SAS^® Data Management for Midsize Business

Features

Four versions available: Basic, pro, desktop and server-based

• The basic version: Available in a PC environment, this solution enables users to profile data for quality issues, improve quality, eliminate duplication and integrate data across different databases.
• The pro version: Available in a PC environment, this solution includes all capabilities of the basic version and also enables users to monitor data anomalies and profile database metadata information.
• The desktop version: Available in a PC environment, this solution includes all capabilities of the pro version and enables users to integrate unstructured data with structured data. Users can also get started on a master data management initiative if desired.
• The server-based version of the software is a comprehensive solution that includes all capabilities of the desktop version and is available on a server with up to eight core processors.

Data profiling and monitoring.

• Performs rule validation by applying advanced business rules to ensure information quality.
• Permits data quality analysis of full data or just a subset from the source system. The user can specify the sample interval.
• Identifies redundant information across tables or data sources. The engine supports both exact and fuzzy match redundancy.
• Ensures relationship discovery across tables and databases, and across different source systems.
• Profiles database metadata information to identify relationships between databases' tables to help determine areas that need further investigation.
• Automatically identifies orphaned records within a specific database or across databases.
• Monitors data to detect anomalies including variances, values inside or outside of ranges, values violating mathematical calculations and more.
• Lets you display data violations and trend and compare values over time through a graphical user interface.
• Allows IT and business users to create custom business rules for data being profiled through an interactive interface.
• Exports data profiling results to HTML, Microsoft Excel or text files.

Data quality.

• Unifies data from different databases and source systems with industry-leading matching technology and innovative clustering logic.
• Establishes data hierarchies and reference data definitions, critical for creating a unified view of a particular data entity such as customer, product or supplier.
• Performs gender analysis using an advanced gender analysis algorithm.
• Accurately identifies and categorizes the type of data found within a field using advanced word vocabularies and pattern libraries. For example, it can identify product data such as quantity, packing, shipping and other common data elements.
• Intelligently breaks multivalue fields into parsed elements such as address, city, first and last name, date, phone number, etc. with extensive pattern libraries (to support different data types) and categorized look-up tables.
• Standardizes data with advanced standardization (mapping) routines including element standardization, phrase standardization and pattern standardization to eliminate duplicate and inconsistent data.
• Replaces the original field with a new value or appends a standardized value directly onto the source record.
• Merges multiple files or duplicate records from a single file into one entity or record:
  • Through DataFlux® technology, SAS offers the only matching engine in the industry that generates a single match key for an entity. These keys can be generated in real time, as well as persisted to a data source to facilitate cross-system matching in batch mode.
  • Supports householding by using sophisticated match keys to group records and assign an integer-based unique identifier.

Data integration.

• Data access capabilities let users connect disparate technology systems and streamline business information transfer to and from various technology resources.
• Users can automatically access and integrate unstructured data from emails, old records and invoices, etc., eliminating the need for manual processing – and related delays and errors.
• Data migration capabilities enable you to transfer data to new or different locations while improving the accuracy and consistency of data during migration projects.
• ETL and ELT capabilities allow the solution to flexibly support both traditional ETL as well as in-database processing (an ELT approach) for companies that have invested in in-database appliances.
• Data synchronization capabilities permit batch, frequent-batch and real-time data synchronization, depending on your data-latency requirements.
• Users can build data services once and reuse them multiple times across the organization.

Master data management foundation.

• Integrate master data resource creation and management with comprehensive data management practices.
• Start with a phased approach by creating a hub of master data based on a subset of your existing data.
• Combine master data management capabilities with matching, clustering and other data management initiatives.
• Conduct batch processing with an architecture that supports many master data management implementations without unnecessary complexity.
• Connect to master data management hubs as if they were any other data target.

Single, interactive interface.

• Works as an interface for all data integration and data quality activities.
• Allows both IT and business users to collaboratively create custom profiling, matching, standardization and integration rules.
• Is fully customizable and extensible, allowing users to add new data definitions.
• Lets users create data integration jobs and workflows once and then reuse them across the organization for better consistency and efficiency.