Neglecting data cleansing can lead to a host of issues, including compromised data quality, inaccurate analytics, and flawed decision-making. Dirty data—or data that is incorrect, incomplete, or irrelevant—can skew analytics results, leading to misleading insights. This can have significant implications for business strategies, operational efficiency, and customer relationships.
Furthermore, unclean data can hinder the performance of machine learning models, reducing their accuracy and reliability. In essence, without regular data cleansing, organizations risk basing their decisions on faulty data, which can have far-reaching negative consequences.