Continuous Defragmentation

Platforms fragment the way disks do: not through any single bad decision, but through thousands of small writes in convenient places. A business rule copy-pasted into a second pipeline, a filter redefined in a dashboard, a lookup table cloned into another schema — each fragment works locally, and collectively they scatter the platform's logic across places no one can enumerate. Fragmentation is the default direction of entropy; defragmentation has to be a deliberate, continuous practice. The practice is a recurring sweep with a fixed destination: find logic that exists in more than one place — or in the wrong place — and consolidate it into the model, where it becomes one definition that generation propagates everywhere it is needed. The model is what makes defragmentation pay off; consolidating into yet another shared script just moves the fragment. Consolidating into metadata retires it: the duplicates become generated instances of a single source of truth. "Continuous" is the operative word. Done as a one-off cleanup project, defragmentation loses to entropy within a year; done as routine — a candidate list fed by lineage analysis and duplicate detection, worked down every sprint — it holds the line. It is the structural companion to watching for semantic drift: drift is meaning fragmenting, and this is the maintenance loop that pulls the pieces back together.