What is the primary purpose of data cleaning?

The primary purpose of data cleaning is to detect and correct inaccurate records. Data cleaning involves the process of identifying errors or inconsistencies in data and rectifying them to improve data quality. This is crucial because inaccurate data can lead to faulty analyses and misguided decision-making. Data cleaning may involve addressing missing values, correcting typographical errors, removing duplicates, and ensuring that data is consistent across the dataset.

High-quality data is essential for any further analysis, visualization, or modeling processes. Inaccuracy in data can significantly compromise the integrity of insights derived from the data, making it necessary to prioritize data cleaning to ensure that the analysis reflects the true underlying patterns and information.

The other options, while important components of the data analysis process, do not encompass the primary goal of data cleaning. Enhancing data visualization techniques, performing statistical analysis, or creating machine learning models all rely on previously cleaned and validated datasets. Without addressing the quality of the data through cleaning, these subsequent steps can produce misleading or incorrect results.