Which practice helps to ensure the quality of data before analysis?

Data wrangling is the practice that focuses on cleaning, organizing, and preparing raw data for analysis, ensuring its quality and usability. This process includes identifying and correcting errors, handling missing values, and transforming data into a consistent format. By systematically addressing these issues, data wrangling allows for more accurate analysis and insights, making it essential for effective data-driven decision-making.

In contrast, while data transformation is related to converting data from one format or structure to another, it doesn't inherently ensure the quality of the data itself. Data protection, although critical for securing data and ensuring privacy, does not focus on the quality of the data for analysis purposes. Finally, data visualization is concerned with presenting data in a graphical format to help understand relationships and trends, but it does not address the data quality before the analysis takes place. Therefore, data wrangling stands out as the key practice for ensuring the quality of data prior to analysis.