One size does not fit all

©2015 Cutter Consortium

One Size Does Not Fit AllDr. Murray Cantor, Senior Consultant [email protected]


Things I have heard from over the yearsn “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 organizationsn 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

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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, …

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There is always a process. Is it what you intend?


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

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Choosing your methods needs to align

n With your organization level and goals

n With the mix of work you do

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Work item, artifact completion Staff member Commits to

Project, product delivery Project manager, team lead

Commits to

Efficiency, value delivery Senior manager Commits to

Profit, return on investment Line of business executive Commits to

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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

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(re)Set Goal

Take action

(practices)

Measure progress (analytics)

React


Adapting your organization

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Commits to


Profit, return on investment, mission fulfillment Line of business executive Commits to

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Meeting goals requires analytics

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Work item, artifact completionStaff member Commits to

Project, product deliveryProject manager, team lead Commits to

Efficiency, value deliverySenior manager Commits to


Before


Aligning goalsn 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

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Commits to





Commits to



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Adapting to your mix

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Kinds of Development Efforts: What is your mix?

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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

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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

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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 effortsn 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 Flowapply in this bucket

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Artifact-centricity is the appropriate process model for this (routine efforts) bucketn 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

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Semantics of artifact-centric value stream maps

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Example: A Value Stream model for routine efforts

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Control challenges• Random arrival intervals• Variation of effort to address work items (unlike standardized manufacturing)


Descriptive example: Cycle times

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These will be described in more detail in next webinar


To Visualize the data, use a histogram

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80% point is about 105 days


Insights and Actionsn 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

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This is what improvement looks like

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Example 2: Fitting analytics and practices to high innovation projectsn 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

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Estimating effort remaining

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+ … + =

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

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Summary'StatisticsMean 11.5377134Median 2.00294414Variance 3412.51999Standard'Deviation58.4167783Lower'Percentile'[25.0]E1.3278719Upper'Percentile'[75.0]7.37082892


Parting Thoughts: Putting It All Together

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The ‘Secret Sauce’ of the Integrative Frameworkn 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

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Commits to



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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

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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 CantorEmail: [email protected]

Contact Me

.


Murray Cantorn 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

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One size does not fit all

Engineering

Transcript of One size does not fit all