Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
Why BI projects stall halfway
A look at the recurring reasons why business intelligence projects fail to reach a useful result, and what to do about them.
Machine learning for mid-size business: what is real, what is not
An honest look at which problems machine learning actually solves for companies without research labs, and which ones remain academic.
The Target breach and the end of perimeter security
What the largest retail data breach on record says about why protecting the perimeter is no longer a viable security strategy.
A data warehouse without a data team
How a small company can build a manageable data warehouse without hiring a BI department or buying an expensive platform.
What to read and watch in 2014 if you are building a system, not a career in hype
A closing post for the year that gives the reader a map of directions, not just a list of fashionable words.
Postdiction 2014-2016: containers, semantics and cloud will grow together, not separately
The winner will not be a single technology stack, but companies that can combine several waves into one platform.
The year in one frame: cloud trust, semantics, containers and a new maturity of the agenda
2013 is changing not the tools, but the direction of architecture and trust in IT.
ML in fraud detection: where AI saves money and where it only complicates the investigation
A look at the decision loop and the explainability problem in machine-learning-based anti-fraud systems.
Multitenancy and trust boundaries in SaaS
Where the convenience of a shared platform ends and the noisy-neighbour risk begins.
The platform team as the next step after strong operations
Why a centralised platform is becoming a separate function, and what that means for an IT leader.
Cloud exit plan: contract, migration, and the exit test
While everything is working, nobody calculates the cost of leaving. That is exactly why it needs to be calculated before it becomes urgent.
Model quality vs data freshness: what matters more in applied analytics
In most real tasks, the winner is not a smarter model but a better-managed operational loop that keeps data current.