Registration data quality

Every promise an event platform makes — relevant matches, personalized agendas, qualified leads for exhibitors — is a promise about registration data, because the algorithms downstream can only work with what the form captured. That makes data quality a design problem before it's a cleaning problem: field validation that catches mistyped emails at entry, dropdowns instead of free text for anything you'll ever filter on (free-text job titles produce thousands of variants that no report can group), required fields limited to what you'll genuinely use, and progressive capture that asks more once someone's committed rather than everything up front. The decay problem is separate and stubborn: data that was true at registration drifts as people change roles and plans, so long campaigns benefit from a light re-confirmation pass near the event. Where quality shows up commercially is exhibitor-facing: lead retrieval that returns "n/a" job titles and dead emails is the exhibitor's lasting impression of your event's professionalism. The common mistake is measuring quality by completeness alone — a form that forces answers gets fields filled with garbage, and "aaa" in the company field is worse than a blank because it looks like data. One honest nuance: quality costs friction, and friction costs registrations; the honest optimum accepts thinner data on casual visitors and invests the rigour where value concentrates — buyers, delegates, and matchmaking participants.