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Notes on dataarchitecture.

Short essays on architecture, cost, hiring and AI: one cartoon, one idea, most days of the week. No tutorials, no listicles.

Mar 4, 2026 Organizational Dynamics

The Compliance Incident That Cost a Giant Fine (And One Email)

They had microservices, a data lake, and Grafana dashboards for everything. They didn't have an answer to "where does this customer's data …

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Mar 3, 2026 MVP to Platform

The Data Platform Scaling Checklist (When to Evolve)

Scaling too late costs you 6 months. Scaling too early costs you 18.

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Mar 2, 2026 Organizational Dynamics + Technical Debt & Economics

Parts Unlimited" Is Your Data Platform

The Phoenix Project described a company drowning in unplanned work. Gene Kim called it Parts Unlimited. You might recognize the patterns.

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Mar 1, 2026 Organizational Dynamics

Communication Overhead Kills Data Team Velocity

You doubled your data team. Delivery got worse. Fred Brooks explained why in 1975.

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Feb 28, 2026 Technical Debt & Economics

From EUR80K/Month Cloud Bill to EUR45K - The Optimization Sprint

This company cut their cloud data bill from EUR80K to EUR45K in 6 weeks. No functionality lost.

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Feb 27, 2026 Organizational Dynamics

Analytics Lead Time - The Time to Insight Metric

Your data team takes 2 weeks to answer a business question. That decision's already been made.

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Feb 26, 2026 Organizational Dynamics + Data Architecture

The Five Ideals Applied to Data Teams

Gene Kim wrote The Unicorn Project about a developer trapped in bureaucracy. Data engineers live that story every day.

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Feb 25, 2026 Organizational Dynamics

The Shadow IT Problem - When Fast Beats Right

Shadow IT doesn't start with rogue employees. It starts when the gap between 'I need this' and 'we can deliver this' gets too wide.

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Feb 24, 2026 Data Architecture & Strategy

The 6 Dimensions of Data Quality

Data quality has 6 dimensions. Most teams only measure 2. Here's what you're missing.

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Feb 23, 2026 Organizational Dynamics + Data Architecture

The Three Ways Applied to Data Pipelines

Your data team ships pipelines fast. That's the First Way. They're ignoring the other two.

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