What is the primary goal of data validation?

The primary goal of data validation is to enhance data quality. This process involves ensuring that the data collected is accurate, complete, and reliable for use in analysis. High-quality data is essential for making informed decisions based on factual information. Data validation checks for errors, inconsistencies, and discrepancies within the data, helping to identify and correct them before the data is used for any purpose.

Enhancing data quality is crucial because poor-quality data can lead to incorrect conclusions, which can subsequently affect business strategies and outcomes. It involves various techniques and practices, such as checking for missing values, ensuring that data types are appropriate, and confirming that the data conforms to specified standards and requirements.

In contrast, gathering more data, eliminating all data outliers, and visualizing data more effectively serve different functions in data management and analysis but do not directly address the validation of existing data quality. While they may contribute to a broader data strategy, they do not encapsulate the core purpose of data validation, which is fundamentally about maintaining and improving the integrity of the dataset in use.