DQ#
Support business decision-making by data quality management
DATAWARETM DQ# systematically manages quality issues such as inaccuracy, redundancy, and inconsistency of data caused by the expansion and complexity of information systems operated by companies. It makes data become a real asset of the company.
Provides convenient functions and monitoring functions
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1
Linkage and extensibility
- Web-based GS 1st grade certificated solution supporting various platforms (OS, browsers)
- Automatic linkage with various modeling tools, including application impact analysis, data quality management, and data flow management
- Support public data quality management level assessment -
2
Efficient handling of large amounts of data
- Fast and stable processing of large data such as profiling and BR verification by applying large database handling techniques
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3
Efficient data quality management
- Efficient quality management through data quality standard definition, business rule management, quality measurement, result analysis and error correction
- Provide status information on various quality management such as quality status and registration status.
- Providing metadata-based quality management through linkage with metadata and modeling tools
Data quality management
Securing optimal data quality through continuous data quality management and visible monitoring
Provides convenient functions for data quality management
and monitoring function
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01
Define data quality management standards - Supports classification patterns for data quality management
in a hierarchical structure.
- Each level is subject to role-based authority management
- Data quality indicator management and key quality management
target management functions. -
02
Profiling and business rule management - Performs profiling for statistical analysis of data and defines and
manages business rules for quality measurement.
I is registered collectively using model information. -
03
Continuous quality improvement management - It supports efficient use of resources and periodic data quality measurement by scheduling and registering time-consuming
tasks such as data profiling and business rule verification. -
04
Analysis of the causes of data quality errors - Provides cause analysis and improvement management functions for error data, provides error data status by quality standard.
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05
Analysis of data quality measurement results - Key quality targets, quality indicators, and data quality status by business rule are provided in figures and charts.
Web-based solution support any OS, any browsers
