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You are a Microsoft Fabric Analytics Engineer working on a project to analyze a large dataset of customer transaction records for a retail company. The dataset contains millions of records with various numerical and categorical variables. Your goal is to ensure a thorough and comprehensive analysis to identify trends, anomalies, and opportunities for business improvement. The company emphasizes the importance of data quality and accuracy in their analysis. Considering the need for a detailed profiling of the dataset, which of the following steps should you prioritize to address potential data quality issues before proceeding with advanced analytics? Choose the best option.
A
Immediately apply advanced machine learning algorithms to uncover hidden patterns and clusters within the data, assuming the data is clean and ready for analysis.
B
Perform a comprehensive data quality assessment to identify and address missing values, outliers, and inconsistencies, ensuring the dataset's reliability for subsequent analysis.
C
Calculate and compare basic statistical measures like mean, median, and standard deviation across all numerical variables to quickly summarize the data's characteristics.
D
Generate visualizations such as histograms and box plots for each variable to visually inspect distributions and identify any obvious anomalies or patterns.