Data Profiling for Assets > Introduction to data profiling
Introduction to data profiling
You can determine the quality of data across sources and understand the completeness, conformity, and consistency of data in your organization with the help of data profiling.
Use data profiling to determine the characteristics of column data in source objects such as tables and views. When you run a profile on data elements, data profiling analyzes source data and displays column level statistics. You can see statistics such as value distribution, patterns, and data types in the profile results. These statistics help you discover the content and structure of data and determine the suitability of data to solve business problems.
When you run a profile, the results include the following column statistics:
•Number of distinct, non-distinct, and null values
•Percentage of distinct, non-distinct, null, and blank values
•Documented and inferred data types
•Number of patterns
•Percentage of top pattern
•Maximum and minimum length of values
•Maximum and minimum values
•Value frequencies and outliers
After you run a profile, you can perform the following actions:
•View historical and latest profile results.
•Compare two profile runs to analyze the statistics.