Data wrangling, also known as data cleaning, data remediation, or data munging, is the process of converting raw data into a usable form for analysis. It involves a variety of processes designed to transform raw data into more readily used formats, such as merging multiple data sources into a single dataset for analysis, removing errors, and combining complex data sets to make them more accessible and easier to analyze. The exact methods of data wrangling differ from project to project depending on the data being leveraged and the goal being achieved.

Data wrangling is an essential step in data science projects, as it ensures that the data being analyzed is reliable and complete, leading to accurate and valuable insights. The process typically includes the following steps: discovery, transformation, validation, and publishing. Data wrangling can be a manual or automated process, and it can be done by data scientists or other team members in larger organizations, while non-data professionals are often responsible for cleaning their data in smaller organizations.

The importance of data wrangling lies in the fact that any analyses a business performs will ultimately be constrained by the data that informs them. If data is incomplete, unreliable, or faulty, then analyses will be too, diminishing the value of any critical insights gleaned. Wrangled data makes it easier to analyze and interpret information, leading to many benefits, including better solutions, decisions, and outcomes.