Pop the bubbles, create a learning ecosystem
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Pop the bubbles create a learning ecosystem

The population of Detroit doubled between 1910 and 1920, it went from half a million to a million. The key driver of the growth was the automobile industry, and the majority of the new recruits were immigrants from Italy, Russia, Hungary and other parts of Eastern Europe (ref).

A 1915 survey showed that the workforce in Highland Park Ford Plant spoke more than 50 languages.

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Source: Google Maps

Highland Park Ford Plant produced the legendary Model T (ref) which is arguably one of the greatest engineering masterpiece of its time. So I wonder how was it possible for workers who did not share a single common language to work together to assemble this complex, sophisticated vehicle.

As paradoxical as it sounds, the answer is they did not have to. There was no need to work together, to discuss, to share information and learnings.

Assemblers worked in perfect division of labour, their task was to put nuts and bolts in the parts. The fact that they could not speak to their fellow assemblers had no impact on the productivity of Ford’s assembly line. This was the world of Taylorism, the world of Scientific Management.

As a stark contrast, today, 100 years later working in silos, not being able to collaborate, share learnings has a direct negative impact on productivity and organisational outcomes. We are living in the era of the knowledge workers.

“Our brain loves patterns … culture is a bunch of patterns, patterns of behaviours” Dr Steve Robbins

I will share a number of anti-patterns that experience and research shows are hindrance to the creation of a learning organsiation and corresponding patterns.

Anti-pattern 1: Information and Learning Siloes

“I shall reconsider human knowledge by starting from the fact that we can know more than we can tell.” Mihaly Polanyi, tacit knowledge

As Mary and Tom Poppendieck pointed out in their book: Implementing Lean Software Development: From Concept to Cash. Addison-Wesley, 2006. (ref) a great amounts of tacit knowledge remain with the creator and never get handed off to the receiver. They suggest that taking a conservative estimate, up to 50% of the knowledge is lost by each hand-offs.

It makes it even worse that the knowledge loss is compounded.

So considering a usual software delivery through analysis, design, code and test role based silos, this would translate to ~87% of knowledge loss by the 3rd handoff.

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Yet most software delivery set-ups, solution delivery life cycles are based on role based silos.

Anti-pattern 2: Outputs over Outcome

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Stuck in the Busy Trap. Photo: Zsolt Berend, Time Square, NYC

Modern corporate culture glorifies “busyness” as Tim Kreider calls it we stuck in the ‘busy’ trap. Since I read Tim Kreider’s blog I keep testing the audience at conferences with the question of what is the standard response they get when they ask a colleague: how they are doing, how is work? The most significant words in the world cloud of the responses are always: ‘busy’ and its synonyms.

This behavioural pattern is a legacy of 19th century Taylorism when there was a linear, direct correlation between “busyness” and organisational outcomes which is unfortunately still applied in the knowledge workers’ world.

Anti-Pattern 3: Bubble Effect

Human beings are social animals according to Aristotle, the great ancient Greek philosopher.

We also follow tribal behavioural patterns.

“Humans are tribal. We need to belong to groups. We crave bonds and attachments, which is why we love clubs, teams, fraternities, family. Almost no one is a hermit. Even monks and friars belong to orders. But the tribal instinct is not just an instinct to belong. It is also an instinct to exclude.” Amy Lynn Chua, American lawyer, academic and writer.

This promotes the creation of social bubbles whether consciously or unconsciously.

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Photo by Sharon McCutcheon on Unsplash

We can observe various forms of bubbles within organisations. Role based silos, agile, scrum, kanban teams, long lived value stream aligned teams etc. Besides the positives of these social structures they all share some undesired effects. They all suffer from the bubble effect:

Silo mentality

People, teams within the bubble develop silo mentality a mindset of wanting to protect and not to share information with others within the same company.

Limited discoverability

Are we living in a Universe or Multiverse? Learnings, knowledge, information outside the bubble can not be reached, discovery is limited.

Poor learning retention

Since the bubbles are disconnected, when people leave the company the learnings are lost forever

Duplication

Same ideas could be developed, same impediments could be tackled, duplication of work, inefficiency.

Anti-Pattern 4: Deterministic Culture.

“God does not play dice” — Albert Einstein

It is hard to argue with a statement when it comes from one of the greatest minds in human history. However as it happens sometimes even Einstein made mistakes. He did not believe in the non-deterministic nature of the world of atomic particles. This world is ruled by Heinsberg’s uncertainty principle, nothing can be certain, things, e.g. speed or location of particles can only be described as probabilities.

Behind the deterministic mindset lay the fear of uncertainty. The fear of not being able to tell when things are going to get done, to estimate the length of the delivery pipeline. This leads to lengthy up-front planning process, big upfront design and architecture.

This promotes command and control style leadership. Leaders who exhibit these behavioural patterns want deterministic plans with all milestones identified, agreed upfront and strong control over the execution.

However as Jonathan Smart points out in his blogAgility: Build the right thing

“You don’t know when you’re going to reach a milestone. The last 20% of the journey might take 80% of the time. There might be roadworks, an accident or a flooded river crossing. You only know you’ve reached a milestone when you reach a milestone. And even then, given your intent, you might find that it’s not the optimal place to be.”

These behavioural patterns lead to no or limited space for learning for individuals and for the whole of the organisation, promotes ignorance outside the very narrow specialization.

The quote below from Frederick Winslow Taylor: Scientific Management gives a sobering picture of this approach.

“In our scheme, we do not ask the initiative of our men. We do not want any initiative. All we want of them is to obey the orders we give them, do what we say, and do it quick.”

Todays’ theory X leadership style has strong roots in Taylorism. “This management style assumes that the typical worker has little ambition, avoids responsibility, and is individual-goal oriented.” Source: Wikipedia

Anti-Pattern 5: Weaponised Metrics

“When a measure becomes a target, it ceases to be a good measure.” Goodhart’s law

Theory X leaders fear data or at least data that show that they will not reach a set target. The infamous RAG status lead to the watermelon effect:

Green, Green, Green …. and just days or very close proximity of the go live it goes

Red overnight.

Such fear promotes faking certainty, gaming metrics. Targets are reached, by faking them.

As an example, some NHS Hospital Trusts introduced Hello Nurses to meet the targets of seeing patients quickly in the emergency department. Yet patients left waiting for treatment for hours. ‘People have got around this by achieving the target, rather than the spirit of it, which is that those who need attention receive it as quickly as possible,’ says Gordon Mitchell, deputy chief executive of medical assessment charity Health Quality Service (ref)

Pattern 1: Optimise for Learning

As pointed out in Anti-pattern 1: Information and Learning Siloes handoffs lead to loss of information, silos prevent free uninterrupted flow of knowledge, shared learnings.

In order to optimise the flow first we need to install transparency, to make queues, handoffs points, bottlenecks, impediments visible.

This can be done using value stream mapping. Through a positively intense couple of hours and lots of postits teams produce a map of all steps, activities the work is going through from concept, through business case, analysis, design etc. all the way to production. This is an awesome learning exercise for the entire team, which results in a number of impediments and improvements to act on.

One of the default results is the need to shift activities left, to start earlier and to collaborate more.

Collaboration is a true game changer. You can have cross functional team that covers all necessary skills to deliver value to the customer. Yet they still work in role based slios.

It only changes when individuals start working together, pairing up, swarming on the same work. This leads to developing new skills and eventually breaking specialisation, breaking silos. Such teams go from having deep and narrow specialists, I-shaped to having team members with broad and multiple specialist skills like T, PI, Comb-shaped.

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Breaking specialisation through developing new skills

“Under certain conditions, matter will spontaneously self-organise.” Jeremy England, theoretical physicist, studying the origins of life

Teams and individuals require autonomy and empowerment, the right leadership support to self-organise, collaborate, develop new skills, break specialisation and with that to create shared learning ecosystem.

Pattern 2: Nested learning with built-in feedback loops

“Ironically, jobs are actually easier to enjoy than free time, because like flow activities they have built-in goalsfeedback rules, and challenges, all of which encourage one to become involved in one’s work, to concentrate and lose oneself in it. Free time, on the other hand, is unstructured, and requires much greater effort to be shaped into something that can be enjoyed.” Cal Newport, Deep Work

Continuous learning

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Nested Learning with built-in feedback Loops

Install learning by creating a routine, kata, habit for learning in a nested cadence. Sessions like daily stand-ups, coordination, pairing-ups, weekly planning, replenishment, retrospectives, monthly show and tells, open spaces, unconferences, internal meet-ups, yearly conferences are all good examples of feedback-loops on ways of working, behavioural patterns, team policies, routines, mental models.

Dailyweeklymonthly releases into production, quarterly OKRs, business outcomes driven insights, leading indicators, team shared objectives, yearly portfolio objectives, lagging indicators, multi-year company level strategic objectives are all good example of cadences to learn whether the RIGHT things get delivered.

Once we establish these routines of seeking and getting feedback the next building block is to use these learning to improve.

Continuous improvement

Experiment with lean principles and practices for continuous improvement.

One powerful practice is the Improvement Kata. It is a routine for moving from a current state to a new desired state iteratively, which uncovers obstacles that need to be worked on. Below is a canvas where on the left there are the current and future states supported by data and articulation of leading and lagging indicators. On the right hand side on the top is the next target state and the bottom right is a Kanban board with the steps to do to reach the next target state.

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Create based on Jimmy Janlen and Mark Williams

Pattern 3: Over Communication

Pop the bubbles, destroy the fantasy of living in an isolated world and with that all the undesired behavioural patterns.

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Conferences, UnconferencesOpen Space

Open some space, and Spirit will certainly show up. Allow the magic of self-organization to work for you, and the complex adaptive system that we are will find its own power.” Harrison Owen, civil rights activist, developed the idea of Open Space Technology in 1980.

Invite people across the organisation to openly share their topics of interest and co-design, co-develop the ideas. Unconferences are great format to run these sessions.

Organise internal conferences and open up for internal and external people to speak, share their knowledge and experience reports.

Awards

Sharing success stories is a great thing, collecting them though is not an easy thing due to:

  • people are ‘busy’ as we stuck in the busy trap
  • discoverability is limited as we live in our own bubbles so we do not know the teams that could submit a success story
  • takes a long time to write, review and approve the content as teams and leaders are suffering of silo mentality not wanting to share knowledge as they like living in their bubble

The major problem is ultimately that this is a push approach.

So instead, flip it around and generate pull. Run awards. E.g. an annual agility awards open to all teams agnostic whether IT or non-IT and size. You will get a flood of submissions, rich in content and data. Besides awards and celebrations which attracts a lot of people these submissions are gold source for sharing in webinars, show and tells, roadshows, conferences, internal meetups.

Rotation, Secondment, Mobility

Accelerate up-skilling and sharing knowledge by rotating people between teams. Provide discoverability of skills and experiences through making CVs, interests, experiences searchable across the organisation. Encourage internal mobility.

Psychological Safety

“Being able to show and employ one’s self without fear of negative consequences of self-image, status or career”, Kahn, 1990

Establish an environment of physiological safety so people can bring themselves to work, learn, develop without fear.

Centre of Enablement Fractal

Establish fractal or hub and spoke like distribution of small centre of enablement teams of change agents and coaches to nurture the culture, explore and connect the bubbles.

Communities of Practice (CoPs)

Establish global community of practices with local chapters. Have local ‘community organisers’ mobilise and run activities based on common interest, like in the form of conferences, unconferences. Establish channels for sharing and discussing topics like internal chat channels, file sharing spaces, webinars and events etc.

Pattern 4: Be comfortable with uncertainty

So God does play dice with the universe. All the evidence points to him being an inveterate gambler, who throws the dice on every possible occasion.” Steven Hawking (ref)

We live in a world of uncertainty combined with volatilitycomplexity and ambiguity (VUCA). In order to navigate in this world we need to develop an growth mindset. A mindset of embracing trial and error culture, defining and running experiments and using the learnings to define the next set of experiments.

According to Carol Dweck’s research, employees in organisations that encourage growth mindset are 47% likelier to say that their colleagues are trustworthy, 34% likelier to feel a strong sense of ownership and commitment to the company, 65% likelier to say that the company supports risk taking, and 49% likelier to say that the company fosters innovation.

Leaders with growth mindset encourage learning loops, experimentation.

As Stanley McChrystal describes “Many leaders are tempted to lead like a chess master, striving to control every move, when they should be leading like gardeners, creating and maintaining a viable ecosystem in which the organization operates.”

Pattern 5: Metrics for Learning

“If a measurement matters at all, it is because it must have some conceivable effect on decisions and behaviour. If we can’t identify a decision that could be affected by a proposed measurement and how it could change those decisions, then the measurement simply has no value” Douglas W. Hubbard, How to Measure Anything: Finding the Value of “Intangibles” in Business

Pivot from output to outcome metrics

Measuring outputs #specifications, #enhancements, #features encourage the wrong behaviour to create more and more which leads to busy trap. It does not bring any insights, learnings.

In a conference, one speaker described proudly how his company had delivered more than 1,500 features the previous year. What the speaker didn’t detail was how many of those features were used. He didn’t say how many were creating revenue or what impact they had had on the organisational performance. Outputs are always much easier to define, monitor, and track than outcomes.

Outcome metrics on the other hand are articulated with corresponding leading, launch and lagging indicators. They are designed to help sense, generate insights and learnings.

As described in Jonathan Smart blog: Focus on Flow, Quality, Happiness, Safety and Value

“For us, our desired outcomes are described as Better Value Sooner Safer Happier, each of which is measurable.”

  • Better => Quality (#incidents in production)
  • Value => bespoke to context of product and service (OKRs, Outcomes with leading, launch and lagging indicators, e.g. #visits, #complaints, %increase in market segment, #referrals, #activation, #retention
  • Sooner => Flow metrics (e.g. concept to learning, release frequency, throughput, flow efficiency)
  • Safer => GRC Control Compliance (e.g. InfoSec, Know-Your-Client, Data Privacy, GDPR type mandatory requirements). Speed & Control. Agile not fragile.
  • Happier => customer and colleague satisfaction, citizens, climate

5 Whats

Run workshops, open space to co-create a starter set for metrics. Follow the thought process, derivation of:

What do we need to measure? Any top strategic measures, KPIs?
What level do we need to aggregate? app, product, team, value stream etc.?
What do we want to measure?
Off of that what can we measure? We have data for.
Off of that what are the measures that are easy to measure? Small set.

Self-serve dashboard

Install transparency, create a self-serve dashboard for data consumption and drill down capabilities. Ideally the underlying data is auto harvested from the various sources across the value delivery pipe.

Correlations, insights

Data is gold. Use data analytics to draw correlations between data. E.g. between release frequency and number of defects.

Vector metrics, %s and trends

Avoid absolute numbers, use vector metrics, %s and trends as much as you can. This helps ease the fear of revealing data and have the right focus on analysing, decision making, learning from whether the lever we used brought the desired effects.

Summary

Pivot from disconnected learning bubbles to a learning ecosystem.

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Acknowledgements

Thanks to Jonathan Smart, Simon Rohrer, Tony Caink, Richard James, Jim Roberts

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