Data wrangling often involves which of the following activities?

Data wrangling primarily involves combining data from multiple sources to create a cohesive dataset that is suitable for analysis. This process is essential because data often exists in various formats and from disparate sources, making it difficult to analyze without integration.

During data wrangling, activities typically include cleaning, transforming, and enriching the data to ensure quality and usability. By merging information from different datasets, analysts can derive more comprehensive insights. This may also include resolving inconsistencies, handling missing values, and reshaping data structures to achieve the desired format for subsequent analysis.

The other options relate to important functions in data management and analytics. Storing data in secure locations addresses data security but does not pertain directly to the transformation of data itself. Encrypting data for security also focuses on protection rather than the manipulation or preparation of data for analysis. Visualizing data trends is a crucial part of analysis and interpretation but occurs after data wrangling is complete, rather than being a component of the wrangling process itself. Thus, the correct focus on the integration of multiple data sources through wrangling is what makes that option accurate.