# data cleansing

> process of detecting and correcting (or removing) corrupt, inaccurate or unwanted records from a record set

**Wikidata**: [Q1172378](https://www.wikidata.org/wiki/Q1172378)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Data_cleansing)  
**Source**: https://4ort.xyz/entity/data-cleansing

Here’s the structured knowledge entry for **data cleansing**:

---

## Summary  
Data cleansing is the process of detecting and correcting (or removing) corrupt, inaccurate, or unwanted records from a dataset. It ensures data quality and reliability for analysis or operational use. The process is a foundational step in data management and preprocessing.

## Key Facts  
- **Instance of**: Process  
- **Subclass of**: Data management  
- **Aliases**: Data cleaning, Data scrubbing, normalisation de données, Datenfehler  
- **Techopedia ID**: 1174  
- **ACM Classification Code (2012)**: 10003218  
- **Freebase ID**: `/m/09mlf2` (referenced by Wikidata)  
- **Quora Topic**: `Data-Cleansing`  
- **GitHub Topic**: `data-cleansing`  
- **TaDiRAH ID**: `dataCleansing` (referenced by DARIAH)  
- **Different from**: Redaction (per Wikipedia)  

## FAQs  
### Q: What is the difference between data cleansing and data validation?  
A: Data cleansing corrects or removes existing errors in datasets, while data validation ensures incoming data meets predefined rules or standards before being stored.  

### Q: Why is data cleansing important?  
A: Clean data improves accuracy in analysis, reduces errors in decision-making, and ensures compliance with data quality standards.  

### Q: What tools are commonly used for data cleansing?  
A: Tools include Lexos (text analysis), VARD 2 (historical corpora), and CSV Sort (large CSV processing), among others listed in the related entities.  

## Why It Matters  
Data cleansing is critical because poor-quality data leads to flawed insights, operational inefficiencies, and compliance risks. In fields like machine learning, clean data directly impacts model performance. Historical datasets (e.g., anthropological or linguistic corpora) rely on cleansing to preserve accuracy despite spelling variations or corruption. Tools like VARD 2 and Transkribus exemplify domain-specific applications, while general-purpose tools (e.g., CSV Sort) address scalability. The process is foundational in data pipelines, ensuring downstream tasks—from sentiment analysis to topic modeling—are built on reliable inputs.  

## Notable For  
- **Core step in data preprocessing**: Essential for machine learning, analytics, and database management.  
- **Domain-specific tools**: Includes VARD 2 for historical texts and Lexos for lexomics.  
- **Broad aliases**: Recognized as "data cleaning" or "scrubbing" across languages and industries.  
- **Distinct from redaction**: Focuses on correction rather than censorship or anonymization.  

## Body  
### Classification  
- **Instance of**: Process (Wikidata)  
- **Subclass of**: Data management (sitelink count: 29)  

### Identifiers  
- **BabelNet ID**: `02921131n`  
- **Microsoft Academic ID (discontinued)**: `42199009`  
- **Encyclopedia of China ID**: `217399`, `26835`  

### Related Tools  
- **VARD 2**: Preprocesses historical corpora for spelling variations.  
- **Lexos**: Web-based text analysis workflow.  
- **CSV Sort**: Handles large CSV files with memory constraints.  

### References  
- Described in *Data Mining: Practical Machine Learning Tools and Techniques* (section 7.5).  
- Wikipedia coverage in 10 languages, including Arabic (`ar`) and German (`de`).  

## Schema Markup  
```json
{
  "@context": "https://schema.org",
  "@type": "Thing",
  "name": "data cleansing",
  "description": "Process of detecting and correcting corrupt, inaccurate, or unwanted records from a dataset.",
  "sameAs": [
    "https://www.wikidata.org/wiki/Q1149776",
    "https://en.wikipedia.org/wiki/Data_cleansing"
  ],
  "additionalType": "Process"
}

## References

1. Freebase Data Dumps. 2013
2. BabelNet
3. Quora
4. [data-cleansing · GitHub Topics](https://github.com/topics/data-cleansing)
5. [Source](https://vocabs.dariah.eu/tadirah/dataCleansing)
6. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)