Title: Data Cleansing Author: Kriko Published: Jun 7, 2023 Last modified: Jul 7, 2026 --- 1. [Home](https://kriko.io/) / 2. [Glossary](https://kriko.io/glossary) / 3. D Letter # Data Cleansing **[Data](https://kriko.io/services/data-analysis-insight) cleansing**, also known as [data](https://kriko.io/services/data-analysis-insight) 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](https://kriko.io/services/data-analysis-insight) cleansing is to **improve [data](https://kriko.io/services/data-analysis-insight) quality** and ensure that the information used in analysis is accurate, consistent and reliable. The term "dirty [data](https://kriko.io/services/data-analysis-insight)" 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 It includes all inaccurate, incomplete, outdated, duplicated or improperly formatted information.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](https://kriko.io/services/data-analysis-insight) 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](https://kriko.io/services/data-analysis-insight) cleansing activities, organisations can reduce these problems and create more dependable datasets. A typical **[data](https://kriko.io/services/data-analysis-insight) cleansing process** may include the following stages: **[Data](https://kriko.io/services/data-analysis-insight) 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](https://kriko.io/services/data-analysis-insight) 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**. ## Discover it in the dictionary ### Deep Learning Deep learning is a form of machine learning that uses artificial neural networks to analyse complex data structures. Inspired by certain aspects of how… ### Guerrilla Marketing Guerrilla marketing is a marketing approach that aims to help brands connect with their target audience through creative, unexpected and attention-grabbing methods outside traditional… ### DevOps DevOps is a methodology used to improve collaboration between software development and IT operations teams. 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