Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
Kubernetes: what the hype omits about the operational side
Kubernetes solves real problems at scale. It also introduces a new operational surface that most teams are not ready for. A realistic look before you commit.
API-first is a business decision, not a technical one
Why the API-first approach is about business architecture rather than development practice, and how it affects company agility over three to five years.
Data quality: four metrics that are worth tracking in practice
Most data quality programs stall because the metrics are too abstract. Here are four concrete measurements that show up problems early and connect to business outcomes.
Why ML teams keep rebuilding the same data pipelines
The hidden cost of ML at scale is not the models - it is the duplicated feature engineering work every team does independently. What a feature store is and whether you actually need one.
Event-driven architecture: when it helps and when it creates noise
A practical look at event-driven design - what problems it solves, where it adds complexity without benefit, and how to decide whether you need it.
ML in production: the gap between a pilot and a working system
Why machine learning pilots often fail to become production systems, and what to do differently from the very beginning.
IT budget planning: the three layers most plans collapse into one
Annual IT budgets fail when run costs, change costs, and investment costs are mixed together. Separating them makes the conversation with management much more honest.
Legacy IT modernisation: a risk map for executives
How to think about replacing outdated systems without breaking the business processes that depend on them.
The Equifax breach: lessons for any company holding customer data
A breakdown of the Equifax incident and practical takeaways for executives whose companies collect and store personal data.
PostgreSQL as your main database: what changed for business
Why PostgreSQL stopped being a niche choice and what to verify before making it the foundation of a corporate architecture.
Data lake: questions to ask before you start building
Why data lake projects often turn into data swamps, and what founders and managers should ask before committing budget.
Microservices vs monolith: when splitting is actually justified
Why moving to microservices is not automatically the right decision, and how to tell whether your team and system are ready for the complexity it brings.