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Articles tagged eigenaarschap
17 articles about eigenaarschap.
Stop rules for AI: set your exit criteria before go-live
Stop rules are the criteria, set in advance, for pausing or stopping an AI application. Which criteria you need, who decides and how to actually switch it off.
What is the EU AI Act? What applies now and what applies from December 2027
The AI Act regulates AI by risk. Which obligations already apply, what the Digital Omnibus postponed, and what your organisation needs to arrange now.
Five questions a supervisory board can ask about AI every quarter
Overseeing AI takes no technical knowledge, just the right questions. Five quarterly questions for the supervisory board, what a good answer looks like and when to probe further.
AI governance for executive and supervisory boards: value, ethics and stop rules
AI governance is the agreement on what AI may do, who is accountable and when you stop. A three-question framework for boards and supervisors.
Implementing data governance: a ninety day approach
Not with a framework, but with five terms people argue about. That is how you get something that stays standing within a quarter.
Why a semantic layer isn't enough for AI
A semantic layer defines your metrics. AI needs meaning that is governed, reaches the operation, and stays yours.
What is data governance? Roles, agreements and evidence
Data governance is not a policy document. It is the agreement about who owns which term, what it means, and how you can still retell that six months later.
Effective data lifecycle management
Data has a life: created, used, retained, removed. Skip a phase and you pay twice - in storage, and in wrong answers.
You do not own what you cannot take with you
Sovereignty decides where your intelligence runs. Ownership decides whether you can leave with it. Most AI platforms hand you neither, and the gap only shows on the day you try to go.
Start with one number
Two departments, two figures for the same thing. How to turn that one contested number into an agreement a team dares to steer on, in a single afternoon.
Everyone is buying the same AI
Almost everyone buys the same models from the same vendors. Your edge is not in the model but in the layer beneath it: your definitions, your owners, your meaning.
Data quality has two owners
Every test passes and still nobody trusts the number. Data quality holds two questions: is the data technically correct, and is the right thing being computed?
Technology only creates value once people work differently
The platform runs, the numbers add up, and still nothing changes. Value shows up when the work around the system changes, in small steps.
A data platform finds the fault. It does not change behaviour.
A test reports the deviation reliably, day after day. Still the alert sits there. Why data quality only moves when ignoring it hurts, and what agentic AI changes about that.
From meaning to decision. How a company brain works inside.
Two numbers for the same question is not a data problem but a meaning problem. Four steps from a disputed figure to a decision a team steers on.
AI needs your own knowledge. The industry now agrees.
Databricks and Microsoft renewed their partnership around one principle: AI delivers value only once it is grounded in your own knowledge. The principle is settled. Two questions are not: where does it start, and whose is it?
What is a data mesh?
A data mesh puts ownership of data with the domains doing the work instead of one central team. What that buys you, and what to settle first.
Start with one decision that has to get better
Pick a recurring question where teams lose time or contradict each other. Make the meaning behind it explicit and bring it straight into daily operations.