Cloud Data Quality

Transformations are a part of a mapping that represents the operations that you want to perform on data. Transformations also define how data enters each transformation. Let's see how to integrate Cloud Data Quality (CDQ) Assets into the Cloud Data Integration (CDI) Mapping feature and the value that it provides a user.

The Cleanse transformation adds a cleanse asset that you created in Data Quality to a mapping. It allows you to add data transformation logic that you designed elsewhere to a mapping. A cleanse asset is a set of data transformation operations that standardize the form and content of your data.

The Parse transformation adds a parse asset that you created in Data Quality to a mapping. It allows you to add data transformation logic that you designed elsewhere to a mapping. It also shows incoming and outgoing fields.

The Deduplicate transformation adds a deduplicate asset that you created in Data Quality to a mapping. This can be used to analyze the levels of duplication in a data set and optionally to consolidate sets of duplicate records into a single, preferred record. It also analyzes the identity information in the records.

Scorecard is a collection of rule occurrences and it represents data quality scores calculated when you profile a source dataset. Use scorecards to measure data quality scores and monitor data quality progress for existing and new profiles.

In Data Profiling, when you run a profile that has a rule associated with it, you can view the data quality scorecard in Data Governance and Catalog after a successful profile run. Rule occurrences related to the scorecard are generated in Data Governance and Catalog. You can associate a data element to a rule occurrence that is generated from a scorecard.

An exception record is a record that contains unresolved data quality issues. Exception Management is how to use a rule specification to identify exception records in your data set and to create an exception task from the profiling task that applies the rule specification that you configured to the data.

Use data observability to identify anomalies in the characteristics of the data in your source system. Data observability analyzes a sudden deviation from a usual trend within a source system and generates events for the anomalies detected within your source system data.