Think small – get it done

Our society is growing from a functionally structured to a network organized one. While we needed spezialization to achieve the wealth we experience today we need this change to happen to master the challenges that lie ahead. Climate crisis, poverty, decreasing economies hit mankind globally and communication between experts is to cumbersome to meet these threats. In a network organized society knowledge is easily accessible and equally distributed so that individuals meet and organize in networks. They come together to solve a specific task. If they are successfull they associate for longer, if not they look out for new „neurons“ to align with. Everyone can be a member of different networks, depending on the task he/she has to solve.
Look into the sky between some skyscrapersIn such a network organized society a part (neuron) with too much weight would lead to a malfunction.

Can big players solve our problems?

What does this mean for our IT business? Currently it is still dominated by the big ones. Software vendors and Consultancies still try to match the huge industries they find out there. In classical terms this is just a logical consequence of the functionally organized society. To put it in terms of Luhmans system theory: to be able to connect, systems need to trust each other. Resembling systems tend to trust each other more likely than others. So no wonder that IT departments from big players in industries tend to hire big players in the software business.
Can these big players really solve our problems? Looking at the change we experience during our shifting to a network-organized society, I would say: no. All they have to offer are highly specialized experts trying to solve a small part of a problem using prefabricated solutions. This was – as stated – the right answer during the phase of industrialization where scaleability in the real world was a prerequisite. Scaleability in the digital world is not an issue. Adaption of solutions to real world challenges is. To do so the understanding of a challenge is a crucial part of it. Finding a solution usually involves finding different perspectives to the problem. And this is granted by building networks. Instead of spending millions for a consultancy or a software solution to solve your problem you think of your network and the people who may have solved a similar problem before. And they might even think of further people in their network which again may solve a different aspect of the problem. For a certain period of time a part of the network is building an efficient association. Certain people in this association will communicate al lot, other a bit less. Sometimes these networks maintain stable to solve even more problems. But the important characteristic is, that the network is stablized by communication, not by functionality or hierachy.

Data Science is based on networks

Sleigh dogs
The reason I like Data Science is founded exactly here: A Data Scientist is thinking in networks, not in functionalities. She/he encounters problems in a specific environment. To solve them she/he has methods but to grasp an understanding and the possible solution she/he needs other people, independently from function or hierachy. Possible solution can involve so many aspects like production processes, finance, technology, architectures and even political aspects. This can’t be encountered with classical approaches from IT we learned thirty years before.
In smaller companies you grow up learning to think in networks. The challenges are many the ressources not. So you start to create those networks and start this network thinking. We started to break down our huge software solutions into smaller microservices and organized them in containers. Maybe it is time to do raise this way of thinking to a general paradigm. If you are afraid, that your billion-dollar-project may fail, why not start to break it down in smaller ones and share whith others. It’s not Think Big – start small but Think small – get it done.

(c) Photos: https://deathtothestockphoto.com/

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