What is the purpose of data cleaning in the data analytics process?

Data cleaning ensures the data is accurate and reliable before analysis. Sarah used Python and Pandas to automate this process, handling missing values, removing duplicates, and correcting errors in the dataset. By filling in missing sales data and standardizing customer feedback formats, she created consistency across the information. This critical step established a foundation of data integrity, ensuring that Sarah's subsequent insights and business recommendations would be based on accurate, trustworthy information rather than flawed data.