What is data cleansing process?

The data cleansing process, also known as data scrubbing or data cleaning, involves identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in data management to ensure the accuracy, reliability, and consistency of data. Here are the key points that outline the data cleansing process:

• Data Assessment: It involves identifying the types of errors or issues present in the dataset, such as missing values, duplicate records, inconsistent formats, or invalid entries.
• Data Profiling: Conduct a comprehensive analysis of the dataset to gain insights into its structure, patterns, and quality issues. This step helps in understanding the scope of the data cleansing process and determining the appropriate techniques to be applied.
• Data Validation: This step requires validating the data to ensure that it adheres to defined rules and constraints. This involves checking for data integrity, accuracy, and compliance with predefined standards or business rules.
• Data Standardization: This phase includes standardizing the data by enforcing consistent formats, units, and representations. This covers formatting dates, addresses, phone numbers, or other data elements to a uniform structure.
• Data Deduplication: This next step requires identifying and removal of duplicate records or entries from the dataset. Duplicate data can skew analysis and lead to inaccurate insights. Various techniques, such as record matching or similarity algorithms, can be applied for effective deduplication.
• Data Correction: Correct errors or inconsistencies in the data. This may involve fixing misspelled words, resolving formatting issues, or updating inaccurate values based on predefined rules or external references.
• Data Completion: Address missing or incomplete data entries. This can be done by inferring missing values based on patterns or using imputation techniques to estimate values.
• Data Verification: Verify the accuracy and quality of the cleansed data through data sampling, cross-referencing with external sources, or running validation checks.
• Documentation: Document the data cleansing process, including the steps taken, transformations applied, and any assumptions made. This documentation helps in maintaining data lineage and facilitating future audits or updates.