DATA
Education and presentation resources for AI and ML deployment teams.
Why Centralising Data Adds Risk and Bottlenecks in AI Systems
Short headline: Centralising everything slows decisions and increases exposure.
When all data is pulled into one place, teams spend more time moving and securing it than using it. In real deployments, data is generated at the edge, across business units, and inside regulated environments. Forcing everything into a single hub creates delays, larger failure zones, and governance overhead that slows down AI delivery.
Performance and Latency
Centralising means shipping large volumes of data across networks. This adds latency, increases costs, and makes real-time inference harder. Systems become less responsive when every request depends on a distant data center.
Security and Attack Surface
A single data hub becomes a high-value target. More data in one place increases the blast radius of a breach and concentrates risk in one failure domain.
Compliance and Data Locality
Regulations like GDPR often require data to stay within specific regions or systems. Centralising can violate locality requirements, forcing expensive workarounds and slowing approvals.
Consulting Insight
In my consulting work, we design architectures that keep data close to where it is generated, and move models or summaries instead of raw data. This reduces latency, limits exposure, and keeps compliance teams comfortable while still delivering reliable AI outcomes.