Reasons why Data cleansing is necessary

Data cleansing, also known as data scrubbing or data cleaning, is the process of identifying and correcting or removing incorrect, incomplete, or irrelevant data from a database or dataset. Data cleansing is an important step in the data preparation process, as it helps to ensure the accuracy, consistency, and completeness of the data, which is critical for making informed decisions and achieving desired outcomes.

There are several reasons why data cleansing is necessary:

• Data quality issues: Data can become dirty or incorrect over time due to various reasons such as data entry errors, outdated information, formatting errors, and so on. Data cleansing helps to identify and fix these issues, so that the data is accurate and reliable.
• Data consistency: Inconsistent data can lead to confusion and misinterpretation, which can impact the accuracy of the analysis and the decisions made based on it. Data cleansing helps to ensure that the data is consistent across the different sources and is in a standardized format.
• Data completeness: Incomplete data can lead to inaccurate conclusions and flawed decisions. Data cleansing helps to identify and fill in any missing data, so that the data is complete and can be used confidently.

Techniques that can be used for data cleansing, including:

• Data validation:This involves checking the data against a set of rules or standards to ensure that it is accurate and complete.
• Data standardization:This involves transforming the data into a standardized format, such as converting all dates into a specific format or converting text to all lowercase.
• Data deduplication:This involves identifying and removing duplicate data entries.
• Data enrichment:This involves adding additional information to the data, such as adding geographical coordinates to address data.