Data quality in analytics determines whether the conclusions drawn from measurements are usable. Distortion tends to be more common than expected, and it is caused by recurring reasons: double measurement after a second tracker is deployed, unblocked internal and supplier traffic, automated activity from bots and monitoring tools, inconsistent campaign tagging, missing measurement on parts of the site after a redesign, and differing definitions of the same event over time. The result is reports that look convincing and lead to poor decisions. Basic hygiene is simple: maintain a documented measurement plan with event definitions, filter out internal traffic, regularly check for anomalies, and re-verify the measurement after every major change to the website. Date changes to definitions so you know from when the figures are comparable.
See also: KPIs for a marketer, Website migration without losing rankings, Conversion window.
