Data pipeline

Every AI ambition an event has — matchmaking, churn prediction, personalized marketing — rests on pipelines, because the raw material lives in systems that don't naturally talk: registration in one platform, app behaviour in another, badge scans in a third, exhibitor accounts in a CRM. A pipeline moves records between them automatically, cleans the mess (duplicate people, mismatched company names, "ACME Ltd" vs "Acme Limited"), and ties activity to the same person across systems and editions. Commercially, pipeline quality is invisible until it isn't: exhibitor lead reports that disagree with the app's numbers, attendees emailed after opting out, an AI feature launched on data that's three weeks stale. The unglamorous truth is that most "AI readiness" work at event businesses is pipeline work. In practice, that means demanding real integrations (not CSV exports someone uploads monthly), assigning ownership of data quality, and testing the flows before show week, when volumes spike and failures go public. The common mistake is buying the AI layer first and discovering the plumbing problem second — models trained on disconnected, duplicated data produce recommendations exactly as confused as their inputs. One honest nuance: "we integrate with everything" usually means an API exists, not that the integration is built, maintained, or included in the price. Ask what flows automatically, in which direction, and how often.