References & influences
My approach didn't appear from nowhere. It's built on decades of thinking by people who shaped how we model, historize and govern data — and on collaborators I've built real things with. I've written about many of them — here's where to find their work.
Collaboration
I've worked closely with CrossBreeze and its founders, Harmen Wessels and Willem Otten, on Model-Driven Data Engineering. I first met them on the Rabobank project, and later invited them to help build the MDDE framework at de Volksbank / ASN.
Their CrossModel tooling — open-source, logical-model-first, generating schemas and transformations from metadata — is MDDE in product form. A genuine meeting of minds on the same vision: structure first, generate the rest.
Data Vault & Ensemble modeling
Ensemble Logical Modeling — a major influence on how I think about business-keyed, agile data models.
LinkedIn →His work on the historical staging area and data-engine thinking supports a model-driven framework.
LinkedIn →His Data Town Plan — a bridge between business and IT — and his Universal Data Vault thinking shaped how I connect business intent to a generalized, reusable model. I've written about the Data Town Plan myself.
Data Town Plan →Modern Data Vault practice and patterns — a sharp, practical voice in the temporal-modeling community.
LinkedIn →His work on temporal data strengthens the bi-temporal / dual-SCD2 architectures I rely on.
LinkedIn →Creator of Anchor Modeling — an elegant, agile approach to information that changes over time. A key reference for the temporal thinking behind my historization patterns.
anchormodeling.com →
Foundations of data warehousing
I followed his three-day Business-Oriented Data Modeling masterclass — a major influence on my conviction that good models start with the business, not the database. The clearest articulation of the business focus I bring to every model.
LinkedIn →Behind CaseTalk and a leading voice in FCO-IM — fact-based modeling that grounds conceptual models in real, verifiable facts. Someone I know personally; his work informs how I keep models honest and business-readable.
casetalk.com →The case for 3NF and the business language model still shapes my integration-layer thinking.
Find his work →Timeless dimensional patterns that still shape modern, time-aware data platforms.
Kimball Group →Inventor of the Unified Star Schema — an elegant, resilient approach to the delivery layer that solves many-to-many and drill-across cleanly. A concept I love and have written about.
LinkedIn →Data Modeling Zone and a body of work that keeps the craft of modeling alive.
stevehoberman.com →The Data Model Resource Book series — universal, reusable patterns that underpin my business-oriented, reuse-first modeling.
LinkedIn →From whiteboards to working models — his thinking influenced how I run modeling sessions.
LinkedIn →The deepest PowerDesigner and metadata-management voice I know — his Metadata Matters work sharpened my own PowerDesigner automation and metadata thinking.
metadatamatters.com →Author of Metadata First: The Future of Data — a calm, principle-driven voice arriving at the same place I have: structure and metadata come first, the tool comes second. He generously quoted my work in the book; kindred spirits on what makes data trustworthy.
LinkedIn →Modern data & engineering
Founder of DQOps and a clear voice on data-quality frameworks — and a maker of excellent infographics. Data quality is exactly the wedge in my own work, so his thinking lands close to home.
LinkedIn →Author of the ssp.sh Data Engineering Blog and a public Data Engineering Vault — a data engineer who works the way I do: in markdown, in the open. Right at the PKM × data-engineering crossover.
ssp.sh →Co-founder of AgileData and creator of the Information Product Canvas — a business-first way to capture what data should deliver. I've written about the canvas; his pragmatic, agile take on modeling resonates with mine.
shagility.com →His work on SuperNova / data virtualization shaped my approach to delivery and the semantic layer.
LinkedIn →His principles (and Fundamentals of Data Engineering) reinforce a disciplined, model-driven approach.
LinkedIn →His Business Data Lake keynote thinking aligns with the model-driven future of data management.
LinkedIn →Links point to each person's public profile. I keep a running list of the thinkers I reference across my articles.
Personal knowledge management
Data architecture taught me rules and governance. PKM taught me linked, markdown-native thinking. Both turned out to be groundwork for the AI phase — the vault that organises my knowledge is now the context the model reasons over.
His Linking Your Thinking approach and Maps of Content shaped how I structure a connected vault — exactly the structure that now feeds AI.
linkingyourthinking.com →The PARA method and the second-brain idea — capturing and organising knowledge so it compounds instead of evaporating.
buildingasecondbrain.com →The atomic-note, link-as-you-think discipline behind durable knowledge — the smallest unit of structure.
Smart Notes →PKM gave me human-readable, linked structure; data architecture gave me rules and validation. AI needs both — and that convergence is where I work. More on the convergence →
What drives me
This isn't a reading list. It's intrinsic to how I think and work — the same operating system in my personal life and my professional one. See the system, find the model, build the structure, stay growth-minded. I don't switch it off when I leave the data behind; it's how I approach a project, a trip, a year, a life.
A latticework of mental models for clearer thinking and better decisions — the habit of reaching for the right model, not the first idea.
Farnam Street →Principle-centred effectiveness — begin with the end in mind, first things first. Structure applied to character and priorities.
The 7 Habits →Abilities grow with effort and learning. The mindset behind staying curious, reinventing, and treating every phase as a chance to grow.
Mindset →Designing work around life through systems, leverage and elimination — not more hours. Output over busyness.
tim.blog →A trusted system so the mind is for having ideas, not holding them. Capture, clarify, organise — productivity as structure.
GTD →Build the system and the results follow. Small, compounding, measurable improvements beat heroic one-off effort.
How I apply it →My interest in systems, models, productivity and a positive mindset is a real part of who I am — and it's the same instinct I bring to data and AI.
Manifestos I stand by
I like manifestos. A good one compresses a way of working — or a way of living — into something you can actually hold onto. These shaped mine.
Technical
The original. Value working software, collaboration and responding to change — principles I carry into how data teams should work.
agilemanifesto.org →Agile principles applied to data: working analytics, treating analytics as production, monitoring quality and performance continuously.
dataopsmanifesto.org →I've written my own — Data Engineering Manifestos, the Metadata-First and Universal Data Models manifestos, and the MDDE Manifesto. Principles to shape the practice.
Read on Medium →Life & psychological
The one that guides my life — it hangs on my wall. "This is your life. Do what you love and do it often. If you don't like something, change it. If you don't like your job, quit."
It's the spirit behind my own pivot — and a reminder that the same instinct for living well drives the way I work.
holstee.com →My library
A curated shelf, not a reading list — each one earned its place. Here's why it's in my collection and what I built with it.


Bill Inmon, Ranjeet Srivastava · 2021 · data warehousing

Patrick Cuba · 2020 · data vault

Ben Jones · 2019 · bi viz

Martin Kleppmann · 2017 · data engineering

Dan Linstedt, Michael Olschimke · 2015 · data vault

Hans Hultgren · 2012 · data vault

David C. Hay · 2011 · data modeling


David C. Hay · 1995 · data modeling

Roelant Vos, Dirk Lerner · 2025 · data vault



Michiel Rozema, Henk Vlootman · 2022 · bi viz

Joe Reis, Matt Housley · 2022 · data engineering

James Densmore · 2021 · data engineering

Danette McGilvray · 2021 · data quality governance

Dave Fowler, Matthew C David · 2021 · data architecture

Ronald G. Ross · 2020 · business rules requirements

Ben Jones · 2020 · bi viz


John Giles · 2019 · data vault

David C. Hay · 2018 · data architecture

Steve Hoberman · 2015 · data modeling

Ken Puls, Miguel Escobar · 2015 · bi viz

David C. Hay · 2013 · data modeling

John Giles · 2012 · data modeling

Donald C. Gause, Gerald M. Weinberg · 2011 · business rules requirements

Dan Linstedt, Kent Graziano · 2011 · data vault

Len Silverston · 2011 · data modeling

Christopher Adamson · 2010 · data warehousing

Stephen Few · 2006 · bi viz

C. J. Date, Hugh Darwen, Nikos A. Lorentzos · 2003 · data modeling

Len Silverston · 2001 · data modeling

Ronald G. Ross · 1998 · business rules requirements


Larry Shiller · 2026 · data quality governance

Remco Broekmans · 2025 · business rules requirements

Claire Frankel · 2024 · data modeling

Simon Aubury, Ned Letcher · 2024 · data engineering

Rui Pedro Machado, Helder Russa · 2023 · data engineering

Serge Gershkovich · 2023 · data modeling


Andy Petrella · 2023 · data quality governance

Ole Olesen-Bagneux · 2023 · data quality governance

Ronald G. Ross · 2022 · business rules requirements

Ergest Xheblati · 2022 · data engineering

Joyce Avila · 2022 · data warehousing

Helen Lippell · 2022 · data quality governance

Priyanka Vergadia · 2022 · data architecture

Alex J. Gutman, Jordan Goldmeier · 2021 · bi viz

Bill Inmon, Mary Levins, Ranjeet Srivastava · 2021 · data warehousing

Larry Burns · 2021 · data modeling

Peter ter Braake · 2021 · data modeling


Hamid Mahmood Qureshi, Hammad Sharif · 2021 · data warehousing

Mary Levins, W. H. Inmon, Daniel Linstedt · 2019 · data architecture

Kent Graziano · 2015 · data vault

Vladimir Pantic · 2013 · data modeling

Kevin P. Murphy · 2012 · data engineering

George McGeachie · 2011 · data modeling

Donald C. Gause, Gerald M. Weinberg · 2011 · business rules requirements

Roland Wenzlofsky · 2023 · data warehousing

Ron Itelman, Juan Cruz Viotti · 2023 · data architecture

Gareth Eagar · 2021 · data engineering


Roman Zykov · 2021 · data engineering

Joe Kraynak, David Baum · 2019 · data warehousing

Ralph Kimball, Margy Ross · 2016 · data warehousing

Kensu · data quality governance

Nikhil Gupta, Jason Yip · 2024 · data architecture


Roberto Zagni · 2023 · data engineering



DAMA International · 2017 · data architecture
If these names are on your shelf too, we'll get along.
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