Once standardized data formats have been established, the following step is to review your dataset for duplicates that may have been inadvertently overlooked in previous stages. The existence of duplicate entries can be detrimental for numerous reasons. To begin with, if identical entries are present multiple times, the overall quality of the dataset is compromised. This inconsistency makes it difficult to accurately gauge the effectiveness of your campaign, as the metrics may not align. Furthermore, duplicates can pose serious challenges for companies that rely on predictive and prescriptive analytics.

When machine learning models are fed inaccurate data, they tend to produce unforeseen outcomes. This situation results in skewed performance assessments and unsatisfactory results in subsequent marketing initiatives. In reality, duplicate data cleansing is not overly complex, provided that analysts possess fundamental technical skills. For instance, in SQL, a primary cleansing task can be accomplished with a few straightforward queries.