Data cleansing, also known as data cleaning, is the process of identifying and correcting inaccurate, incomplete, irrelevant, duplicated or inconsistent information within a database. Invalid or unusable records may also be removed where necessary.
During this process, duplicate information may be merged or deleted, incorrectly formatted values may be corrected and missing fields may be completed using appropriate methods. The primary purpose of data cleansing is to improve data quality and ensure that the information used in analysis is accurate, consistent and reliable.
The term “dirty data” does not refer only to records that are deleted from a database. It includes all inaccurate, incomplete, outdated, duplicated or improperly formatted information.
Companies, organisations and brands that manage large datasets frequently perform data cleansing activities. Automated tools and algorithms can be used to identify problematic records, correct common inconsistencies and detect duplicate data. These tools can process large volumes of information more efficiently and provide significant time savings.
Incorrect customer details, empty fields, invalid values, inconsistent formats and duplicate records can negatively affect the accuracy and reliability of a database. By carrying out data cleansing activities, organisations can reduce these problems and create more dependable datasets.
A typical data cleansing process may include the following stages:
Data Analysis: The dataset is examined to identify errors, missing values, inconsistencies and duplicate records.
Correction: Incorrect values, spelling errors and formatting problems are corrected.
Missing Data Management: Missing values may be completed using reliable sources, estimated through suitable methods or left empty depending on the intended use of the data.
Removal of Unnecessary Data: Invalid, irrelevant or duplicated records that do not provide analytical value are removed from the dataset.
Standardisation: Dates, addresses, telephone numbers, currencies and other data types are converted into consistent formats.
The cleansed data should then be validated and reviewed through quality control procedures to confirm that the changes have been applied correctly. This helps prevent inaccurate information from affecting analysis, reporting and decision-making processes.
Cloud-based data platforms and data preparation, integration and ETL services provided by vendors such as AWS can also be used to automate and scale data cleansing operations.
