How to be an Evil Scientist - DevOps 2020

Have you ever fancied turning your co-workers into laboratory rats and have them scurry around a maze searching for food whilst you ring a bell and the office cat slavers at the mouth?

No? Okay, well we weren't going to tell you how to do that anyway!

What we will share is how to embed a culture of ongoing experimentation that enables your team to learn and adopt new technologies, techniques, and processes - whilst also achieving current objectives.

No animals were harmed in the creation of this talk.

Transcript

1. armakuni.com
   DevOps 2020
   London - January 2020
   Benedict Steele
   How to be an
   Evil Scientist
   

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2. Why are we evil enough to be standing in front of you?
   

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3. How to be an Evil Scientist
   1. Choose an evil name
   

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4. Your first name
   A The Evil N The Crazy
   B The Terrifying O The Iron
   C The Big P The Poison
   D The Dangerous Q The Bloody
   E Captain R The Annoying
   F The Ghostly S The Dangerous
   G Professor T The Rancid
   H Doctor U The Invisible
   I Phantom V The Dastardly
   J The Brutal W The Atomic
   K The Unstoppable X The Mega
   L The Vile Y The Grand
   M The Dark Z The Vicious
   

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5. Your last name
   A Shadow N Child
   B Wizard/Witch O Corpse
   C Tarantula P Slayer
   D Skull Q Spider
   E Mastermind R Creature
   F Wizard S Werewolf
   G Ninja T Monster
   H Devil U Vampire
   I Freak V Mutant
   J Beast W Robot
   K Criminal X Claw
   L Master Y Machine
   M Lord/Lady Z Clown
   

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6. How to be an Evil Scientist
   1. Choose an evil name ✔
   2. Share the most evil thing you’ve ever done
   

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7. Not evil enough for you?
   

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8. Not evil enough for you?
   

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9. Not evil enough for you?
   

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10. This is Billie
    ● Consulting Engineer for Armakuni
    ● Quite tall
    ● Helps people use best practices
    ● Favourite animal is the capybara
    ● Evil Name - The Terrifying Monster
    ● We stole this talk from her, turned it into a
    workshop and didn’t even say “thank-you”!
    

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11. How to be an Evil Scientist
    1. Choose an evil name ✔
    2. Share the most evil thing you’ve ever done ✔
    3. Learn the rules every evil scientist must follow
    

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12. Evil Scientist Rules
    

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13. Evil Scientist Rules
    Never get caught
    monologuing
    

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14. Evil Scientist Rules
    Never get caught
    monologuing
    Always splice
    things together
    

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15. Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Never get caught
    monologuing
    Always splice
    things together
    

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16. Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Always start small
    Never get caught
    monologuing
    Always splice
    things together
    

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17. Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Always start small
    Always have an
    arch-enemy
    Never get caught
    monologuing
    Always splice
    things together
    

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18. Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Always start small
    Always have an
    arch-enemy
    Always have an escape
    plan
    Never get caught
    monologuing
    Always splice
    things together
    

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19. Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Always start small
    Always have an
    arch-enemy
    Always have an escape
    plan
    Never get caught
    monologuing
    Always measure
    everything
    Always splice
    things together
    

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20. Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Always start small
    Always have an
    arch-enemy
    Always have an escape
    plan
    Never wear
    capes
    Never get caught
    monologuing
    Always measure
    everything
    Always splice
    things together
    

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21. Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Always start small
    Always have an
    arch-enemy
    Always have an escape
    plan
    Always boast, there’s
    no point in being evil if
    you don’t boast about
    it
    Never wear
    capes
    Never get caught
    monologuing
    Always measure
    everything
    Always splice
    things together
    

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22. How to be an Evil Scientist
    1. Choose an evil name ✔
    2. Share the most evil thing you’ve ever done ✔
    3. Learn the rules every evil scientist must follow ✔
    4. Discover our arch enemies
    

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23. Our Arch Enemies
    Waiting
    Around Kid
    Captain
    Defect
    General
    Heroics
    The Crimson
    Handoff
    Gold Plated
    Features Girl
    Mr Unneeded
    Process
    El Manual
    Work
    Awful
    Comms
    Boy
    Knowledge
    Drain Man
    Constance
    “Task”
    Switching
    The
    Relearner
    Rework
    

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24. Our Arch Enemies
    Partially
    Completed
    Work
    Woman
    Dr Overly
    Complex
    Solutions
    The Siloed
    Worker
    Poor
    Visibility
    Man
    

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26. Works
    unsociable
    hours
    

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27. Works
    unsociable
    hours
    Poor
    mentors
    

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28. Refuses to
    collaborate
    or attend
    meetings
    Works
    unsociable
    hours
    Poor
    mentors
    

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29. Refuses to
    collaborate
    or attend
    meetings
    Works
    unsociable
    hours
    Code
    structure is
    in their
    head
    Poor
    mentors
    

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30. Refuses to
    collaborate
    or attend
    meetings
    Works
    unsociable
    hours
    Code
    structure is
    in their
    head
    Believe
    they don’t
    need
    training
    Poor
    mentors
    

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31. Refuses to
    collaborate
    or attend
    meetings
    Works
    unsociable
    hours
    Code
    structure is
    in their
    head
    Believe
    they don’t
    need
    training
    Poor
    mentors
    The go-to
    person for
    QAs and
    support
    

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32. How to be an Evil Scientist
    1. Choose an evil name ✔
    2. Share the most evil thing you’ve ever done ✔
    3. Learn the rules every evil scientist must follow ✔
    4. Discover our arch enemies ✔
    

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33. armakuni.com
    DeadlyOps 2020
    London - January 2020
    The Terrifying Werewolf
    How to be an
    Evil Scientist
    

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34. Ladies and gentlemen: the story you
    are about to hear is true.
    Only the names have been changed
    to protect the innocent.
    

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35. Department for Feline Empowerment
    

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36. This Dastardly Ashley
    

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37. Pair Programming
    Pipelines
    Test-Driven
    Development
    

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38. ?
    

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39. It worked!
    

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40. Superheroes were being defeated
    

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41. She was asked to help Sam and Alex do it too
    

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42. So she did
    

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43. ...but she had demands!
    

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44. They did exactly the same thing, but it didn’t work
    

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45. They were thwarted by super heroes at every turn
    

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46. And they all went to jail
    

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47. Thankfully Ashley always has an escape plan
    

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48. The future is already here - it’s
    just not evenly distributed
    — William Gibson
    

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49. Every team is different
    

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50. The Department for Feline Empowerment needed to go
    back to the drawing board
    

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51. Ashley’s first few weeks
    

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52. Ashley didn’t monologue - she listened and observed
    

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53. Found the pain points and the gaps between vision and reality
    

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54. Worked out what the problems were and what potential
    fixes could be
    

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55. Tried them out one by one in real world villainous situations
    

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56. She then looked back to see if they worked
    

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57. What if Alex and Sam took the same approach and
    experimented with their teams?
    

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58. What does Ashley know
    about teams?
    

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60. View Slide

61. TEAM
    Tools
    Process
    People
    

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62. View Slide

63. Evil Scientist Rules
    

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64. Evil Scientist Rules
    Never get caught
    monologuing
    

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65. Evil Scientist Rules
    Never get caught
    monologuing
    

    View Slide

66. Evil Scientist Rules
    Never get caught
    monologuing
    Always splice
    things together
    

    View Slide

67. Evil Scientist Rules
    Never get caught
    monologuing
    Always splice
    things together
    

    View Slide

68. Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Never get caught
    monologuing
    Always splice
    things together
    

    View Slide

69. Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Never get caught
    monologuing
    Always splice
    things together
    

    View Slide

70. Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Always start
    small
    Never get caught
    monologuing
    Always splice
    things together
    

    View Slide

71. Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Always start
    small
    Never get caught
    monologuing
    Always measure
    everything
    Always splice
    things together
    

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72. Empathise
    Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Always start
    small
    Always measure
    everything
    Always splice
    things together
    

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73. Empathise
    Evil Scientist Rules
    Always have lots of
    evil schemes - you
    never know if there’s
    going to be a sequel
    Always start
    small
    Always measure
    everything
    Synthesise
    

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74. Empathise
    Evil Scientist Rules
    Always start
    small
    Always measure
    everything
    Ideate
    Synthesise
    

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75. Empathise
    Evil Scientist Rules
    Always measure
    everything
    Ideate
    Prototype
    Synthesise
    

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76. Empathise
    Evil Scientist Rules
    Ideate
    Prototype
    Test
    Synthesise
    

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77. Empathise
    Design Thinking
    Ideate
    Prototype
    Test
    Synthesise
    

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78. Design thinking is a human-centered
    approach to innovation that draws from
    the designer's toolkit to integrate the
    needs of people, the possibilities of
    technology, and the requirements for
    business success.
    — Tim Brown, CEO of IDEO
    

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79. Stakeholder Mapping
    Team Metrics
    Empathise
    

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80. Defining the stakeholders
    ● Who will be impacted by the project?
    ● Who will be responsible or accountable for the
    project?
    ● Who will have decision authority on the
    project?
    ● Who can support the project?
    ● Who can obstruct the project?
    ● Who has been involved in this type of project
    in the past?
    Keep informed Manage closely
    Monitor
    Anticipate and
    meet needs
    Interest
    Low
    Influence
    Low High
    High
    PMO
    CEO
    SA
    Ops
    Data
    PO
    BA
    UX
    EA
    Dev
    QA
    

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81. A pattern of shared tacit assumptions that was
    learned by a group as it solved its problems of
    external adaptation and internal integration, that
    has worked well enough to be considered valid
    and, therefore, to be taught to new members as
    the correct way to perceive, think, and feel in
    relation to those problems.
    — Edgar Schein
    

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82. Westrum typology to measure culture
    Statement Your Score
    On my team, information is actively sought.
    On my team, failures are learning opportunities, and messengers of them are
    not punished.
    On my team, responsibilities are shared.
    On my team, cross-functional collaboration is encouraged and rewarded.
    On my team, failure causes enquiry.
    On my team, new ideas are welcomed.
    Likert Scale
    Strongly disagree - 1
    Disagree - 2
    Somewhat disagree -3
    Neither agree nor
    disagree - 4
    Somewhat agree - 5
    Agree - 6
    Strongly agree - 7
    

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83. Westrum typology to measure culture
    Pathological (power-oriented)
    Score 6-18
    Bureaucratic (rule-oriented)
    Score 19 - 30
    Generative (performance-
    oriented)
    Score 31-42
    Low co-operation Modest co-operation High co-operation
    Messengers shot Messengers neglected Messengers trained
    Responsibilities shirked Narrow responsibilities Risks are shared
    Bridging discouraged Bridging tolerated Bridging encouraged
    Failure leads to scapegoating Failure leads to justice Failure leads to enquiry
    Novelty crushed Novelty leads to problems Novelty implemented
    

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84. Team Cohesion
    Trust
    Conflict
    Commitment
    Accountability
    Results
    Building trust requires
    vulnerability
    Healthy conflict implies
    candid debate
    Commitment
    follows healthy conflict
    To take
    accountability takes prior
    commitment
    Focus on delivering measurable
    results. Collective and individual
    accountability, and feedback
    

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85. Deployment
    frequency
    Four Key Metrics
    Lead Time for
    change
    Mean time
    to recovery
    Change failure
    percentage
    Stability
    Speed
    

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86. Empathise
    Service Health
    Check
    

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87. The intent of maturity models is usually
    benign… because “maturity” sounds a bit…
    well…. patronizing. Plus, most of our models
    don’t involve progressing through different
    levels, and the primary audience is the
    team itself rather than management.
    — https://labs.spotify.com/2014/09/16/squad-health-check-model/
    

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88. “Deployment is all automatic”
    “We security test git on push”
    “Commit to VCS and the customers have
    it in seconds”
    “Anyone can do a deploy!”
    Continuous Delivery Continuous Delivery
    “Deployment Joe is the only one who
    can do that”
    “That’s the security team’s job”
    “The customers get it a quarter later”
    “Only some people can deploy”
    “We find out direct from our users by...
    “Our kanban board shows all the work,
    and where it is”
    “Our stories usually last no than half a
    sprint”
    "We weren’t sure so we ran an
    experiment”
    Product & Process
    Product & Process
    “I don’t really know what our users
    think”
    “Sometimes work comes from the
    backlog except...”
    “Sometimes stories last multiple sprints”
    “That’s the way we’ve always done it”
    Insert here....
    “Good statement”
    “Another good statement”
    “Good thing number three”
    “Fourth good thing”
    “Number five in the list of things that are
    good...”
    Insert here....
    “Bad statement”
    “This thing is bad too”
    “Terrible, terrible, bad thing”
    “Bad thing which is the norm, everyone does
    it but really shouldn’t”
    “Bad thing that we didn’t even know was
    bad”
    “Rather than a sign off process we pair
    program”
    “The app gathers metrics and decide
    what’s next”
    “Our checks spotted the problem before
    our customers DID”
    “We only take on one thing at a time”
    Lean Management & Monitoring Lean Management & Monitoring
    “Oh we need to wait for CAB before we
    release”
    “No idea how the business decides what to
    do next”
    “The customer reported it”
    “We’re constantly doing 7 or 8 things”
    “It was a week before we even noticed”
    Code Quality
    “Absolutely everything is in source
    control”
    “We automatically test on every
    commit”
    “There’s only really the master branch”
    “I can add as many or as few examples
    as I need”
    Code Quality
    “It’s in source control except...”
    “We manually have a look”
    “Our branches are around forever and
    there are loads of them”
    “...but the data wasn’t like that on prod”
    

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89. Value Stream Mapping
    Service Blueprint
    Empathise
    

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90. Value Stream Mapping
    CUSTOMER
    CREATE
    STORY
    ---------
    ANALYST
    DEVELOP
    FEATURE
    ---------
    ENGINEER
    DEPLOY 

    TO PRE
    ---------
    DEL MGR
    TEST
    FEATURE
    ---------
    LCO
    AUTO.
    DEPLOY
    LT: 1D
    PT: 1H
    C&A: 90%
    AR: 14%
    LT: 15D
    PT: 1D
    C&A: 20%
    AR: 7%
    LT: 2D
    PT: 30 M
    C&A: 80%
    AR: 3.5%
    LT: 14D
    PT: 10D
    C&A: 60%
    AR: 71%
    TLT: 32D
    TPT: 11D 1H 30M
    AC&A: 60%
    TAR: 35%
    

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91. Synthesise
    

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92. Ideate
    1-2-4-All
    

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93. Ideate
    Hypothesis Generation
    

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94. We believe
    Will result in
    We will know we have succeeded when
    
    

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95. What is our
    one priority?
    What do we
    need to learn?
    What is our
    riskiest
    assumption?
    

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96. Time Box
    Is the experiment
    timely?
    Can we get data faster?
    Would less data be
    sufficient?
    Metrics
    Qualitative or
    quantitative?
    Is it actionable?
    Is it Measurable?
    Fail Condition
    (If this happens, our
    hypothesis is clearly
    false!)
    Early Stop
    (If this happens, stop!
    Experiment is broken,
    retro!)
    Plan
    How will you collect the
    data?
    Is it Specific?
    Is it Achievable?
    Link to any supporting
    documents.
    

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97. Prototype
    

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98. Test (and share)
    

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99. Journal
    

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100. Many different ways
     Micro Journal
     Daily Journal
     Week Notes
     Blog
     Talks
     Ad-hoc
     

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101. Measure
     

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102. What is AWKSS?
     Awareness
     Willing
     Knowledge
     Skills
     Support
     

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103. AWKSS
     Awareness
     Willing
     Knowledge
     Skills
     Support
     1 2 3
     4 5
     

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104. AWKSS
     Awareness
     Willing
     Knowledge
     Skills
     Support
     1 2 3
     4 5
     

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105. Copying Ashley’s first week they designed an experiment to run on with
     their teammates
     

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106. x
     

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107. x
     

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108. View Slide

109. View Slide

110. Formulating it as an experiment made it easy get permission to fail
     (even with a terrifying boss)
     

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111. Focus on value
     

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112. Iterate and work out what works for that specific team
     

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113. This allowed them to crush all opposition
     

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114. And take over the world!
     

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115. And take over the world!
     

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116. 84
     Today I learned hopefully something
     I will test that by doing something
     I will know it works for me
     when measure
     shows change in reading
     

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117. Thank you!
     Come and say ‘Hi!’
     @armakunihq @benedictsteele
     

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118. Thank you!
     

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