DataFlux^® qMDM

Features

Best-in-class data quality and data management capabilities

• Standardize and rationalize data during the MDM data life cycle with capabilities that include:
  • Data access.
  • Data profiling.
  • Data quality, including address verification, data enrichment, standardization, parsing, etc.
  • Data matching, linking and householding.
  • Business rule monitoring.

Multiple-domain data models

• Create master data domains for party (citizen, customer, student, patient, etc.), organization, site, supplier, product, asset or other data elements:
  • Identify and centralize key core reference data.
  • Grow from batch-oriented maintenance to real-time application integration through SOA.

Master data repository: hub loading and maintenance

• Customize data models and transformation/hub-loading processes for common entities
  (person, organization, location, etc.).
• Support for hub loading and maintenance processes with:
  • Out-of-the-box models that are extensible and user-created models that can be easily and seamlessly integrated with other models.
  • Built-in data quality functionality such as address verification, standardization, parsing, etc.
  • Entity matching, source system record linking, cross-referencing and best-record creation.
  • Data-rule monitoring.
  • Historical tracking with changes saved for contributing records and best records.
  • Initial load and incremental transformation/load capabilities.

Browser-based data governance and data stewardship console

• Control the master data hub, as well as the processes and workflows that govern the creation of master data, through an intuitive, business-focused interface that lets you:
  • Search and display entities.
  • Add, modify and retire entities and hierarchies.
  • Create new hierarchies and modify them.
  • Perform entity resolution and survivorship tasks with clusters of entities.
  • Manage access to attribute content by role, allowing only authorized individuals to view sensitive attribute content.
  • Manage metadata for entities, attributes and transformations.
  • View dynamic and batch reports.
  • Support the consistent maintenance and use of master data attributes through a common data model and centralized data maintenance and access processes.
  • Ensure ongoing conformance with rules and quality using data profiling and business-rule monitoring.
  • Create business glossaries and reference data structures with technologies that are integrated into the solution (requires additional license).

Entity resolution and matching

• Create and maintain the master record using a patented data-matching engine.

Survivorship

• Validate master data representation through entity resolution, best-record selection and editing, and the creation of a universal identifier.

Batch and real-time deployment

• Import data, service inquiries and searches into the hub through batch or real-time (service-oriented architecture) interfaces.

Business glossary creation and management

• Create and manage a glossary of business terms using a Web-based interface.
• Graphically explore business terms and categories.
• View relationships across elements.
• Use audit trails to know what data was changed and who changed it.
• Store and apply definitions to guide all data management processes.
• Reuse, manage and reports on the use of a single glossary.
• Safeguard sensitive or protected information through read-only access to glossary content data.

Reference data structure creation and management

• Store and manage different types of hierarchies using a Web-based interface.
• Identify, profile and validate reference data from any source system.
• Integrate accurate and reliable reference data.
• Maintain and manage history and date changes from a single interface.
• Create and manage business rules across processes.
• Integrate business terms and glossaries defined in the DataFlux Business Data Network.
• Create enterprisewide standards for data quality at an attribute level.

DataFlux^® Master Data Management Foundations for an evolutionary approach to MDM

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