data.KISS Blog by Andreas Buckenhofer

Data platform complexity is rarely something that happens to an organization.

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.

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.

The data warehouse of the 1990s, the data lake of the 2010s and today’s GenAI platform fail surprisingly often for the same reasons.

Big context, little content – that is the short summary of what research tells us about large LLM context windows.

GenAI critical thinking is becoming one of the scarcest skills in knowledge work.

Open table formats like Apache Iceberg and Delta Lake are changing how companies store analytical data – and who controls it.

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.

I’m a passionate runner.

In the rapidly evolving landscape of 2026, Data Tech Unlearning has become a critical survival skill for architects and leaders.