Mystery, Magic, or Science?
I’m at ALE14 unconference in Krakow, enjoying lots of inspiring and engaging conversations. I have a special connection to this event and its people, as I organised the first ALE conference three years ago in Berlin. this is a draft post… written in a hallway while Pablo is waiting for me to come join him for dinner:
— pablo pernot (@pablopernot) August 21, 2014
Discussions about Science and Data
I’ve indulged myself into hundreds or thousands of discussions about science and data and opinions, to find out what’s right and wrong, better or worse… An I’m tired of it. This is just a quick first sketch of my thinking, which is evolving in conversations here…
This post is bound to be full of generalisation and simplification. I think that’s ok. It’s kind of the point. (You’ve been warned.)
Data is from the Past
The future is unknown. The value of data from the past to learn something valuable for the future becomes less probable with every new connection humankind makes… Uncertainty is increasing.
Methods are Overrated
The conversation that triggered this happened last night, with my dear friend Stephen Parry. He spend years having research done at universities to find out which of the methods he used actually made a difference. The (much much simplified) bottom line is this: it’s him who makes the difference, not the methods.
Human beings feel invited to change when they feel seen and understood. When they receive empathy and compassion. Being present with my clients makes the difference. It doesn’t matter at all if I use Scrum or Kanban, the difference between these methods is very small if it’s me using them. It’s me being present making the difference.
Context is King
Science is based on the assumption something (a method, for instance) can be researched in experiments and that outcomes of these can be applied to different contexts. Sometimes that works, and it’s valuable. Sometimes it doesn’t – I think we need to focus less energy on that than we currently do. What works in 99 places might not work in the next one. That’s what uncertainty is all about…
Models are Linear
We need models and categorisations to be able to talk, to think, to make sense, to communicate and engage in dialogue. They need to be right or wrong, they just need to be helpful. Linear models (Tuckman comes to mind) of non-linear phaenomena (like team-building) are necessarily simplifying reality—and that may or may not be helpful for you, depending on what you want.
What We Don’t Know
Magic is something i don’t (yet) understand. I’m in awe…
What We Can’t Know
The meaning of life, the importance of purpose, the diverse human strategies to meet our needs and wants… Too contextual, too specific and unique for science and data. Interesting for dialogue, for listening and understanding. This is where we want to focus if we want to make a difference..