SAS Data Quality Features List

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.

SAS Viya Address, Email & Phone Verification

• Verify, standardize and enrich data from any country or territory in the world.
• Resolve address, geocode, email and phone data quality issues.
• For additional details on licensing address/email/phone enrichment & verification solutions, please refer to our partner page.

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 & 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 & 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.