Repairing a Corrupted Global Museum Dataset Using SAS and R
From Museum Chaos to Analytical Intelligence: Repairing a Corrupted Global Museum Dataset Using SAS and R for Enterprise-Grade Reporting Introduction:When Dirty Data Turns Cultural Intelligence into Business Risk Imagine a global tourism intelligence platform preparing an annual report ranking the world's most visited museums. Government tourism departments, investment firms, city planners, and cultural organizations rely on these numbers to allocate funding and forecast tourism growth. Three days before publication, analysts discover serious problems: Duplicate museum identifiers inflate visitor counts. Missing opening dates distort historical trend analysis. Negative visitor numbers appear after faulty ETL migrations. Invalid email addresses prevent stakeholder communication. Region codes use mixed standards such as EU, europe, Eur, and EUROPE. Museum categories contain corrupted values like "Artt", "histor y",...