What is the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets to ensure data quality?

What is the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets to ensure data quality?

• A. Data cleaning
• B. Data
• C. Preprocessing
• D. Tool creation

Answer: (A)

Data cleaning focuses on identifying and correcting errors, inconsistencies, and inaccuracies in datasets to ensure data quality. It involves addressing issues like missing values, duplicate records, inconsistent formats, outliers, and typos so the data can be relied on for analysis and modeling. This is the precise, targeted step that improves data quality, whereas preprocessing is a broader phase that includes cleaning plus other transformations like normalization and encoding. Raw data refers to the unprocessed material, and tool creation is about building software rather than cleaning data.

Data cleaning focuses on identifying and correcting errors, inconsistencies, and inaccuracies in datasets to ensure data quality. It involves addressing issues like missing values, duplicate records, inconsistent formats, outliers, and typos so the data can be relied on for analysis and modeling. This is the precise, targeted step that improves data quality, whereas preprocessing is a broader phase that includes cleaning plus other transformations like normalization and encoding. Raw data refers to the unprocessed material, and tool creation is about building software rather than cleaning data.