The correct answer is D. Data Cleansing.
Data cleansing is the process of identifying and correcting (or removing) inaccurate, incomplete, or irrelevant data from a data set, table, or database. It is a critical step in data quality management and can help to improve the accuracy and reliability of data-driven decision making.
Data integration is the process of combining data from multiple sources into a single, consistent view. It can be used to create a more complete picture of a business or to improve the efficiency of data analysis.
Data transformation is the process of converting data from one format to another. It can be used to make data more compatible with different software applications or to make it easier to analyze.
Data aggregation is the process of combining data from multiple sources into a single, summary view. It can be used to identify trends, patterns, and relationships in data.
Here are some additional details about each of the options:
- Data integration is the process of combining data from multiple sources into a single, consistent view. This can be done through a variety of methods, such as data warehousing, data federation, and data virtualization. Data integration is often used to create a more complete picture of a business or to improve the efficiency of data analysis.
- Data transformation is the process of converting data from one format to another. This can be done through a variety of methods, such as data mapping, data conversion, and data translation. Data transformation is often used to make data more compatible with different software applications or to make it easier to analyze.
- Data aggregation is the process of combining data from multiple sources into a single, summary view. This can be done through a variety of methods, such as data summarization, data consolidation, and data roll-up. Data aggregation is often used to identify trends, patterns, and relationships in data.
- Data cleansing is the process of identifying and correcting (or removing) inaccurate, incomplete, or irrelevant data from a data set, table, or database. This can be done through a variety of methods, such as data profiling, data scrubbing, and data deduplication. Data cleansing is a critical step in data quality management and can help to improve the accuracy and reliability of data-driven decision making.