What does Data Profiling primarily analyze?

Data profiling primarily analyzes data structure, content, and quality. This process involves taking a deep look at datasets to determine the characteristics and details that make up the data. By examining the structure, you can understand how the data is organized, what types of data fields are present, and how they relate to one another. Content analysis helps in assessing what data is actually present, identifying patterns, distributions, and any outliers. Quality assessment is essential in determining the accuracy, completeness, consistency, and validity of the data.

The significance of data profiling is especially crucial during data integration, cleaning, and preparation stages, where understanding the characteristics of the data can lead to better decision-making and improved data governance strategies. By ensuring high-quality data, organizations can rely on their datasets for analytics and reporting, ultimately leading to more informed business decisions.