What is involved in data profiling?

Data profiling refers to the process of examining data from an existing dataset to understand its structure, quality, and relationships. This process often involves checking for discrepancies in data, which is crucial for ensuring the integrity and accuracy of the information being analyzed. By identifying inconsistencies, such as duplicate records, missing values, or format errors, data profiling helps organizations pinpoint issues in their data quality that could impact decision-making and analysis.

The other options, while related to data management, do not accurately describe the core activities involved in data profiling. Creating new datasets focuses on generating additional data instead of analyzing existing data. Reducing the volume of data may occur through processes like data cleansing or aggregation, but it is not a defining aspect of profiling. Summarizing data attributes can be part of the profiling process, but it primarily emphasizes understanding quality and identifying discrepancies, making the focus on discrepancies the most fitting choice for this question.