The process of analyzing data for errors and inconsistencies is known as:

The process of analyzing data for errors and inconsistencies is known as:

• A. Data cleansing
• B. Data profiling
• C. Data parsing
• D. Data standardization

Answer: (B)

Data profiling is the process of analyzing data to uncover inaccuracies, inconsistencies, and anomalies within datasets. It involves examining the data's structure, content, and relationships, providing insights into its quality and characteristics. This analysis helps organizations assess the reliability and usability of their data, serving as a crucial step before any data cleansing or correction efforts. Data profiling often includes generating statistics about data attributes, assessing data distributions, and identifying missing or erroneous values. By conducting this analysis, organizations can better understand their data challenges and take informed actions to improve the overall quality of their datasets. In contrast, data cleansing specifically focuses on correcting identified errors and inconsistencies, while data parsing refers to breaking down data into its component parts for easier analysis. Data standardization is about ensuring that data formats are uniform across different datasets. These processes are related but serve different functions in data management.

Data profiling is the process of analyzing data to uncover inaccuracies, inconsistencies, and anomalies within datasets. It involves examining the data's structure, content, and relationships, providing insights into its quality and characteristics. This analysis helps organizations assess the reliability and usability of their data, serving as a crucial step before any data cleansing or correction efforts.

Data profiling often includes generating statistics about data attributes, assessing data distributions, and identifying missing or erroneous values. By conducting this analysis, organizations can better understand their data challenges and take informed actions to improve the overall quality of their datasets.

In contrast, data cleansing specifically focuses on correcting identified errors and inconsistencies, while data parsing refers to breaking down data into its component parts for easier analysis. Data standardization is about ensuring that data formats are uniform across different datasets. These processes are related but serve different functions in data management.