How to be an Evil Scientist - SEAM 2019

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How to be an Evil Scientist Study Of Enterprise Agility Meetup London - October 2019 Benedict Steele - @benedictsteele

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How to be an Evil Scientist 1. Trick them into doing an evil laugh

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Why am I evil enough to be standing in front of you?

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How to be an Evil Scientist 1. Trick them into doing an evil laugh 2. Give them all an evil name

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Your first name A The Evil N The Crazy B The Mad O The Iron C The Big P The Poison D The Dangerous Q The Bloody E Captain R The Dark F The Ghostly S The Dangerous G Professor T The Rancid H Doctor U The Invisible I Phantom V The Black 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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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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How to be an Evil Scientist 1. Trick them into doing an evil laugh 2. Give them all an evil name 3. Find out the most evil thing they’ve ever done (you might have to go ﬁrst)

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This is Billie ▪ Consulting Engineer for Armakuni ▪ Quite tall ▪ Helps people use best practices ▪ Favourite animal is the capybara ▪ I stole this talk from her, I didn’t even say thank-you

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Not evil enough for you?

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How to be an Evil Scientist 1. Trick them into doing an evil laugh 2. Give them all an evil name 3. Find out the most evil thing they’ve ever done (you might have to go ﬁrst) 4. Tell them about the rules every evil scientist must follow

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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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Our Arch Enemies The Waiting Around Kid Captain Defect General Heroics The Crimson Handoﬀ Gold Plated Features Girl Mr Unneeded process

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Our Arch Enemies El Manual Work Awful Comms Boy Knowledge Drain Man Constance “Task” Switching The Relearner Rework

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

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How to be an Evil Scientist Scary Evil Alchemist Meetup London - October 2019 The Mad Werewolf - @benedictsteele

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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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Cat Paw Inc.

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This Dastardly Ashley

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Pair Programming

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Pipelines

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Test-Driven Development

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Team

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It worked!

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Superheros were being defeated

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She was asked to help Sam and Alex do it too

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Sam and Alex have their own teams, and badly want to take over the world too

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So she did

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Demands!

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

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They were thwarted by super heroes at every turn

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And they all went to jail

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Thankfully Ashley always has an escape plan

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“ The future is already here — it's just not very evenly distributed William Gibson

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Every team is different

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Cat Paw Inc. needed to go back to the drawing board

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Alex and Sam ask Ashley to talk about how she decided what to do with her team

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Ashley’s first few weeks

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Ashley didn’t monologue - she listened and observed

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Found the pain points and the gaps between vision and reality

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

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Tried them out one by one in real world villainous situations

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She then looked back to see if they worked

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

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What does Ashley know about teams?

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TEAM Tools Process People

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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

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Design Thinking Ideate Prototype Empathise Test Synthesise

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Empathise Stakeholder Mapping Team Metrics

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“ 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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Westrum typology to measure culture Pathological (power-oriented) Bureaucratic (rule-oriented) Generative (performance-oriented) 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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The 5 dysfunctions of a team Inattention to RESULTS Avoidance of ACCOUNTABILITY Lack of COMMITMENT Fear of CONFLICT Absence of TRUST To take accountability takes prior commitment Focus on delivering measurable results. Collective and individual accountability, and feedback Commitment follows healthy conﬂict Healthy conﬂict implies candid debate Building trust requires vulnerability

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

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Define Value Stream Mapping Service Blueprint

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Define Service Health Check

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“ 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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Code Quality Code Quality

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Continuous Delivery Continuous Delivery

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Architecture Architecture

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Product and Process Product and Process

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Lean Management and Monitoring Lean Management and Monitoring

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Ideate 1-2-4 All Hypothesis Generation

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Prototype

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Protype Learning Goal

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Learning Goal ▪ What do we need to learn? ▪ What is our riskiest assumption? ▪ What is our one priority?

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Hypothesis/Assumptions ▪ Is it falsiﬁable? ▪ Is it speciﬁc? ▪ Is it causal? (eg. If X the Y?) ▪ Is it relevant to the learning goal?

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Experiment Idea to Hypothesis We believe Will result in We will know we have succeeded when

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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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Test (and share)

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Journal

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Measure

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AWKSS Awareness Willing Knowledge Skills Support

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AWKSS Awareness Willing Knowledge Skills Support 1 2 3 4 5

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AWKSS Awareness Willing Knowledge Skills Support 1 2 3 4 5

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

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