Which of the following outputs is typically associated with data profiling?

Which of the following outputs is typically associated with data profiling?

• A. Visual representations of data flows
• B. Counts of nulls, min/max lengths, and data types
• C. Full confirmation of data authenticity
• D. Data encryption standards

Answer: (B)

The output typically associated with data profiling includes counts of nulls, min/max lengths, and data types. Data profiling is the process of examining the data available in an existing data source and compiling statistics and informative summaries about that data. The counts of null values help identify missing data, which is crucial for understanding data quality and integrity. The minimum and maximum lengths of data fields provide insights into the data's structure and requirements, which is essential for ensuring consistency. Additionally, knowing the data types (e.g., integer, string, date) is fundamental in understanding how data can be used, processed, and stored. This type of information is key to assessing data quality, preparing for data integration, and planning for data management strategies. The other options do not align with the primary outputs of data profiling. While visual representations of data flows may aid in understanding the architecture of data systems, they are not direct outputs of data profiling. Full confirmation of data authenticity goes beyond profiling, which focuses more on quality metrics rather than validating the validity of data origins. Data encryption standards relate to the security of data rather than its profiling characteristics. Thus, the counts of nulls, min/max lengths, and data types provide a foundational set of metrics that are essential outputs of

The output typically associated with data profiling includes counts of nulls, min/max lengths, and data types. Data profiling is the process of examining the data available in an existing data source and compiling statistics and informative summaries about that data.

The counts of null values help identify missing data, which is crucial for understanding data quality and integrity. The minimum and maximum lengths of data fields provide insights into the data's structure and requirements, which is essential for ensuring consistency. Additionally, knowing the data types (e.g., integer, string, date) is fundamental in understanding how data can be used, processed, and stored. This type of information is key to assessing data quality, preparing for data integration, and planning for data management strategies.

The other options do not align with the primary outputs of data profiling. While visual representations of data flows may aid in understanding the architecture of data systems, they are not direct outputs of data profiling. Full confirmation of data authenticity goes beyond profiling, which focuses more on quality metrics rather than validating the validity of data origins. Data encryption standards relate to the security of data rather than its profiling characteristics. Thus, the counts of nulls, min/max lengths, and data types provide a foundational set of metrics that are essential outputs of