Data profiling is an essential component of any data processing workflow. It provides the insight needed to significantly mitigate the negative effects of low-quality data on an organization's decisions, revenue, and actions. This process boosts the efficacy of various data-related activities, including integration, cataloging, procurement, analytics, and artificial intelligence, by defining, enforcing, and validating data accuracy and consistency.

During the profiling process, data is evaluated based on its adherence to the conditions and values provided within the profile rules. These rules help define data quality and enforce the recognized dimensions of data quality: accuracy, completeness, consistency, timeliness, validity, and uniqueness. Once complete, the results of the profile can offer insight into the type, and volume, of data quality issues found within a given dataset. During execution, Data Profiler writes data to two separate targets: Pass and Fail. Data that meets the criteria within the profile rules is written to the Pass target and data that does not meet that criteria is written to the Fail target. This bifurcation allows the immediate processing of the data in the Pass target, while the Fail target data can be routed for remediation.

A new data profile can be created by following the steps in our data profile editor. The steps include:

Navigation between pages of the guided workflow is possible by using the Back and Continue buttons at the bottom of the page. Navigating between pages will not clear data that has been entered by the user. When navigation to other pages is not possible, the button will fade in color and will not be active. You can exit the process anytime without saving any information by clicking Cancel.