Before analyzing a new data set, what should a data analyst do?

Using data profiling to understand the data is a crucial first step for a data analyst before diving into any analysis. Data profiling involves examining the data to gather information about its structure, quality, and content. This practice helps analysts identify issues such as missing values, duplicates, and inconsistencies that could affect the accuracy of their analysis. By getting a comprehensive overview of the dataset, the analyst can determine appropriate cleaning steps, understand data distributions, and ascertain the context in which the data was collected. This foundational understanding is critical for applying the correct analytical techniques and ensuring that conclusions drawn from the data are valid.

In contrast, summarizing the data findings comes after the analysis has been performed, while sharing the data with colleagues is typically done post-analysis or for collaborative purposes, and changing the format of the data may not be necessary unless specific requirements arise from the initial profiling. Thus, understanding the data through profiling is essential for effective analysis.