Data cleaning is the process of identifying and correcting or removing inaccurate, incomplete, or invalid data from a dataset. It is a crucial step to ensure that the data is accurate, consistent, and ready for analysis or modeling.
Data cleaning may involve a variety of tasks, such as correcting typos or errors, removing duplicates, handling missing values, or standardizing data formatting. These tasks can be performed manually, by a data scientist, analyst or labeler, depending on task complexity. They may also be semi-automated using software tools or scripts.
It is important to carefully review and clean the data before it is used for analysis or modeling, as poor quality data can compromise the accuracy and reliability of the results. Quality data is essential for making informed decisions, generating reliable reports, and supporting effective business operations.
People for AI is specialized in data labeling but we also provide the data cleaning service.
Synonyms: Data cleansing
