Use deep data profiling and analysis to provide understanding of the content, quality and structure of tables and files. This includes column analysis, data classification, data quality scores, relationship analysis, multicolumn primary key analysis and overlap analysis.

Control the ingestion of “bad” data by running data quality rules as data is being transformed and before you load it into the data warehouse, data lake or into applications. Use more than 200 built-in rules to route data to the right person to be fixed to make sure the data is trusted.

Identify where personally identifiable information (PII), sensitive and other classes of data are stored. You can also identify the type of data contained within a column using more than 250 built-in data classes, including credit card, taxpayer IDs and US phone numbers. Create and customize three types of data classes: valid values list, regular expression (regex) and Java class.

Synthesize all of the data coming from various sources into a common format or standard for the target environment. Remove duplicates and merge multiple systems into a single view to create accurate data that can be trusted.

Take advantage of the Health Summary by Data Rules report, which also shows rules not linked to information governance to support the enablement of data rules for exception management.

Transition into a private or public cloud with flexible deployment options and subscription pricing. You can extend your on-premises capacity or move directly to the cloud. Realize faster time-to-value, reduce administration costs and lower risk subscription pricing.

Use machine learning for an accelerated metadata classification process (auto-tagging) by using column names and data class to assign and suggest terms for a given column.