“Big data” usually means high volume, velocity, or variety — systems designed for scale. “Small data” is focused, often already structured, and frequently sufficient for high-value decisions.
Many teams over-engineer for petabytes when the critical path is a well-modeled few gigabytes with clear ownership and fast feedback loops.
Choosing wisely
- Start from the decision and required latency
- Measure real data volumes and growth
- Prefer simple architectures that can expand later
- Invest in quality and semantics before scale theater
AEDI’s philosophy: solve creatively, keep things clean and simple, and scale when the outcome demands it.
