The Job Description That Reads Like an FCO-IM Feature List
Something quiet is happening in enterprise job postings. Look at the roles being opened right now for information architecture — the responsibilities read almost like a description of a modelling method invented in the Netherlands in the 1990s.
The specific employer is not the point. The vocabulary is.
Words like: AI-ready, trusted, context-rich, semantically aligned, discoverable, reusable, governed.
Keeping the Chimney Smoking
A quiet tribute to the modelers and information architects who keep FCO-IM working — day to day, year to year, decade to decade
There is a Dutch saying — de schoorsteen moet roken, "the chimney must keep smoking." It is what you say about the unglamorous, essential work that keeps a household fed and a business running. Not the launch, not the keynote, not the clever idea on a whiteboard — the steady, faithful labour that means the lights are still on next winter.
This article is about the people who keep the chimney smoking for the information side of an organisation: the modelers and information architects who took FCO-IM not as a theory to admire, but as a practice to live by — for years, and sometimes for decades. They rarely get written about. So this is for them.
Meaning Is Not Metadata
An Afterthought to Against the Stream
TL;DR — Metadata is descriptive and secondary — it points at data that already exists. Meaning is constitutive and primary — it precedes data and is what data should have been derived from. The semantic layer treats meaning as an annotation, when it is actually a foundation. You cannot retrofit a foundation. If you want AI to reason about your domain rather than hallucinate about it, you need a domain model constituted from meaning, not annotated toward it.
Some arguments need time to settle before their full weight becomes clear. Against the Stream made the case that most business-language modeling artifacts flow in the wrong direction — they enter the river midstream, assume vocabulary is already established, and call the resulting drift alignment. The argument was about directionality. But a question has been nagging since: why does the direction matter so fundamentally? What is it, precisely, that gets lost when you start from the wrong end?
The answer is deceptively simple, and it changes everything.
What We Lost When We Fed the Machine
We ask AI to find meaning in our data — but the authentic story was gone before it ever looked
In What We Lost When Datum Became Data, I traced how a word that once meant "something given" shed its own etymology and became a synonym for "values in a system." Along the way, the who, the why, and the context quietly disappeared. I used the Groningen definition, the Employee-Person example, and the metadata stack to show where those losses happen.
That argument had its own reasons. It has sharper ones now.
What we lost when Datum became Data
A Word That Carried Its Own Philosophy
In Dutch, the everyday word for data is gegevens. It is not a technical term. Any citizen uses it naturally, without a second thought. And yet it encodes something profound: gegevens are things given — handed over, recorded, established by someone, for someone, with intent.
The English word 'data' has the same Latin root. Datum means 'something given'. But that etymology is effectively dead in English practice. Nobody says "one datum" anymore. Nobody asks "given by whom, for what?" The word has shed its own origin.
What remained is a term that implies objectivity, neutrality, and independence from any observer. Data just exists. It is out there. You collect it, store it, analyse it. The act of creation — the who, the why, the context — has quietly disappeared.
That disappearance has consequences.
The Void That Thinks
What Ancient Wisdom Teaches Us About Working With AI
The Oldest Observation
Ancient Hebrew texts describe the moment before creation as tohu va-vohu — formless and void. Not empty in a trivial sense, but charged with potential. What is striking is not the nothingness itself, but that it was considered real enough to name, to describe, and to stand in relation to.
The Kabbalistic tradition deepens this with Ayin — the nothingness that precedes all being. The void is not the absence of reality. It is a different kind of reality.
Now consider your own experience of not knowing something. It is not a blank. It has texture. It draws you toward it. You can dwell inside it, sit with it, let it work on you. Unknowing is not the opposite of knowing — it is a generative state, a threshold, a beginning.
This is a profoundly human capacity. And it is precisely what AI cannot do.
Understanding the difference — and learning to use it deliberately — will make you a better user of AI, and a better information modeler.
..you can inhabit it. And when you do, you change what AI can do for you..
Against the stream
Why Most "Business Language" Artifacts Flow the Wrong Way
TL;DR — Most artifacts that claim to speak "business language" — DDD context maps, OWL ontologies, Gherkin scenarios, DSLs — are built from the technical side and traveled toward business readability. They flow against the current. Genuine business language originates from the natural sentences domain experts actually use, and is formalized from there toward technical implementation. The direction of derivation is not a detail. It determines whether a model is owned by the business or merely legible to it.
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