Data Cleansing & Enrichment

Profile, cleanse, augment, and monitor data to create consistent, reliable information

Data quality is a critical factor for the success of enterprise intelligence initiatives. Bad data on one system can easily and rapidly propagate to other systems. If information shared across the organisation is contradictory, inconsistent or inaccurate, then interactions with customers, suppliers and others will be based on inaccurate information, resulting in higher costs, reduced credibility and lost business.

SAS Data Integration provides a single environment that seamlessly integrates data quality within the data integration process, taking users from profiling and rules creation through execution and monitoring of results. organisations can transform and combine disparate data, remove inaccuracies, standardise on common values, parse values and cleanse dirty data to create consistent, reliable information.

Rules can be built quickly while profiling data, and then incorporated automatically into the data transformation process. This speeds the development and implementation of cleansed data. A workflow design environment facilitates the easy augmentation of existing data with new information to increase the usefulness and value of all enterprise data.

Key Benefits

• Speeds the delivery of credible information by embedding data quality into batch and real-time processes.
• Reduces costly errors by preventing the propagation of bad data and correcting mistakes at the source.
• Keeps data current and accurate with regular auditing and cleansing.
• standardises data from multiple sources and reduces redundancy in corporate data to support more accurate reporting, analysis and business decisions.
• Adds value to existing data by generating and/or appending information from other sources.

Key Features

• Database/data warehouse/data mart cleansing through a variety of techniques, including standardization, transformation and rationalization, while maintaining an accurate audit trail.
• Data profiling to identify incomplete, inaccurate or ambiguous data.
• Data enrichment and augmentation.
• Create reusable data quality business rules that are callable through custom exits, message queues and Web services.
• Real-time transaction cleansing using standard business rules.
• Data summarization. Compress large static databases into representative points making them more amenable for subsequent analysis.
• Support for more than 20 worldwide regions with specific language awareness and localizations.