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Speaking out loud

  • Process Modelling
  • Software Design
Reading silently will make everything look plausible. Speaking in front of an audience will improve the quality of your model.

Our brain is a lazy machine. It will try to skip deep analytical thoughts in favour of quick pattern matching whenever possible. In practice - especially when we're running out of energy - it will take shortcuts and pretend we understand something even if we don't.

Like most of our brain's loopholes, the problem is that we won't be aware of our brain taking a shortcut. Or worse: we might be annoyed by this pedantic need for precision on a trivial issue and call a "Come on! Let's not waste our time on this one!"

Therefore

If you indulge in shortcuts, maybe a break will be necessary (see also Manage energy ). But if you want to increase the precision of your collaborative modelling session, then you can call your brain's verbal ability to the rescue. Speaking out loud, describing the steps of our model in detail, and, better yet, touching the sticky notes that describe the actual steps, is a great way to detect inconsistencies in your modelling artefact.

What looks plausible in silent skimming becomes embarrassing if you engage in vocal storytelling in front of your peers.

When you do that, you'll also exercise the part of your brain whose responsibility is avoiding looking or sounding stupid in public or saying something you'll regret later. You'll be surprised how this practice will trigger many corrections and more specific tricks.

A big part of the effect is correcting the story before saying it : the audience raises the bar of the precision needed in your storytelling, and your current storytelling doesn't click. So you have to correct the building blocks of your story before making it public.

The remaining effect is delegated to the audience: the moment something is officially spoken, you may expect somebody to say, "Wait a minute, this isn't always true." And then add the missing part of the story.

Warnings

The underlying mantra is that we will work on the wrong model most of the time. It's not about the results, it's about the journey. The model will improve step by step towards robustness and maybe perfection. But if you are a perfectionist, be ready to have some annoying conversations. You can't model a problem right from the start. Rush to the goal can give you a baseline quickly, but then you'll need improvement rounds.

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