What is the main goal of data cleansing?

What is the main goal of data cleansing?

• A. To transform raw data into a readable format
• B. To detect and correct data errors to improve data quality
• C. To store data in a single repository
• D. To create reports for data analysis

Answer: (B)

The primary goal of data cleansing is to detect and correct data errors to improve data quality. This process involves identifying inaccuracies, inconsistencies, and incomplete data within a dataset. By addressing these issues, organizations can ensure that their data is accurate, consistent, and reliable, which is essential for making informed decisions based on that data. High-quality data supports better analytics, reporting, and overall business intelligence, ultimately leading to improved operational efficiency and strategic outcomes. In the context of data management, simply transforming raw data into a readable format does not address the integrity or correctness of the data itself, nor does it ensure its overall quality. Storing data in a single repository focuses more on data organization than on quality, and while creating reports for data analysis is an important function of data usage, it relies on the premise that the underlying data is clean and trustworthy. Therefore, the focus of data cleansing on improving data quality is crucial for effective data management.

The primary goal of data cleansing is to detect and correct data errors to improve data quality. This process involves identifying inaccuracies, inconsistencies, and incomplete data within a dataset. By addressing these issues, organizations can ensure that their data is accurate, consistent, and reliable, which is essential for making informed decisions based on that data. High-quality data supports better analytics, reporting, and overall business intelligence, ultimately leading to improved operational efficiency and strategic outcomes.

In the context of data management, simply transforming raw data into a readable format does not address the integrity or correctness of the data itself, nor does it ensure its overall quality. Storing data in a single repository focuses more on data organization than on quality, and while creating reports for data analysis is an important function of data usage, it relies on the premise that the underlying data is clean and trustworthy. Therefore, the focus of data cleansing on improving data quality is crucial for effective data management.