How to be an Evil Scientist - DevOps 2020

33a8eda64dec30551fd0b474443e4b35?s=47 Armakuni
January 29, 2020

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.

33a8eda64dec30551fd0b474443e4b35?s=128

Armakuni

January 29, 2020
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Transcript

  1. armakuni.com DevOps 2020 London - January 2020 Benedict Steele How

    to be an Evil Scientist
  2. Why are we evil enough to be standing in front

    of you?
  3. How to be an Evil Scientist 1. Choose an evil

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

    name ✔ 2. Share the most evil thing you’ve ever done
  7. Not evil enough for you?

  8. Not evil enough for you?

  9. Not evil enough for you?

  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”!
  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
  12. Evil Scientist Rules

  13. Evil Scientist Rules Never get caught monologuing

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

    together
  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
  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
  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
  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
  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
  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
  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
  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
  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
  24. Our Arch Enemies Partially Completed Work Woman Dr Overly Complex

    Solutions The Siloed Worker Poor Visibility Man
  25. None
  26. Works unsociable hours

  27. Works unsociable hours Poor mentors

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

    mentors
  29. Refuses to collaborate or attend meetings Works unsociable hours Code

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

    structure is in their head Believe they don’t need training Poor mentors
  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
  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 ✔
  33. armakuni.com DeadlyOps 2020 London - January 2020 The Terrifying Werewolf

    How to be an Evil Scientist
  34. Ladies and gentlemen: the story you are about to hear

    is true. Only the names have been changed to protect the innocent.
  35. Department for Feline Empowerment

  36. This Dastardly Ashley

  37. Pair Programming Pipelines Test-Driven Development

  38. ?

  39. It worked!

  40. Superheroes were being defeated

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

    too
  42. So she did

  43. ...but she had demands!

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

  45. They were thwarted by super heroes at every turn

  46. And they all went to jail

  47. Thankfully Ashley always has an escape plan

  48. The future is already here - it’s just not evenly

    distributed — William Gibson
  49. Every team is different

  50. The Department for Feline Empowerment needed to go back to

    the drawing board
  51. Ashley’s first few weeks

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

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

    reality
  54. Worked out what the problems were and what potential fixes

    could be
  55. Tried them out one by one in real world villainous

    situations
  56. She then looked back to see if they worked

  57. What if Alex and Sam took the same approach and

    experimented with their teams?
  58. What does Ashley know about teams?

  59. None
  60. None
  61. TEAM Tools Process People

  62. None
  63. Evil Scientist Rules

  64. Evil Scientist Rules Never get caught monologuing

  65. Evil Scientist Rules Never get caught monologuing

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

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

    together
  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
  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
  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
  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
  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
  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
  74. Empathise Evil Scientist Rules Always start small Always measure everything

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

  76. Empathise Evil Scientist Rules Ideate Prototype Test Synthesise

  77. Empathise Design Thinking Ideate Prototype Test Synthesise

  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
  79. Stakeholder Mapping Team Metrics Empathise

  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
  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
  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
  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
  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
  85. Deployment frequency Four Key Metrics Lead Time for change Mean

    time to recovery Change failure percentage Stability Speed
  86. Empathise Service Health Check

  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/
  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”
  89. Value Stream Mapping Service Blueprint Empathise

  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%
  91. Synthesise

  92. Ideate 1-2-4-All

  93. Ideate Hypothesis Generation

  94. We believe <this capability> Will result in <this outcome> We

    will know we have succeeded when <we see a measurable signal>
  95. What is our one priority? What do we need to

    learn? What is our riskiest assumption?
  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.
  97. Prototype

  98. Test (and share)

  99. Journal

  100. Many different ways Micro Journal Daily Journal Week Notes Blog

    Talks Ad-hoc
  101. Measure

  102. What is AWKSS? Awareness Willing Knowledge Skills Support

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

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

    5
  105. Copying Ashley’s first week they designed an experiment to run

    on with their teammates
  106. x

  107. x

  108. None
  109. None
  110. Formulating it as an experiment made it easy get permission

    to fail (even with a terrifying boss)
  111. Focus on value

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

  113. This allowed them to crush all opposition

  114. And take over the world!

  115. And take over the world!

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

  118. Thank you!