What is one of the key outcomes of conducting exploratory data analysis?

Conducting exploratory data analysis (EDA) is primarily focused on understanding the data set through visualization and summary statistics. One of the key outcomes of EDA is that it uncovers underlying trends and anomalies within the data. This process helps analysts to reveal patterns that may not be immediately visible and can highlight relationships, trends, or outliers that are significant to the dataset. By identifying these aspects, analysts can formulate hypotheses, refine their models, and ultimately make more informed decisions based on the insights gathered.

The other options do not accurately describe the primary focus of exploratory data analysis. For instance, real-time predictions involve predictive modeling and forecasting rather than the foundational analysis that EDA provides. Encrypting sensitive data pertains to data security measures, which fall outside the scope of data exploration. Lastly, while streaming data processing is crucial for handling real-time data, it does not relate specifically to the outcomes of exploratory data analysis, which is more concerned with data inspection and initial assessment rather than processing.