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[jorgenguldmann]

The layer has a name: Ontology.

Traditional BI answers “𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱?” Semantic models answer “𝗛𝗼𝘄 𝘀𝗵𝗼𝘂𝗹𝗱 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗶𝘁?” Taxonomies answer “𝗪𝗵𝗮𝘁 𝗸𝗶𝗻𝗱 𝗼𝗳 𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗶𝘀?” Knowledge graphs answer “𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱, 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄?” 𝗢𝗻𝘁𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗮𝗻𝘀𝘄𝗲𝗿 “𝗛𝗼𝘄 𝗮𝗿𝗲 𝗮𝗹𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱?” Your AI agent doesn’t know what a customer is. It knows you have a table called CUST_MASTER_V2. Closing that gap takes…

The layer has a name: Ontology.

[jorgenguldmann]

Data Models: Conceptual vs Logical vs Physical

Every database starts as an idea. By the time it’s running in production, that idea has passed through three distinct stages, each one adding detail the last one didn’t have. Skipping straight to the last stage is how projects end up with schemas that technically work but don’t actually reflect how the business thinks. Understanding all three and treating them as separate steps makes the whole…

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[jorgenguldmann]

Your Silver Layer Is Not Missing Data. It Is Missing Decisions.

Most Silver layers are full of cleaned tables, standardised formats and automated transformations. But that is not where the real value sits. The real value of Silver is that it turns business decisions into reusable enterprise meaning. That is why a canonical model is not just a shared schema between applications. It is the organisation’s shared understanding of its business, expressed in a…

Your Silver Layer Is Not Missing Data. It Is Missing Decisions.

[jorgenguldmann]

AI Governance: A Structural Perspective for Practitioners

As an architect, I’m watching a still-immature space, one where organisations are throwing themselves into AI faster than the structure around it can keep up. I keep seeing the same moment play out, usually right after something goes slightly wrong: who was supposed to be watching this? A chatbot gives a customer bad advice. A model quietly drifts after a few months in production. A team spins up…

AI Governance: A Structural Perspective for Practitioners

[jorgenguldmann]

Clear roles. Stronger governance. Better data.

Data governance often fails not because organizations lack policies or tools, but because nobody is quite sure who is supposed to do what. A data quality issue surfaces, and three teams point at each other. A new data product needs an owner, and the request stalls in email threads. A security exception is requested, and no one knows who actually has the authority to approve it. A RACI matrix,…

Clear roles. Stronger governance. Better data.

[jorgenguldmann]

Where Medallion Architecture and Canonical Models Actually Meet

Medallion architecture has become the default mental model for organizing a data platform: land raw data in Bronze, clean and conform it in Silver, serve trusted, business-ready data from Gold. It’s simple, it’s intuitive, and it works, right up until three teams build three different “customer” tables and nobody can agree on the number in the boardroom. That failure mode isn’t a medallion…

Where Medallion Architecture and Canonical Models Actually Meet

[jorgenguldmann]

Losing You Twice

Losing you once broke my heart.Losing you twice broke the part of methat had learned to live without you. I thought time would soften the ache,but grief has a way of finding its way home. There are no words for this silence,only memories that lingerand love that refuses to fade. Grief is love with nowhere to go.

Losing You Twice

[jorgenguldmann]

Distributed Attribute Ownership Specification - Template

What this describes: how ownership/responsibility for a single data attribute is distributed across multiple parties, and how that responsibility hands off as the attribute (or its governing lifecycle) moves through states. Fill in every ‹ › placeholder. Delete the guidance notes when done. 1. Identity FieldValueAttribute‹name of the attribute being governed›Type / shape‹e.g. enum, string,…

Distributed Attribute Ownership Specification - Template

[jorgenguldmann]

Differentiated VAT Won't Break Anything

Every time differentiated VAT comes up, the same chorus predicts the apocalypse. Accountants drowning, shops seizing up, chaos erupting, the economy collapsing because, we’d apply more than one rate.It’s nonsenseMultiple VAT rates are the global default. Germany, France, Sweden, nearly every country runs several rates at once and has for decades, on the same systems sold here. Nobody invented…

Differentiated VAT Won't Break Anything

[jorgenguldmann]

Data Contract - Template

How to use this. A data contract is an agreement between the people who produce a dataset and the people who consume it. It exists to replace assumption with accountability, not to generate paperwork. Fill the Minimum Viable Contract (sections 1–5) for every governed dataset. Add sections 6–9 only when the data’s scale, risk, or number of consumers actually justifies them. If a section does not…

Data Contract - Template

[jorgenguldmann]

I love you, you know that

but sometimesit feels like you’re lookingfor something to break like even on the good daysthere’s this momentwhere everything turns and I don’t always understand it I just stand theretrying to hold on to uswhile it slips and then somehowwe find each other again like we always do and I love that part toothe way you come backthe way we come back I just wishwe didn’t have to fall apart…

I love you, you know that

[jorgenguldmann]

Dulce Far Niente

I sit where the light fallsand do not move. Time loosens its grip,slips quietly pastwithout asking to be held. Thoughts drift in and outlike distant waves,never staying long enoughto matter. Nothing is required of me here.Nothing is expected. And in that quiet absence,something gentle returns.

Dulce Far Niente

[jorgenguldmann]

From Data Responsibility to Trust

Let us get real. Most companies talk a big game about data platforms, self-service analytics, and unlocking the potential of data. Then complicated topics like PII enter the scene, creating chaos or prompting many to turn a blind eye to the problem. This is why the role of a data platform owner is critical. Without clear ownership, compliance is not a checkbox; it is a ticking time bomb.…

From Data Responsibility to Trust

[jorgenguldmann]

Sleepless

She doesn’t say no,but she doesn’t say yes,and I’m left in between. I replay her wordslike footprints in snow,already fading. Some nights she feels close,other nights she disappears. And in the silenceI stay awake,trying to understandthe spacebetween what she givesand what I hope for.

Sleepless

[jorgenguldmann]

Critical Data Elements

This time, I am focusing on Critical Data Elements and, more importantly, the perspectives through which they must be understood. CDEs are not valuable on their own. Their value emerges only when they are placed in the right context, tied to business processes, and supported by clear roles that both contribute to and rely on these perspectives. Before reading this post, I strongly recommend…

Critical Data Elements

[jorgenguldmann]

Master Data Management Platform Selection

From Tool Evaluation to Operating Model Alignment Selecting a Master Data Management (MDM) platform is often framed as a technology decision. In practice, it is far more consequential. The choice of MDM platform implicitly defines how data governance is executed, how business processes are enforced, and how complexity is managed across the enterprise landscape. This post presents a…

Master Data Management Platform Selection

[jorgenguldmann]

A Double Edged Sword

In modern IT organizations, compartmentalization is the practice of dividing responsibilities, systems, or data domains into clearly separated units. This approach can be incredibly helpful when it brings clarity, control, and protection. It can also create problems when it interferes with collaboration or end to end visibility. Nowhere is this balance more important than in Master Data…

A Double Edged Sword