Journal
Articles on data, systems, and AI, alongside notes on where Atab is and where it is going.
Where Atab started, and how it got to where it is now.
Most businesses have more history than they can use. Not for lack of tools. For lack of reconciliation.
The smallest datasets produce the loudest findings. Statistical discipline is what stops you publishing nonsense.
You don't need an institutional data budget to build institutional-grade analysis. You need an honest transformation layer.
Most AI projects die after the demo, not during it. The model was never the hard part. The system around it was.
The instinct is always to add: another tool, another integration, another dashboard. The higher-leverage move is usually to remove.
A polished dashboard says the numbers can be trusted. If the data underneath was never reconciled, it just launders the mess into something confident.
Disconnected tools do not cost you a licence fee. They cost you an employee, quietly turned into a human bridge between two systems that will not talk.
Automation does not fix a broken process. It runs it faster, at scale, with fewer chances to catch the mistake before it lands.
Build the one thing that makes you different. Buy everything else. Most teams get this exactly backwards.