Data Platform Complexity, Part 4: Four Models to Fight It
Data platform complexity is rarely something that happens to an organization. It is something the organization builds, one reasonable decision at a time. Many data platforms are not an architecture;...
View ArticleData-Informed vs Data-Driven, Part 3: What Maps Leave Out
Every dataset is a map, which means it shows a territory in simplified form and leaves things out, and both of those are the point rather than the flaw. The first two parts dealt with structures and...
View ArticleGoodhart’s Law in Data and AI, Part 2: How Metrics Fail
Goodhart’s Law in data and AI describes a moment every data-driven organization walks into sooner or later: the moment a metric stops describing reality and starts producing it. That is the weakness...
View ArticleMental Models for Data and AI, Part 1: Why Silos Persist
The data warehouse of the 1990s, the data lake of the 2010s and today’s GenAI platform fail surprisingly often for the same reasons. Three decades, three generations of technology, one set of...
View ArticleBig Context, Little Content: Why the LLM Context Window Is Not a Data Lake
Big context, little content – that is the short summary of what research tells us about large LLM context windows. Vendors advertise one million tokens as if capacity were the same as capability. Load...
View ArticleGenAI Critical Thinking: The Skill AI Cannot Replace
GenAI critical thinking is becoming one of the scarcest skills in knowledge work. GenAI makes it easier than ever to get answers. What it does not do is make it easier to question them. That asymmetry...
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Open Table Formats: From Vendor Lock-In to Data Sovereignty
Open table formats like Apache Iceberg and Delta Lake are changing how companies store analytical data – and who controls it. If you store your analytical data in a proprietary format, you’re locked...
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Value Over Volume: Why AI Makes Data Professional Fundamentals Matter More...
Data professional fundamentals have never been more valuable, yet for years the industry measured progress by volume: lines written, data products shipped, pipelines grown, dashboards created. AI has...
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From Heart Rate to H3: Six Ways to Think About Your Running Data in Oracle 26ai
I’m a passionate runner. And like many data enthusiasts, I can’t resist collecting data about the things I care about. This post is both a personal reflection on my training and a hands-on tour of how...
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The Art Of Data Tech Unlearning: Which Habits We Need to Drop Now
In the rapidly evolving landscape of 2026, Data Tech Unlearning has become a critical survival skill for architects and leaders. In the data world, we often define ourselves by the complexity of the...
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One Database, Six Workloads: Analyzing F1 Telemetry With Oracle Converged...
The Complexity We Created Somewhere along the way, we convinced ourselves that specialized databases were the answer to everything. Need to store documents? Spin up DocumentDB X1. Time series data?...
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The Data House: Strategy, Governance, Management, Security – Explained and...
In AI and data programs, a surprising amount of friction doesn’t come from technology – it comes from language. Terms like data strategy, data governance, data management, security, privacy, and...
View ArticleFrom Ticket-Takers to Value-Makers: Navigating 2026 Tech Predictions
In late 2025, the tech world is drowning in “2026 tech predictions” from every analyst and hyperscaler in the industry. But behind the buzzwords lies a shift that will define the next decade: the...
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Agentic AI with DuckDB and smolagents: Natural Language Queries for Analytics
Agentic AI with DuckDB turns natural language queries for analytics from a nice idea into something you can actually run today. Many users don’t want to think in joins, window functions, or aggregate...
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Data and Analytics Skills 2026+: Why You Need More Than a Full Toolbox
The demands on data professionals and leaders are changing rapidly. Technologies, methods and tools come and go – and with LLMs like ChatGPT, generic knowledge is increasingly automatable. This makes...
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SQL in Jupyter Notebook (or Google Colab) – with DuckDB & JupySQL
If you live in Python notebooks all day, it’s only a matter of time until you want proper SQL right next to your code. No extra UI, no switching tools, just plain SQL with output. This article covers:...
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The illusion of simplicity: hidden reporting pitfalls of the Big-Table model
At first glance, a single denormalized big-table seems like a reporting hero: no joins, just one table to maintain, and demo queries that fly. But the honeymoon rarely lasts. When real-world business...
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If It’s Easy, Is It Worthless? The Data Complexity Trap
If It’s Easy, Is It Worthless? Google Search, the iPhone, and countless other products prove the opposite: the simplest experience often delivers the highest value. So why do our data products still...
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TDWI Munich 2025 Unleashed – My Key Take-Aways on Generative AI, Complexity...
The TDWI Munich 2025 once again proved why it is an excellent event for data, analytics and AI professionals. Below you’ll find my personal notes and reflections from three packed days in the “Data...
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From Raw Text to Ready Answers — A Technical Deep-Dive into...
Large language models (LLMs) are astonishing pattern-completion engines, but they are also static archives: everything they “know” is baked into billions of parameters frozen at training time. Once...
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