#Data Quality

Data cleaning is the altar upon which endless typos and missing values are secretly wiped away to worship the illusion of consistency. It gathers the trash of errors and anomalies with the dedication of a zealot, while twisting reality in shadows no one will notice. Praised as a sacred rite in business meetings, in practice it only draws the jeer of "Is it done yet?" from weary engineers. It demands vast labor and bottomless coffee sacrifices to obtain pristine datasets, serving as the unnoticed backstage work. The moment it's "finished," no one cares about the pain endured—a pointless aesthetic pursuit masquerading as necessity.