What is data cleansing?
Data cleansing is the act of identifying and correcting errors or inconsistencies in data, such as missing or incorrect information, duplicate entries, or formatting issues.
Why is data cleansing important?
Data cleansing is a critical process in data management that involves identifying and correcting inaccurate, incomplete, or irrelevant data. In today's data-driven world, businesses rely heavily on the data they collect to make informed decisions. However, when data is pulled from various sources, it tends to be in different formats, and this can lead to a variety of problems that necessitate the need for data cleansing.
Without accurate and reliable data, businesses can make incorrect decisions that can impact their profitability and reputation.
Here are some of the problems that can arise when data is not cleansed:
- Data inaccuracy: The wrong or outdated information is entered into the dataset.
- Duplicate data: The same information is entered into a system multiple times.
- Incomplete data: Important information is missing from a dataset.
- Data inconsistency: The same data is represented differently in different parts of a dataset.
How is data cleansing done?
The process of data cleansing starts with identifying errors and inconsistencies in the data. This can be done manually or with the help of automated tools. Once the errors are identified, the next step is to correct them. This can involve a range of techniques, such as:
- Data standardization: Ensure that all data follows a consistent set of rules, such as capitalization, spelling, or abbreviations.
- Data deduplication: Identify and remove duplicate records in the database.
After the data is cleaned, the next step is to validate the data to ensure that it is accurate and reliable. This can involve comparing the data with external sources, such as industry benchmarks, or using statistical methods to identify outliers or anomalies.