Architecture & Data Systems

  • Aligning Business Semantics with Data Design in Data Vault Environments

    Aligning Business Semantics with Data Design in Data Vault Environments

    Daniel Linstedt

    This article explores a workshop focused on aligning business semantics with technical data design in Data Vault environments. It highlights the persistent gap where technical correctness does not ensure business understanding or trust. The workshop uses practical exercises to reconcile business concepts with analytical requirements, producing business-centric conceptual models. The discussion exposes the capability gap…

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  • Relational Thinking Beyond SQL Assumptions

    Relational Thinking Beyond SQL Assumptions

    Daniel Linstedt

    This article examines a workshop at the Worldwide Data Vault Consortium 2026 that addresses gaps between SQL usage and relational theory. It highlights why relational rigor is essential for maintaining semantic clarity and trust in data systems. Key topics include nullology, view updating, and the Closed World Assumption. The discussion exposes how common SQL practices…

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  • Detecting Definition Drift Early in Practice

    Detecting Definition Drift Early in Practice

    Daniel Linstedt

    Definition drift occurs when data element meanings change without clear communication, impacting analytics reliability. Practitioners must embed clear ownership and metadata discipline to detect and manage drift effectively. Sequencing detection activities involves baseline documentation, recurring reviews, and human validation of automated signals. Clear roles and decision rights prevent fragmented accountability and support controlled change management.…

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  • Data Mesh as Organizational Doctrine, Not Architecture

    Data Mesh as Organizational Doctrine, Not Architecture

    Daniel Linstedt

    Data Mesh represents a recurring enterprise pattern of decentralizing data responsibility and federating decision-making, not a new architectural category. This article maps Data Mesh components to historical precedents, clarifying what it governs-accountability and operating behavior-and what it does not provide automatically, such as integration or semantic consistency. It highlights risks when organizational labels are mistaken…

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  • Medallion Labels and Their Historical Roots in Data Readiness Classification

    Medallion Labels and Their Historical Roots in Data Readiness Classification

    Daniel Linstedt

    Medallion labels classify data readiness stages but do not constitute an architecture or governance framework. These labels have historical precedents that reflect recurring enterprise needs to communicate data condition amid scaling pressures. Misinterpreting them as control mechanisms creates accountability gaps and semantic drift. Recognizing their lineage clarifies what they communicate and what responsibilities remain separate.…

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  • Why AI Does Not Eliminate the Need for Data Modeling

    Why AI Does Not Eliminate the Need for Data Modeling

    Daniel Linstedt

    AI initiatives often fail to scale safely without disciplined information management that preserves meaning, lineage, and accountability. This failure is systemic, reflecting deferred decisions and fragmented authority rather than AI technology limitations. Data Vault should be understood as a system of information management that stabilizes semantic consistency across organizational change. Skepticism about data modeling arises…

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  • Distinguishing Data Vault Modeling from System Mastery

    Distinguishing Data Vault Modeling from System Mastery

    Daniel Linstedt

    This article examines the critical distinction between mastering Data Vault modeling techniques and achieving comprehensive system mastery within enterprises. It identifies an authority fracture where decision rights and accountability are misaligned, undermining governance and operational control. The discussion highlights recurring patterns that reveal systemic failure, including fragmented funding and inconsistent enforcement. Through scenario analysis and…

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  • Unlocking Flexibility: Mastering Scalable Data Modeling

    Unlocking Flexibility: Mastering Scalable Data Modeling

    Daniel Linstedt

    Embracing Many-to-Many Relationships in Data Modeling: A Technical Guide PREFACE: there is another false belief out there: Many-to-many (links, pits, bridges) are bad and lead to join hell, and should never be used.  WRONG…. want to know why?  read on.  Note: anyone who claims “LINKS ARE BAD” or “MANY-TO-MANY should never be used” needs to

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  • From the Desk of Dan: Poison Data

    From the Desk of Dan: Poison Data

    Cindi Meyersohn

    With the advent of OpenAI and ChatGPT, it is easier than ever for bad actors to add Poison Data to the public collective. Why should you care?

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  • Data Vault, Data Mesh, and Polysemes

    Data Vault, Data Mesh, and Polysemes

    Cindi Meyersohn

    For those who are familiar with ‘Data Vault’ and are curious about ‘Data Mesh’, you may be asking yourself if any synergy exists between the two.  I’m happy to say that there is beautiful symmetry between the two. Data Vault is a proven methodology for building an analytic solution end-to-end from the businesses’ perspective; in

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