Managing Personal Knowledge? Steal This From a Data Engineer.
One core activity of any data engineer you should be implementing as a PKM aficionado.
Managing personal knowledge is very similar to what data engineers do daily. I see PKM as a homebrew version of my bread and butter.
Data only exists in two states: in transit and at rest. A data engineer’s work involves taking data from one place (at rest) and moving it (in transit) to another (at rest), often modifying it in between. This process is called ETL, which stands for Extract → Transform → Load.
Just like those engineers, knowledge managers can be considered functions with inputs and outputs, performing transformations on incoming facts and spitting out something of value, even if the value is exclusively for the knowledge manager himself.
Most of us are getting into PKM to achieve specific goals, such as writing, ideating, or remembering what we read, to name a few. Nobody ever invents anything, so to be able to “create”, you need to “consume” first.
There is no such thing as reader’s block. If you can’t write, it is because you have nothing to say. You have no ideas. In such a situation, don’t pride yourself on your writer’s block. Read something. If that doesn’t work, read something else – maybe something better. Repeat until the problem is solved.
–JORDAN BERNT PETERSON
Even if great artists steal, the result always has their signature cachet. That’s what we love them for. Not because we’re fond of the same four chords rearranged into different sequences. I previously wrote about the pleasures and pains of consuming more of the same here:
ETL For PKM
You’ll have a massive headstart if you set up knowledge ETL pipelines that work for you.
I have different implementations of such processes for different types of inputs and outputs, but they all follow the same structure:
Extract. Capture the thought, whether yours or someone else’s.
Transform. Filter the noise, connect to existing knowledge, enrich with personal experience, add an analogy or a metaphor, etc.
Load. Store the resulting, highly personal single-idea note in a knowledge graph or share it publicly.
The beauty of such systems is that you can stack them. The output of one pipeline becomes the input for another. This is, once again, what data engineers do: scraping web pages and storing results in a database in a structured form is only the beginning. Generally, this data turns into other ETL pipeline inputs to be transformed into something that benefits the customer.
The same will happen to your processed memos once stored in your knowledge graph as atomic notes. Records in this state are chef’s mise-en-place. The meal’s still to be cooked. Extract different thoughts, Transform them and Load them as a newsletter issue. ;)