SAS® Data Quality Features

Data cleansing

  • Correct nonstandard or duplicate records as well as unknown data types.
  • Establish data hierarchies and reference data definitions.
  • Gain more value from data by separating values as required through natural language parsing.
  • Customize data quality rules to suit your needs with both customer and product data.

      Data monitoring

      • Design and enforce rules to determine if data is maintained within proper control limits and meets predefined business rules.
      • Create data alerts and controls to verify that data remains in compliance with internal and external policies.

      Data profiling

      • Validate data against standard measures and customized business rules.
      • Uncover relationships across tables, databases and source applications.
      • Verify that the data in your tables matches the appropriate description.
      • Establish trends and commonalities in business information and examine numerical trends via mean, median, mode and standard deviation.

          Entity resolution

          • Identify individuals across multiple data sources from incomplete relationships.
          • Manage entity resolution routines through advanced fuzzy-matching technology.
          • Create multirecord clusters, confidence scores and scatter plots to determine potential clusters.
          • Recognize when slight variations suggest a connection between records.

              Data management console

              • Monitor data quality jobs and view data issues and governance activities.
              • Access all data management activity from a single, common control point.
              • Secure role-based access and actions to authorization for specific data quality tasks.
              • Avoid logging in to a different web page or panel when moving from one function of the data management platform to another.

                  Data integration

                  • Embed data quality into extract, transform and load (ETL) and extract, load and transform (ELT) activities from multiple sources using both traditional batch processing and in-database methods.
                  • Transfer data to new or different locations while improving the accuracy and consistency of data through data migration.
                  • Match information within or across data sources, standardizing formatting differences.

                  SAS® Business Data Network and SAS® Lineage

                  • Collaborate on the creation and management of business terms and relationships with technical metadata.
                  • Manage who has access to different types of business terms and metadata.
                  • Visualize and trace data from source to consumer.
                  • Document data changes and transformations.
                  • View point-in-time snapshots of past terms and relationships to meet auditing requirements.
                  • Streamline integration with other applications via the public REST API.

                  Data remediation

                  • Route or correct data issues identified during data management operations.
                  • Engage data stewards and IT through a workflow-driven process.

                  Visual process orchestration and job monitoring

                  • Manage SAS and third-party technologies in a single process.
                  • Minimize or eliminate dependence on third-party scheduling systems and shell scripting for job control.
                  • Control the execution of multistep and multijob processes.
                  • Coordinate SAS and third-party jobs to work in a cohesive managed bundle.
                  • Log in and monitor the status of data management jobs without having to go to various dedicated servers to pull information.

                       

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