Offers rich capabilities to create and monitor data quality

screenshot of InfoSphere QualityStage interface

IBM InfoSphere® QualityStage® is designed to support your data quality and information governance initiatives. It enables you to investigate, cleanse and manage your data, helping you maintain consistent views of key entities including customers, vendors, locations and products. The solution helps you deliver quality data for your big data, business intelligence, data warehousing, application migration and master data management projects. Also available for IBM System z®.

"How in-line address data quality delivers business ready data for AI"

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Benefits of IBM InfoSphere QualityStage

Quality data

Provides capabilities including data profiling, standardization, probabilistic matching and data enrichment.

Unified platform

Delivers data quality functions as part of a complete information integration platform.

Support for information governance

Enables cross-organization capabilities to support your information governance policies.

Key features of InfoSphere QualityStage

Deep data profiling

Deep data profiling and analysis provide understanding of the content, quality and structure of tables and files, including column analysis, data classification, data quality scores, relationship analysis, multicolumn primary key analysis and overlap analysis.

Data quality rules (200+ built-in rules)

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 over 200 built-in rules to route data to the right person to be fixed to make sure the data is trusted.

Data classification (250+ built-in data classes)

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 number. Enables you to create and customize three types of data classes: valid values list, regular expression (regex) and Java® class.

Data standardization and record matching

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.

Built-in governance

The data quality Health Summary by Data Rules also shows data rules not linked to information governance rules and policies to support the enablement of data rules for exception management.

On-premises or cloud deployment

You can transition into a private or public cloud with flexible deployment options and subscription pricing. Using this feature, 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.

Automatic business-term assignment with machine learning

Utilize 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.

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