Which process is primarily involved in Data Cleaning?

Data cleaning is a critical process in data management that focuses on ensuring the accuracy and quality of data before it is analyzed. The correct answer is the choice that involves data collection and connecting datasets because this step includes identifying and correcting errors in the data, removing duplicates, and validating the consistency of data across different sources.

Effective data cleaning often requires collecting data from various sources and ensuring that these datasets are correctly linked or merged, which helps to eliminate inconsistencies and enhance data quality. By integrating and cleaning the data, organizations can prepare a reliable and accurate dataset for further analysis. This foundational step is essential for making informed decisions based on data-driven insights.

In contrast, the other options do not focus on the specific elements of data cleaning – conducting interviews may gather qualitative insights but not directly impact data quality, creating reporting methods pertains to how data is presented rather than its cleanliness, and integrating social media analytics does not directly address the validation and correction aspects of data cleaning.