not improve it”
• Measures are part of feedback loops
– The converse principle: “Don’t bother to
measure what you do not intend to
improve”
• Find a small set of measures, not a long laundry
list
– Einstein’s Principle: “The best solution
is as simple as possible, but not simpler.”
• Pick the right, not overly simple, statistic
8
(re)Set
Goal
Take
action
(practices)
Measure
progress
(analytics)
React

Adapting your organization
9
Work item, artifact
completion
lead
Commits to
Profit, return on investment,
mission fulfillment
Commitments
Analytics

Meeting goals requires analytics
10
Work item, artifact
completion
lead
Commits to
mission fulfillment
Before

Aligning goals
n For each level to meet its goal, the
leader is dependent on the lower
level.
n So, the leader seeks commitments
from that layer. Meeting those
commitments becomes the goal
of the next layer.
n Hence the analytics serve to
integrate the organization
11
Work item, artifact
completion
lead
Commits to
mission fulfillment
Work item, artifact
completion
lead
Commits to
mission fulfillment
Commitments
Analytics

Adapting to your mix
12

Kinds of Development Efforts: What is your mix?
13
1. Low innovation/high
certainty
• Detailed understanding
of the requirements
• Well understood code
2. Some innovation/
some uncertainty
• Architecture/Design in
place
• Some discovery required
to have confidence in
requirements
• Some
refactoring/evolution of
design might be required
3. High innovation/Low
Uncertainty
• Requirements not fully
understood, some
experimentation might be
required
• May be alternatives in choice
of technology
• No initial design/architecture

The methods landscape
14
Kanban
Lean startup: MVP
Agile, Scrum
Product Development Flow
Systems/Software Engineering
Lean Software
Podular Org.
Liminal Thinking.
Technical Debt Management
Iterative learning: Updating estimates and
plans in the face of evidence
DevOps/Continuous Delivery

1. Low innovation - high
certainty: Statistics of
• Cycle, lead times
• Backlogs size, growth
• Time in process
• Utilization
• Non-value added
effort
15
1 2 3
2. Some innovation -
some uncertainty
• Time, cost to delivery
• Velocity
• Burn down
• Cumulative Flow
Diagrams
3. High innovation: Low
certainty
• Time to pivot
• Value of learning
• Business canvas
• Time, cost to delivery
Apply measures in accord with project
characterization
Predictive/Bayesian
Descriptive

Example: Fitting analytics and practices to
routine efforts
n For low innovation efforts (continuous delivery, not “real”
projects), pick product flow practices and analytics
• Uncertainty is low: you have already carried out similar projects many
times
• The only thing that matters is how quickly or efficiently you can carry
out the project
• Suitable for lean/VSM measures
• Tradeoff between speed/efficiency(utilization)
• The principles described by Don Reinertsen in his book Flow
apply in this bucket
16

Artifact-centricity is the appropriate process
model for this (routine efforts) bucket
n Unlike activity-centric processes, artifact-centric processes
focus on describing how business data is changed/updated,
by a particular action or task, throughout the process.
n Specifically, in the routine effort bucket apply value stream
models and flow measures (as described in the previous
couple of slides) to state transitions of work products
(artifacts)
• Two state types:
– In process (undergoing state transitions)
– In backlog (awaiting state transition)
n If you consider this is a departure from traditional Agile
methods, you are right:
• One size does not fit all
17

Semantics of artifact-centric value stream
maps
18

Example: A Value Stream model for routine efforts
19
Control challenges
• Random arrival intervals
• Variation of effort to address work items (unlike standardized
manufacturing)

Descriptive example: Cycle times
20
These will be described in
more detail in next webinar

To Visualize the data, use a histogram
21
80% point is about 105 days

Insights and Actions
n Insights
• Both teams performing comparably: Not
obvious skills issue
• Backlogs too large
• The teams seem to be focusing on the
easier, not the most critical
n Actions
• With team investigate reason for backlog size
• Discovered the governance process (decision
to update statuses) is overly cumbersome
leaving staff free to work elsewhere
• In response, the governance process was:
– Streamlined (an approval eliminated)
– Automated (less time spent finding e-mails)
• Work with teams to set and track cycle time
80% goal by priority
22

This is what improvement looks like
23

Example 2: Fitting analytics and practices to
high innovation projects
n For high innovation projects pick probabilistic methods and
the corresponding set of practices:
• You really do not know what the solution would look like – you must
experiment in order to find it
n Not knowing what the solution would look like, your intuition is
a poor guide for estimating and scheduling under systemic
uncertainty:
• You must experiment in an affordable manner
• The results of the experimentation need to be bi-directionally
propagated
– Forward and,
– Backward
24

Estimating effort remaining
25
+
…
+ =
l e h
No
probability
less than
No
probability
greater than
Most
probable
value
For remaining epics:
• Estimate size
with triangular
distributions
• Sum using
forward
propagation (aka
Monte Carlo)

Bayesian Example:
What improvement looks like: Estimate of weeks late
26
Summary'Statistics
Mean 11.5377134
Median 2.00294414
Variance 3412.51999
Standard'Deviation58.4167783
Lower'Percentile'[25.0]E1.3278719
Upper'Percentile'[75.0]7.37082892

Parting Thoughts: Putting It All
Together
27

The ‘Secret Sauce’ of the Integrative
Framework
n Break your portfolio to the three buckets
n Use the right kind of analytics for each
of the three buckets:
• Analytics ensure on-going alignment
between projects, programs and
portfolios
• In particular, Bayesian analytics enables
us to incrementally and iteratively put
newly accrued data into consideration:
– In other words, Baysian methods enable
iteratively quantified learning
n This iteratively quantified learning
ensure on-going alignment, hence
empowerment
28
Work item, artifact
completion
lead
Commits to
mission fulfillment
Commitments
Analytics

The Virtuous Cycle of the Integrative
Framework
Up-to-Date
Shared Goals
Framework
Based on the
Three Buckets
and Analytics
Initial
Alignment
Empowered
Pods
Learning
through
Analytics
Realignment
29

Some things I have learned over the years
To steal ideas from one person is
plagiarism;; to steal from many is
research.
William Mizner
Human beings, who are almost
unique in having the ability to
learn from the experience of
others, are also remarkable for
their apparent disinclination to
do so.
Douglas Adams
The beginning of wisdom
is calling things by their
right names.
Chinese Proverb

Murray Cantor
Email: mcantor@cutter.com
www.murraycantor.com
Contact Me
.

Murray Cantor
n Areas of research & consulting:
• Agile management
• Lean software development
• Development intelligence
• Systems engineering
• Software development analytics
• Software governance
• Development management due diligence
n Major products delivered:
• AIX 3.X Graphics subsystem
– Founding member OpenGL ARB
• AIX 3.X multimedia subsystem
• Top secret system for USAF Space
Command
• RUPSE (Systems extension for Rational
Unified Process)
n Books:
• Object Oriented Project Management
• Software Leadership
n Sample accolades:
• IBM Distinguished Engineer
• IBM Plateau 4 Inventor
• Software Leadership received 4.7/5
star rating on amazon.com
34

One size does not fit all

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