Eating Complexity

We are all involved in the same activity:

Turning something we have into something we want.
  • Hourly work is turning time into money
  • Project work is turning products into money

There are many different types of work, but the same thing is always happening.
We have a raw material, and through our labour, it is turned into something with value.
  • A chef turns ingredients into a meal
  • Programmers turn coffee into code
  • Salespeople turn hot air into revenue

So what have we got?

In the world of data, it is tempting to think that our raw material is data.
But this is not true.
In the imperfect sense I am using it, data means just 'that we can store and access on a computer'

Our real raw material is complexity.
If we can eat complexity, we can sh*t gold.

What is complexity?

In this sense, complexity is not just data
- It is the context of the data as well
- It is domain knowledge
- It is general knowledge
- More than that, it is how all of these things relate to one another.

What does it mean to Eat Complexity?

  • Complexity can never be reduced, only offset.
  • Eating complexity means that you are offsetting the complexity for someone else.
  • Eating complexity means that you are taking something complex but giving out something simple

Offsetting Complexity

There are two kinds of complexity offsets
  • Complexity of input. Something complex goes in, something simple comes out
  • Complexity of output. Something simple goes in, something complex comes out. This is not really a desirable thing for data analysts. But it is certainly desirable if you are a chef, an artist, or artisan.

How can we Eat Complexity?

Complexity is big, how do we make it into smaller pieces?
There are two main ways.

1. Pattern Recognition.
Patterns are things that hang together when other things do not. Patterns can be spatial or temporal
Patterns are meanings, and meanings are patterns. If you can see a pattern, you can find a meaning.

2. Abstraction
As I understand it, there are two forms of abstraction:
  • Removing what is not needed. Editing things out
  • Adding a layer. Software for example. This can be to connect up parts that are not usually together, or to improve workflow.

In a sense pattern recognition and abstraction are the same thing, but seen from different angles.
They are both the search for meaning.
Pattern recognition is the search for meaning within something
Abstraction is the creation of meaning within something

And this is the crux of it all.
We want our work to have meaning.
We want our lives to have purpose.
With the right eyes, that can be found in anything.

Let’s talk!