Data Profiling

Data profiling is the process of analyzing and assessing the quality, completeness, and consistency of data sets. It involves examining the structure, content, and relationships of data elements in a dataset to identify patterns, trends, and potential issues.

Data profiling typically involves several steps, including data discovery, data quality analysis, and metadata analysis. During the data discovery phase, analysts use GLIDE to identify the data sources and data elements that need to be profiled. In the data quality analysis phase, analysts can use GLIDE to report on the anomalies, inconsistencies, and errors that could affect the quality and reliability of the data. GLIDE can notify for inconsistencies such as missing values, outliers, or duplicate records.

In the metadata analysis phase, the metadata associated with the data elements, such as data types, formats, and descriptions are examined by GLIDE. This helps to provide context for the data and ensure that it is properly understood and used.

Data profiling is an important part of data management, as it helps to ensure that data is accurate, reliable, and appropriate for its intended use. Using GLIDE to identify data quality issues early on, organizations can take steps to address them and improve the overall quality of their data. GLIDE enables teams to scan and analyze large datasets quickly and efficiently.