Data wrangling, also known as data munging or data cleaning, is the process of transforming and mapping raw data into a more accessible and usable format for analysis or operational use. This step is crucial in the data pipeline, especially when dealing with unstructured or messy data that comes from various sources and in different formats. Data wrangling involves a range of activities such as removing duplicates, handling missing values, converting data types, and normalizing and standardizing data. The goal is to improve data quality and prepare it for subsequent stages of data analysis or machine learning. While some data wrangling can be done manually, specialized software tools and programming languages like Python and R are often used to automate and streamline the process. Though time-consuming and sometimes challenging, data wrangling is a fundamental step in making raw data more valuable and actionable.