Data wrangling—also called data cleaning, data remediation, or data munging—refers to a variety of processes designed to transform raw data into more readily used formats. The exact methods differ from project to project depending on the data you’re leveraging and the goal you’re trying to achieve.
Data wrangling can be a manual or automated process. In scenarios where datasets are exceptionally large, automated data cleaning becomes a necessity. In organizations that employ a full data team, a data scientist or other team member is typically responsible for data wrangling. In smaller organizations, non-data professionals are often responsible for cleaning their data before leveraging it.
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