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US Marines, Agile
and Heuristics
Peter Pito
@theppito
Why do projects fail?
3
4
Under pressure to make decisions under constraints of
Time & Knowledge
Yet, we are seen having
supernatural minds possessing
demonic powers of reason,
boundless knowledge and all the
time to make the decisions.
5
And yet again, we
know that’s important
to make decisions.
6
How can we make
good decisions, fast?
7
8
Visions of rationality
Introduction to heuristics
Adaptive toolbox of heuristics
The agile
adaptive toolbox of heuristics
• Process heuristics
• Technical practices heuristics
Departing thoughts
9
Visions of Rationality
Demons
Bounded
Rationality
Unbounded
Rationality
Optimization
under Constraints
Satisficing
Fast and Frugal
Heuristics
Unbounded
rationality
&
Optimization
under
constraints
10
• Decision-making strategies that
have little or no regard for the
constraints of time, knowledge, and
computational capacities that real
humans face.
• Traditionally modeled by probability
theory.
11
Bounded
Rationality
Models of human judgment and decision
making should be built on what we
actually know about the mind's capacities
rather than on fictitious competencies.
Because of the mind's limitations,
humans "must use approximate methods
to handle most tasks" – Simon Herbert
Herbert Simon
Nobel Prize laureate in economics,
“father” of Bounded Rationality
Satisficing
12
• Choice from a set of alternatives
encountered sequentially when
one does not know much about the
possibilities ahead of time. [1]
• End the search for alternatives as
soon as one is encountered that
exceeds the aspiration level.
[1] – Simon Herbert – reproduced from Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
Fast and
Frugal
Heuristics
13
• Fast and frugal heuristics limit
their search of objects or
information using easily
computable stopping rules, and
they make their choices with easily
computable decision rules. [1]
[1] - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
14
Up to 19 cues are measured when
a patient is admitted with a risk of
risk of heart attack.
• blood pressure
• age
• pulse
• ECG
• …
15
A simplified decision tree for
classifying heart attack victims as
high-risk or low risk victims.
Asking at most three yes/no
questions is a fast and frugal
strategy for making decision.
Is the minimum systolic
blood pressure over the
initial 24hr period > 91
High
Risk
Is age > 62.5?
Is sinus tachycardia
present?
High
Risk
Low
Risk
Low
Risk
No
No
No
Yes
Yes
Yes
16
A more accurate method than
some complex statistical
classification methods.
(Breiman et al., 1993).
Is the minimum systolic
blood pressure over the
initial 24hr period > 91
High
Risk
Is age > 62.5?
Is sinus tachycardia
present?
High
Risk
Low
Risk
Low
Risk
No
No
No
Yes
Yes
Yes
In many real-world situations, optimal
strategies are unknown or unknowable
(Herbert Simon) [1].
17[1] - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
Move from omniscient mind
computing intricate
probabilities and utilities to a
bounded mind reaching into a
toolbox filled with fast and
frugal heuristics.
18
Humans and animals make inferences
about their world with limited time,
knowledge, and computational power.
19
Heuristic
"serving to find out or discover."
• 1905 – Albert Einstein used the
term "heuristic" to indicate that
he considered the view he
presented [1] therein as
incomplete, false even, but
still useful.
• 1800 – 1970 "heuristic" referred
to useful, even indispensable
cognitive processes for solving
problems that cannot be
handled by logic and probability
theory
Source - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
[1] "On a heuristic point of view concerning the generation and
transformation of light.” Albert Einstein's Nobel prizewinning paper
20
Heuristic
"serving to find out or discover."
A heuristic is an approach to a problem that is
necessarily incomplete given the knowledge
available, and hence unavoidably false, but
which is useful nonetheless for guiding thinking
in appropriate directions.
Source - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
• ~1920 - Max Wertheimer, Karl Duncker (cognitive
psychologists) defined heuristic as a useful
shortcut, an approximation, or a rule of thumb
for guiding search.
21
Heuristic
"serving to find out or discover."
Source - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
Heuristics are an ideal control mechanism
under conditions of uncertainty and their
general form is easy to understand.
22
Heuristic
"serving to find out or discover."
Dave Snowden,
creator of
Cynefin framework
Photo By: Gunnery Sgt. T. T. Parish
When the battlefield plan breaks
down capture the high ground,
stay in touch, keep moving.
Us marines heuristic
Compliance
easy to test.
Handle
uncertainty. 24
Photo By: Gunnery Sgt. T. T. Parish
When the battlefield plan breaks
down capture the high ground,
stay in touch, keep moving.
Us marines heuristic
Adrian Newey’s heuristic
on how to design a formula 1 car
26
Continue to evolve the design for
an F1 car instead of always starting
from scratch, unless it’s clearly that
you are on a wrong track, or the
regulations are changing.
Adrian Newey, OBE, British Formula 1 designerEvolve to better understand
Joel Spolsky’s heuristic
Rewriting code from scratch
27
Don’t rewrite the code since you’re
throwing away real-world usage,
you are throwing away knowledge.
Knowledge collected in bug fixes.
Years of programming code.
Solve coding issues instead by
carefully moving code around, by
refactoring.
Joel Spolsky, CEO Stack Overflow
https://www.joelonsoftware.com/2000/04/06/things-you-should-never-do-part-i/
Evolve to better understand
Martin fowler heuristic
on coding
28
A heuristic we follow is that
whenever we feel the need to
comment something, we write a
method instead.
Martin Fowler, Refactoring, 1999
29
Adaptive toolbox of heuristics
Different domains of thought require
different specialized tools.
Agile Adaptive toolbox of heuristics
Agile as a domain requires its own
adaptive toolbox of heuristics.
A toolbox full of heuristics for making
reasonable decisions in software delivery.
30
31
Process Heuristics
Agile and Lean toolbox
Process heuristics congruent with
Agile and Lean thinking.
Technical Practices
Heuristics
Engineering practices toolbox
Engineering practices to support the
Agile and Lean processes.
32
Process Heuristics
Agile and Lean Toolbox
Process heuristics congruent with Agile and Lean
thinking.
Discover iteratively
Build multi-disciplinary teams
Make sense of your world
Understand your landscape
Design work-systems
Use an approach based on observation
and measurement
Use a forecasting approach to planning
Set short feedback loops
33
Discover iteratively
Accept that not all the work can or even should be defined
upfront. Use an approach that allows for discovery of the work
and solutions iteratively.
Create conditions for novel solutions to emerge.
Have a mindset for options, never commiting early unless you
know why.
”It’s not iterating if you do it only once” - Jeff Patton.
Coherent ideas prototyping Real options
34
Build multi-disciplinary teams
Create teams around clearly defined work slices. Provide them
with all the means necessary to complete their work.
Build teams in close proximity of Service Manager* for complex
problems.
Create an environment based on psychological safety and a
strong foundation of trust between the Service Manager and
delivery team.
* Service Manager is a GDS defined role, defined as someone with the knowledge and power to make day-
to-day decisions to improve the service
Cross functional teams Clear ownership of work
Psychological safety
Compliance
easy to test.
Handle
uncertainty. 35
Are we discovering
work in an iterative
fashion?
36
Have we built a
multi-disciplinary
team?
37
Make sense of your world
Use sense-making frameworks such as Cynefin to make sense
of the world around you.
Work under the assumption that you will encounter problems
with different levels of complexity. Constantly adapt your
practices according to the complexity domain you are
operating under.
Sense-making
Complex
complicated
obvious
chaotic
38
Understand your landscape
Execution alone is not enough, you will also need a good
strategy. Help visualizing your context with techniques such as
Wardley maps to make better strategic decisions for the future.
Use mapping to understand where to attack and why.
mapping
39
Design work systems
Define a system to track and manage your work.
Give a strong consideration to queue based systems, such as
Kanban.
Adapt the system for various types of work. Managing highly
uncertain work might require a different approach to the one
used for managing more certain one.
visualize Limit wip Manage queues
Make the invisible visible
40
Use an approach based on
observation and measurement
Build using an observation and measurement approach.
Measure Flow metrics such as Work In progress, CycleTime and
Throughput.
Analyse and adapt the system using these metrics.
Measure flow Preserve flow
41
Use a forecasting approach to
planning
Adopt a probabilistic outlook to planning instead of a
deterministic one. Forecast using date ranges backed by
confidence levels.
Perform short and long term forecasts, accepting that short
term forecast will be more accurate.
Reforecast on new information.
Monte Carlo simulations
42
Set short feedback loops
Set feedback loops in the system than enable learning.
Break the releases into multiple chunks, setting each chunk as
small as possible. Remove dependencies between chunks
where possible.
Perform early user testing, run retrospectives, use pair
programming.
Use the feedback to evolve your solutions.
Alpha & beta releases retrospect pair
Create rhythm
43
Coherent ideas prototyping Real options
Cross functional teams Clear ownership of work Psychological safety
Sense-making
mapping
visualize Limit wip Manage queues
Measure flow Preserve flow
Monte Carlo simulations
Alpha & beta releases retrospect pair
Discover iteratively
Build multi-disciplinary teams
Make sense of your world
Understand your landscape
Design work systems
Observation and measurement
Forecasting approach to planning
Set short feedback loops
44
Technical Practices
Heuristics
Engineering practices toolbox
Engineering practices to support the Agile and
Lean processes.
Automate deployments
Integrate early and continuously
Avoid branches, use trunk-based development
Automate tests
Maintain test data
Shift left on security
Implement continual delivery
Version control everything
45
46
Version control everything
Use version control for all production artifacts:
• application code
• application configurations
• system configurations
• scripts for automating build and configuration of the
environment
Infrastructure as code
Version
control
Source - Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
47
Automate deployments
Fully automate deployments so that they do not require
manual intervention.
Computers perform repetitive tasks; people solve problems. [1]
“Automate when tasks become boring” - Dan North.
Zero downtime deployments
[1] - Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
Environments on demand
48
Integrate early and continuously
Use Continuous Integration (CI) as a first step towards
continuous delivery:
• code is regularly checked in, and each check-in triggers a set
of quick tests to discover serious regressions
• the CI process creates builds and packages that are
potentially shippable to production
Test early at higher states of integration.
CI mocking Component testing
49
Avoid branches, use trunk-based
development
Use trunk-based development since it has shown to be a
predictor of high performance in software development and
delivery [1], characterized by:
• fewer than three active branches in a code repository
• branches and forks having very short lifetimes (e.g., less than
a day) before being merged into master
• rare “code lock” periods when no one can check in code or
do pull requests
[1] Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
50
Automate tests
Use test automation where software tests are run
automatically and continuously throughout the development
process.
To sustain high pace of delivery maintain rigour around test
automation, and:
• test early
• test often
• test well
automation
51
Maintain test data
Maintain carefully test data. Test data management is
becoming an increasingly important part of automated testing.
• necessary data is acquired on demand
• data can be conditioned in the pipelines
• data is not the limiting factor for the amount of testing data
can be run
Test data management
Source - Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
52
Shift left on security
Integrate security into design, implementation and testing
phases.
• infosec as team members
• use of preapproved security libraries
• security testing as part of automated suite
Security part of the development process
Source - Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
53
Implement Continuous Delivery
Implement Continuous Delivery (CD) a practice where software
is in a deployable state throughout its lifecycle.
• prioritize keeping the software in a deployable state over
working on new features
• fast feedback on the quality and deployability of the system
is available to all team members
deployable state over new features
Source - Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
54
Infrastructure as code
Zero downtime deployments
CImockingComponent testing
automation
Test data management
Security part of the development process
deployable state over new features
Version control everything
Automate deployments
Integrate early and continuously
Use trunk-based development
Automate tests
Maintain test data
Shift left on security
Implement Continuous Delivery
No branching
Environments on demand
Toolbox
56
57
Discover iteratively
Build multi-disciplinary teams
Make sense of your world
Understand your landscape
Design work systems
Observation and measurement
Forecasting approach to planning
Set short feedback loops
Version control everything
Automate deployments
Integrate early and continuously
Use trunk-based development
Automate tests
Maintain test data
Shift left on security
Implement Continuous Delivery
58
Infrastructure as code
Zero downtime deployments
CI mocking Component testing
automation
Test data management
Security part of the development process
deployable state over new features
Coherent ideas prototyping Real options
Cross functional teams Clear ownership of work Psychological safety
Sense-making mapping
visualize Limit wip Manage queues
Measure flow Preserve flow
Monte Carlo simulations
Alpha & beta releases Adapt based on feedback
No branching
Env. on demand
59
Rules vs principles vs heuristics
http://cognitive-edge.com/blog/cynefin-dynamics/
Set limits to action.
Contain all possible
instances of action.
Measurable guidance
which can adapt to the
unknowable unknowns.
Many actions can be
justified by interpreting
a principle.
Rules principles heuristics
60
We know that sometimes it will work and
sometimes it won’t.
But, should it fail, we should not be punished.
A Heuristic …
http://cognitive-edge.com/blog/rules-is-rules/
61
“Cognition is the art of focusing on the relevant and
deliberately ignoring the rest.”
Source - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
62
Useful?
A heuristic is an approach to a problem that is
necessarily incomplete given the knowledge
available, and hence unavoidably false, but
which is useful nonetheless for guiding thinking
in appropriate directions.
Source - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
This publication contains general information only, and none
of the member firms of Deloitte Touche Tohmatsu Limited, its
member firms, or their related entities (collective, the
“Deloitte Network”) is, by means of this publication,
rendering professional advice or services. Before making any
decision or taking any action that may affect your business,
you should consult a qualified professional adviser. No entity
in the Deloitte Network shall be responsible for any loss
whatsoever sustained by any person who relies on this
publication.
In this document references to Deloitte are references to
Deloitte LLP. Deloitte LLP is the United Kingdom member firm
of Deloitte Touche Tohmatsu Limited (“DTTL”), a UK private
company limited by guarantee, whose member firms are
legally separate and independent entities. Please see
www.deloitte.co.uk/about for a detailed description of the
legal structure of DTTL and its member firms.
Copyright © 2016 Deloitte LLP. All rights reserved.
63
Thank you.
Peter Pito
@theppito
64
References
Cynefin framework for decision making
65
Evolve to better understand Make the invisible visible Create rhythm

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US Marines, Agile and Heuristic

  • 1. US Marines, Agile and Heuristics Peter Pito @theppito
  • 2. Why do projects fail? 3
  • 3. 4 Under pressure to make decisions under constraints of Time & Knowledge
  • 4. Yet, we are seen having supernatural minds possessing demonic powers of reason, boundless knowledge and all the time to make the decisions. 5
  • 5. And yet again, we know that’s important to make decisions. 6
  • 6. How can we make good decisions, fast? 7
  • 7. 8 Visions of rationality Introduction to heuristics Adaptive toolbox of heuristics The agile adaptive toolbox of heuristics • Process heuristics • Technical practices heuristics Departing thoughts
  • 9. Unbounded rationality & Optimization under constraints 10 • Decision-making strategies that have little or no regard for the constraints of time, knowledge, and computational capacities that real humans face. • Traditionally modeled by probability theory.
  • 10. 11 Bounded Rationality Models of human judgment and decision making should be built on what we actually know about the mind's capacities rather than on fictitious competencies. Because of the mind's limitations, humans "must use approximate methods to handle most tasks" – Simon Herbert Herbert Simon Nobel Prize laureate in economics, “father” of Bounded Rationality
  • 11. Satisficing 12 • Choice from a set of alternatives encountered sequentially when one does not know much about the possibilities ahead of time. [1] • End the search for alternatives as soon as one is encountered that exceeds the aspiration level. [1] – Simon Herbert – reproduced from Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
  • 12. Fast and Frugal Heuristics 13 • Fast and frugal heuristics limit their search of objects or information using easily computable stopping rules, and they make their choices with easily computable decision rules. [1] [1] - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
  • 13. 14 Up to 19 cues are measured when a patient is admitted with a risk of risk of heart attack. • blood pressure • age • pulse • ECG • …
  • 14. 15 A simplified decision tree for classifying heart attack victims as high-risk or low risk victims. Asking at most three yes/no questions is a fast and frugal strategy for making decision. Is the minimum systolic blood pressure over the initial 24hr period > 91 High Risk Is age > 62.5? Is sinus tachycardia present? High Risk Low Risk Low Risk No No No Yes Yes Yes
  • 15. 16 A more accurate method than some complex statistical classification methods. (Breiman et al., 1993). Is the minimum systolic blood pressure over the initial 24hr period > 91 High Risk Is age > 62.5? Is sinus tachycardia present? High Risk Low Risk Low Risk No No No Yes Yes Yes
  • 16. In many real-world situations, optimal strategies are unknown or unknowable (Herbert Simon) [1]. 17[1] - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
  • 17. Move from omniscient mind computing intricate probabilities and utilities to a bounded mind reaching into a toolbox filled with fast and frugal heuristics. 18 Humans and animals make inferences about their world with limited time, knowledge, and computational power.
  • 18. 19 Heuristic "serving to find out or discover." • 1905 – Albert Einstein used the term "heuristic" to indicate that he considered the view he presented [1] therein as incomplete, false even, but still useful. • 1800 – 1970 "heuristic" referred to useful, even indispensable cognitive processes for solving problems that cannot be handled by logic and probability theory Source - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart [1] "On a heuristic point of view concerning the generation and transformation of light.” Albert Einstein's Nobel prizewinning paper
  • 19. 20 Heuristic "serving to find out or discover." A heuristic is an approach to a problem that is necessarily incomplete given the knowledge available, and hence unavoidably false, but which is useful nonetheless for guiding thinking in appropriate directions. Source - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
  • 20. • ~1920 - Max Wertheimer, Karl Duncker (cognitive psychologists) defined heuristic as a useful shortcut, an approximation, or a rule of thumb for guiding search. 21 Heuristic "serving to find out or discover." Source - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
  • 21. Heuristics are an ideal control mechanism under conditions of uncertainty and their general form is easy to understand. 22 Heuristic "serving to find out or discover." Dave Snowden, creator of Cynefin framework
  • 22. Photo By: Gunnery Sgt. T. T. Parish When the battlefield plan breaks down capture the high ground, stay in touch, keep moving. Us marines heuristic
  • 24. Photo By: Gunnery Sgt. T. T. Parish When the battlefield plan breaks down capture the high ground, stay in touch, keep moving. Us marines heuristic
  • 25. Adrian Newey’s heuristic on how to design a formula 1 car 26 Continue to evolve the design for an F1 car instead of always starting from scratch, unless it’s clearly that you are on a wrong track, or the regulations are changing. Adrian Newey, OBE, British Formula 1 designerEvolve to better understand
  • 26. Joel Spolsky’s heuristic Rewriting code from scratch 27 Don’t rewrite the code since you’re throwing away real-world usage, you are throwing away knowledge. Knowledge collected in bug fixes. Years of programming code. Solve coding issues instead by carefully moving code around, by refactoring. Joel Spolsky, CEO Stack Overflow https://www.joelonsoftware.com/2000/04/06/things-you-should-never-do-part-i/ Evolve to better understand
  • 27. Martin fowler heuristic on coding 28 A heuristic we follow is that whenever we feel the need to comment something, we write a method instead. Martin Fowler, Refactoring, 1999
  • 28. 29 Adaptive toolbox of heuristics Different domains of thought require different specialized tools.
  • 29. Agile Adaptive toolbox of heuristics Agile as a domain requires its own adaptive toolbox of heuristics. A toolbox full of heuristics for making reasonable decisions in software delivery. 30
  • 30. 31 Process Heuristics Agile and Lean toolbox Process heuristics congruent with Agile and Lean thinking. Technical Practices Heuristics Engineering practices toolbox Engineering practices to support the Agile and Lean processes.
  • 31. 32 Process Heuristics Agile and Lean Toolbox Process heuristics congruent with Agile and Lean thinking. Discover iteratively Build multi-disciplinary teams Make sense of your world Understand your landscape Design work-systems Use an approach based on observation and measurement Use a forecasting approach to planning Set short feedback loops
  • 32. 33 Discover iteratively Accept that not all the work can or even should be defined upfront. Use an approach that allows for discovery of the work and solutions iteratively. Create conditions for novel solutions to emerge. Have a mindset for options, never commiting early unless you know why. ”It’s not iterating if you do it only once” - Jeff Patton. Coherent ideas prototyping Real options
  • 33. 34 Build multi-disciplinary teams Create teams around clearly defined work slices. Provide them with all the means necessary to complete their work. Build teams in close proximity of Service Manager* for complex problems. Create an environment based on psychological safety and a strong foundation of trust between the Service Manager and delivery team. * Service Manager is a GDS defined role, defined as someone with the knowledge and power to make day- to-day decisions to improve the service Cross functional teams Clear ownership of work Psychological safety
  • 35. Are we discovering work in an iterative fashion? 36 Have we built a multi-disciplinary team?
  • 36. 37 Make sense of your world Use sense-making frameworks such as Cynefin to make sense of the world around you. Work under the assumption that you will encounter problems with different levels of complexity. Constantly adapt your practices according to the complexity domain you are operating under. Sense-making Complex complicated obvious chaotic
  • 37. 38 Understand your landscape Execution alone is not enough, you will also need a good strategy. Help visualizing your context with techniques such as Wardley maps to make better strategic decisions for the future. Use mapping to understand where to attack and why. mapping
  • 38. 39 Design work systems Define a system to track and manage your work. Give a strong consideration to queue based systems, such as Kanban. Adapt the system for various types of work. Managing highly uncertain work might require a different approach to the one used for managing more certain one. visualize Limit wip Manage queues Make the invisible visible
  • 39. 40 Use an approach based on observation and measurement Build using an observation and measurement approach. Measure Flow metrics such as Work In progress, CycleTime and Throughput. Analyse and adapt the system using these metrics. Measure flow Preserve flow
  • 40. 41 Use a forecasting approach to planning Adopt a probabilistic outlook to planning instead of a deterministic one. Forecast using date ranges backed by confidence levels. Perform short and long term forecasts, accepting that short term forecast will be more accurate. Reforecast on new information. Monte Carlo simulations
  • 41. 42 Set short feedback loops Set feedback loops in the system than enable learning. Break the releases into multiple chunks, setting each chunk as small as possible. Remove dependencies between chunks where possible. Perform early user testing, run retrospectives, use pair programming. Use the feedback to evolve your solutions. Alpha & beta releases retrospect pair Create rhythm
  • 42. 43 Coherent ideas prototyping Real options Cross functional teams Clear ownership of work Psychological safety Sense-making mapping visualize Limit wip Manage queues Measure flow Preserve flow Monte Carlo simulations Alpha & beta releases retrospect pair Discover iteratively Build multi-disciplinary teams Make sense of your world Understand your landscape Design work systems Observation and measurement Forecasting approach to planning Set short feedback loops
  • 43. 44 Technical Practices Heuristics Engineering practices toolbox Engineering practices to support the Agile and Lean processes. Automate deployments Integrate early and continuously Avoid branches, use trunk-based development Automate tests Maintain test data Shift left on security Implement continual delivery Version control everything
  • 44. 45
  • 45. 46 Version control everything Use version control for all production artifacts: • application code • application configurations • system configurations • scripts for automating build and configuration of the environment Infrastructure as code Version control Source - Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
  • 46. 47 Automate deployments Fully automate deployments so that they do not require manual intervention. Computers perform repetitive tasks; people solve problems. [1] “Automate when tasks become boring” - Dan North. Zero downtime deployments [1] - Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations Environments on demand
  • 47. 48 Integrate early and continuously Use Continuous Integration (CI) as a first step towards continuous delivery: • code is regularly checked in, and each check-in triggers a set of quick tests to discover serious regressions • the CI process creates builds and packages that are potentially shippable to production Test early at higher states of integration. CI mocking Component testing
  • 48. 49 Avoid branches, use trunk-based development Use trunk-based development since it has shown to be a predictor of high performance in software development and delivery [1], characterized by: • fewer than three active branches in a code repository • branches and forks having very short lifetimes (e.g., less than a day) before being merged into master • rare “code lock” periods when no one can check in code or do pull requests [1] Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
  • 49. 50 Automate tests Use test automation where software tests are run automatically and continuously throughout the development process. To sustain high pace of delivery maintain rigour around test automation, and: • test early • test often • test well automation
  • 50. 51 Maintain test data Maintain carefully test data. Test data management is becoming an increasingly important part of automated testing. • necessary data is acquired on demand • data can be conditioned in the pipelines • data is not the limiting factor for the amount of testing data can be run Test data management Source - Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
  • 51. 52 Shift left on security Integrate security into design, implementation and testing phases. • infosec as team members • use of preapproved security libraries • security testing as part of automated suite Security part of the development process Source - Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
  • 52. 53 Implement Continuous Delivery Implement Continuous Delivery (CD) a practice where software is in a deployable state throughout its lifecycle. • prioritize keeping the software in a deployable state over working on new features • fast feedback on the quality and deployability of the system is available to all team members deployable state over new features Source - Forsgren PhD, Nicole; Humble, Jez; Kim, Gene. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations
  • 53. 54 Infrastructure as code Zero downtime deployments CImockingComponent testing automation Test data management Security part of the development process deployable state over new features Version control everything Automate deployments Integrate early and continuously Use trunk-based development Automate tests Maintain test data Shift left on security Implement Continuous Delivery No branching Environments on demand
  • 55. 56
  • 56. 57 Discover iteratively Build multi-disciplinary teams Make sense of your world Understand your landscape Design work systems Observation and measurement Forecasting approach to planning Set short feedback loops Version control everything Automate deployments Integrate early and continuously Use trunk-based development Automate tests Maintain test data Shift left on security Implement Continuous Delivery
  • 57. 58 Infrastructure as code Zero downtime deployments CI mocking Component testing automation Test data management Security part of the development process deployable state over new features Coherent ideas prototyping Real options Cross functional teams Clear ownership of work Psychological safety Sense-making mapping visualize Limit wip Manage queues Measure flow Preserve flow Monte Carlo simulations Alpha & beta releases Adapt based on feedback No branching Env. on demand
  • 58. 59 Rules vs principles vs heuristics http://cognitive-edge.com/blog/cynefin-dynamics/ Set limits to action. Contain all possible instances of action. Measurable guidance which can adapt to the unknowable unknowns. Many actions can be justified by interpreting a principle. Rules principles heuristics
  • 59. 60 We know that sometimes it will work and sometimes it won’t. But, should it fail, we should not be punished. A Heuristic … http://cognitive-edge.com/blog/rules-is-rules/
  • 60. 61 “Cognition is the art of focusing on the relevant and deliberately ignoring the rest.” Source - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
  • 61. 62 Useful? A heuristic is an approach to a problem that is necessarily incomplete given the knowledge available, and hence unavoidably false, but which is useful nonetheless for guiding thinking in appropriate directions. Source - Gerd Gigerenzer;Peter M. Todd;ABC Research Group. Simple Heuristics that Make Us Smart
  • 62. This publication contains general information only, and none of the member firms of Deloitte Touche Tohmatsu Limited, its member firms, or their related entities (collective, the “Deloitte Network”) is, by means of this publication, rendering professional advice or services. Before making any decision or taking any action that may affect your business, you should consult a qualified professional adviser. No entity in the Deloitte Network shall be responsible for any loss whatsoever sustained by any person who relies on this publication. In this document references to Deloitte are references to Deloitte LLP. Deloitte LLP is the United Kingdom member firm of Deloitte Touche Tohmatsu Limited (“DTTL”), a UK private company limited by guarantee, whose member firms are legally separate and independent entities. Please see www.deloitte.co.uk/about for a detailed description of the legal structure of DTTL and its member firms. Copyright © 2016 Deloitte LLP. All rights reserved. 63 Thank you. Peter Pito @theppito
  • 64. 65 Evolve to better understand Make the invisible visible Create rhythm