How To Be An Evil Scientist

Billie Thompson, Abhishek Rajput, Tim Long, Armakuni - Agile Scotland October 2020

Transcript

1. armakuni.com Agile Scotland October 2020 Billie Thompson - Abhishek Rajput - Tim Long How to be an Evil Scientist
2. This is Dr Billie • Consulting Engineer for Armakuni • Quite tall • Helps people use best practices • Favourite animal is the capybara • Evil Name - The Terrifying Monster • Not a doctor
3. This is Abhishek.equals(Abhi) • Consulting Engineer for Armakuni • Tall but shorter than Dr. Billie • Loves to explore & experiment with Open Source Tooling by Incorporating in CI/CD • Evil Name - The Evil Creature • We stole this talk from her, turned it into a workshop and didn’t even say “thank-you”!
4. This is Tim • Consulting Engineer for Armakuni • Medium sized • Needs a haircut • Favourite animal is the Tibetan Mastiff • Evil Name - The Rancid Master
5. Our current & past victims
6. How to be an Evil Scientist 1. Choose an evil name
7. 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
8. 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
9. How to be an Evil Scientist 1. Choose an evil name ✔ 2. Share the most evil thing you’ve ever done
10. 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
11. 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
12. 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
13. 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
14. Our Arch Enemies Partially Completed Work Woman Dr Overly Complex Solutions The Siloed Worker Poor Visibility Man
15. 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
16. 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 ✔
17. What is AWKSS? Awareness Willing Knowledge Skills Support
18. AWKSS Awareness Willing Knowledge Skills Support 1 2 3 4 5
19. AWKSS Awareness Willing Knowledge Skills Support 1 2 3 4 5
20. armakuni.com How to be an Evil Scientist Abominable Scotland October 2020 The Terrifying Monster The Evil Creature The Rancid Master
21. Esteemed people of all genders: the story you are about to hear is true. Only the names have been changed to protect the innocent.
22. Department for Feline Empowerment
23. This Dastardly Ashley
24. Pair Programming Pipelines Test-Driven Development
25. ?
26. It worked!
27. Superheros were being defeated
28. She was asked to help Sam and Alex do it too
29. So she did
30. ...but she had demands!
31. They did exactly the same thing, but it didn’t work
32. They were thwarted by super heroes at every turn
33. And they all went to jail
34. Thankfully Ashley always has an escape plan
35. The future is already here - it’s just not evenly distributed — William Gibson
36. Every team is different
37. The Department for Feline Empowerment needed to go back to the drawing board
38. Ashley’s first few weeks
39. Ashley didn’t monologue - she listened and observed
40. Found the pain points and the gaps between vision and reality
41. Worked out what the problems were and what potential fixes could be
42. Tried them out one by one in real world villainous situations
43. She then looked back to see if they worked
44. What if Alex and Sam took the same approach and experimented with their teams?
45. What does Ashley know about teams?
46. Team Tools Process People
47. How do you create sustainable change?
48. Empathise Design Thinking 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 Ideate Prototype Test Synthesise
49. 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
50. Willing Vict… Volunteer
51. Stakeholder Mapping Team Metrics Empathise
52. 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 PMO CEO SA Ops Data PO BA UX EA Dev QA
53. Empathise Service Health Check
54. 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/
55. “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.... “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”
56. 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
57. Synthesise
58. Westrum typology to define 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
59. 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
60. 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
61. Deployment frequency Four Key Metrics Lead Time for change Mean time to recovery Change failure percentage Stability Speed
62. Value Stream Mapping Service Blueprint Synthesise
63. 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%
64. Ideate Hypothesis Generation
65. Ideate 1-2-4-All
66. We believe <this capability> Will result in <this outcome> We will know we have succeeded when <we see a measurable signal>
67. What is our one priority? What do we need to learn? What is our riskiest assumption?
68. 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.
69. Prototype
70. Test (and share)
71. Journal
72. Many different ways Micro Journal Daily Journal Week Notes Blog Talks Ad-hoc
73. Measure
74. Copying Ashley’s first week they designed an experiment to run on with their teammates
75. x
76. x
79. Formulating it as an experiment made it easy get permission to fail (even with a terrifying boss)
80. Focus on value
81. Iterate and work out what works for that specific team
82. This allowed them to crush all opposition
83. And take over the world!
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
85. Thank you! Come and say ‘Hi!’ @armakunihq @purplebooth
86. Thank you!