The correct answer is A. Data Deduplication.
Data deduplication is the process of identifying and removing duplicate or redundant data from a dataset. This can be done manually or automatically, and it is often used in data warehousing and data mining.
Data deduplication can improve the performance of data analysis by reducing the amount of data that needs to be processed. It can also help to improve the accuracy of data analysis by removing duplicate data that could skew the results.
There are a number of different methods for data deduplication. One common method is to use a hash function to generate a unique identifier for each record in the dataset. The records with the same identifier are then considered to be duplicates and can be removed.
Another common method is to use a database that supports data deduplication. These databases have built-in features that can identify and remove duplicate data.
Data deduplication can be a complex process, but it can be a valuable tool for improving the performance and accuracy of data analysis.
Here is a brief explanation of each option:
- Data Deduplication: The identification and removal of duplicate or redundant data.
- Data Aggregation: The process of combining data from multiple sources into a single dataset.
- Data Normalization: The process of converting data into a standard format.
- Data Imputation: The process of filling in missing data.