Data cleaning also referred as data cleansing, deals with identifying and removing errors and inconsistencies from data in order to enhance the quality of data.
Data cleansing, also known as data cleaning or data scrubbing, refers to the process of detecting and correcting errors, inconsistencies, and inaccuracies in data to improve its quality and reliability. This is a crucial step in data analysis and data management, as the accuracy and reliability of insights derived from data heavily depend on the quality of the data itself.
Data cleansing involves various tasks such as:
- Removing duplicate records: Identifying and eliminating duplicate entries in a dataset to avoid redundancy and ensure accuracy.
- Handling missing values: Dealing with missing or null values by imputing them with appropriate substitutes or removing them altogether.
- Correcting inaccuracies: Identifying and rectifying errors, inconsistencies, and anomalies in data entries, such as typos, incorrect formatting, or outliers.
- Standardizing data: Ensuring uniformity and consistency in data format, units, and representations across different datasets or data sources.
- Validating data: Verifying the integrity and correctness of data through validation rules or algorithms, ensuring that it meets specific criteria or constraints.
Overall, data cleansing is essential for preparing data for analysis, ensuring that the insights derived from it are accurate, reliable, and actionable.