A data fabric doesn’t move an organisation’s data anywhere. It builds a virtual catalog on top of the databases, CRMs, and legacy systems already in place, so a query can reach across all of them as if they were one connected source, without the underlying records ever leaving their original system.
That’s what makes it the fastest of the three main options for fixing fragmented data ahead of an AI deployment, typically three to six months against twelve to eighteen for a full lakehouse migration, and it disturbs legacy systems the least. The trade-off shows up at query time: because the data is still physically distributed, live retrieval through the fabric layer can run slower than a query against data that’s already been consolidated in one place.
It’s the right first move for organisations that need AI working against unified data quickly and can tolerate some retrieval latency, rather than those needing peak analytical performance from day one.