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Seeing Brains

I love the comments I get from people who see a Causal Decision Canvas for the first time.

Once they get to grips with the cause-effect concept they start exploring the Canvas. They’re drawn into the story of the whole system, the dynamic structure, the feedback loops.

They ask questions: What does it mean when that circle or arrow is bigger, fatter, brighter? The Causal Decision Canvas Anatomy helps to answer these questions.

Then people see brains. The shapes formed on the causal canvas literally look like human brains (well, apart from the one which looked like a mouse’s head).

This is the perfect visual metaphor because the pictures emerge from our mental models which are usually trapped, unseen, inside our heads.

Seeing a picture makes something more concrete. Our strategies have taken shape before our eyes; we can see their form with renewed clarity.

Usually though, the early versions of a Causal Canvas are a mess. We have to acknowledge and embrace this complexity because it naturally reflects the myriad ways people think about the world. But we can then start to tame that complexity with visual analytics.

We can get rid of the ‘noise’ and just retain the most influential paths and loops. We can emphasise parts of the model to draw out themes. We can see where we’ll get the most ‘bang for our buck’.

We can point to the most important decisions and outcomes to show where we need to reduce uncertainty or design new performance measures. We can re-cast these parts of the model for more formal problem analysis, or a business case.

The model requires us to express claims, opinions, beliefs, variables and probabilities rather than commit to certainties. The dialogue revolves about the ‘structure’ of the causal model rather than a reflection or judgement of individual positions.

This gives us a new, visual vocabulary. We can articulate purpose, vision, mission, goals, objectives, strategies, decisions all in one place. We can show how investments, initiatives, programmes, projects, interventions could change something for the better.

With this big picture in front of us we can think more clearly and communicate more clearly. In a world of noise, both inside and outside our brains, that’s something worth seeing.

What does Transformation mean to you?

‘Transformation’ must be near the top of the list of most overused business language.

Transformation sounds so grandiose, so important, so necessary and yet at the same time has no universal meaning. Could the hype hitched to this bandwagon even devalue the work of people actually changing the world for the better?

Everything ch-ch-changes. We might be trying to change a Thing. Things are changing around us. If the states of changed ‘Things’ now look very different, they’re transformed. Very Hungry Caterpillars turn into Beautiful Butterflies. Customers now enjoy contacting their bank or broadband provider. People well enough to leave a hospital bed do so, pronto. These are Things in a specific, different state.

‘Transformation’ is vague. Its rarely qualified with a definition of the ‘Thing’ being changed or what the new state would look like. That vagueness only obscures the relative priorities of Things and the evidence of their states.

Is the Thing an organisation chart, new value propositions, the flow of work? And why? To grow revenues, cut headcount, appease shareholders? Such uncertainty surely does its own damage; ‘Yeah, the last transformation [insert fear]’.

It might have been W Edwards Deming who said: ‘There is no such thing as improvement in general’. Can there ever be transformation in general? Something very specific has to change at a process behaviour level to shift an important outcome. What is the cause-effect logic of that hoped-for chain reaction?

Is investing in a big, bold transformation programme the best way to make our most important ‘Things’ better? Can we be so confident until we understand what matters, and what works? Does changing too much dilute the focus, create new systemic shocks? Is change really an initiative, a project, or is ‘getting better’ more of a daily mindset?

So, what does Transformation really mean?

The beauty of a Causal Decision Canvas is that we can take a vague word – like Transformation – and articulate it in an entirely visual way. Whether the material comes from existing words or a workshop of stakeholders, we can distill what ‘Transformation’ really means in each situation. This reveals the strategic hypotheses, the priorities, the decisions and the evidence needed to describe the Thing and its result state.

Now we can see the Thing, why changing it matters and whether it will fly.

Counting What Counts

Over Christmas the kids decided to count stuff in the house.

Perhaps they were bored with their Christmas presents already or were just missing school. Who knows?

Is there something we can learn from the experience of a five and seven year old about measuring things?:

I think so:

  • Error – Different measurement instruments (er, kids) produced different counts.
  • Conflict – These different counts, versions of the truth, produced arguments and recounts.
  • Scope – Some categories of things got counted, some didn’t.
  • Investment – Counting took time and effort.
  • Activity – There was a focus on how much busy counting had been done.
  • Shortcuts – The things that were obvious, easy, visible – coats & shoes – got counted.
  • Persistence – Whole rooms, cupboards, drawers got missed off when the novelty waned.
  • Consistency – There were differences in how the counts were recorded. Conflicting categories, different handwriting, fragments of paper.
  • Interpretation – The final result was a list of words and numbers. No visual comparisons, relative importance, change over time, story.

Above all, I was reminded of Cameron’s* quotation (often attributed to Einstein):

“Not everything that can be counted counts, and not everything that counts can be counted.”

This counting effort had no decision-making intent or ultimate purpose. In other words it had no practical information value. (Sorry kids, the truth hurts).

Yes, the pursuit fulfilled an intrinsic motivation to count things. It satisfied a curiosity. It presented a challenge. It mastered skills. It was teamwork. It was competition. It completed tasks. It ticked boxes. It passed the time. It was fun.

These things aren’t the same as decision value.

We could try to justify the decision value in retrospect; a choice between 8 or 10 coat pegs or a trip to the charity shop. But most of the measurement investment was simply a sunk cost with a near-zero return.

The big lesson is that just measuring stuff is often wasteful and doesn’t, by itself, lead to better decisions. This should caution us against conflating ‘Measurement Activity’ with ‘Measurement Value’. Or ‘Big Data’ with ‘Useful Data’.

How much of what we’re counting right now is waste? Does this data truly help us to make objectively better decisions? Could it be leading us – with anchoring or algorithmic blindness – toward worse ones?

We should challenge ourselves. Instead ask: “Why are we measuring this?”, “What choices will be made better?”, “How is it aligned with a goal, our purpose?”.

As we see in another New Year, maybe we could resolve to measure what really matters for making decisions. Out with the old way, in with the new.

Maybe then, our decisions can change the world for the better.

*1963, Informal Sociology, a casual introduction to sociological thinking by William Bruce Cameron, Page 13, Random House, New York.