Data Cleansing vs. Data Normalization
Maintaining quality data is necessary to ensure accurate analytics and decision-making processes. Two commonly used methods to ensure data quality are data cleansing and data normalization. While they may seem interchangeable, they have distinct differences. In this blog post, we'll explore those differences, including facts and figures.
Data Cleansing
Data cleansing, also known as data scrubbing, is the process of identifying and correcting or removing inaccuracies and inconsistencies from a dataset. The main objective of data cleansing is to ensure that data is consistent, accurate, and complete. Data cleansing is a crucial step in ensuring that the analytics performed is based on reliable and relevant data.
Examples of inaccuracies or inconsistencies that may be found in a dataset include typos, missing values, duplicates, outliers, and non-standardized formats. These issues can lead to inaccurate results and conclusions if not properly addressed.
According to a survey of data professionals conducted by Experian, data cleansing can take up to 15% of a company's time dedicated to data preparation. Additionally, poor data quality costs US companies $3.1 trillion a year, according to IBM.
Data Normalization
Data normalization is the process of organizing data in a database into tables, to reduce data redundancy and improve data integrity. The process involves breaking down larger tables into smaller, related tables, with each table focusing on a specific topic, also known as a relation.
Data normalization is usually done in stages or forms, with each stage increasing the level of data normalization. The most common form of data normalization is the first normal form (1NF) where data is organized into tabular form and has atomic values.
Data normalization enhances data consistency, accuracy and allows for efficient querying and data retrieval. It also enables the elimination of insert, update, or delete anomalies which can impact data integrity.
Data Cleansing vs. Data Normalization
While both techniques focus on ensuring data quality, data cleansing and data normalization address different types of data issues.
Data cleansing mainly deals with data accuracy and consistency issues by correcting or removing inaccuracies and inconsistencies from a dataset. Data normalization, on the other hand, primarily focuses on data redundancy and improving data integrity by organizing data into tables and eliminating insert, update, or delete anomalies.
Here is a table summarising the major differences between data cleansing and data normalization:
| Data Cleansing | Data Normalization |
|---|---|
| Corrects inaccuracies and inconsistencies | Organizes data into tables to reduce redundancy and improve integrity |
| Improves data accuracy and consistency | Enhances data consistency and enables efficient querying |
| Helps to eliminate irrelevant data | Eliminates insert, update or delete anomalies |
| Takes about 15% of a company's data preparation time | Requires knowledge and understanding of data concepts |
Conclusion
Both data cleansing and data normalization are crucial steps in ensuring the quality of data used for analytics. While they address different types of data issues, they work together to improve data accuracy, consistency, and integrity. To ensure reliable analytics, it's important to invest time and resources into both techniques.
References:
- Experian. "The State of Data Quality: 2020." Experian, 2020.
- IBM. "The High Cost of Poor Data Quality." IBM, 2018.