QualityStream™ is a system that continuously manages, maintains and improves quality of data by approaching to target data to diagnose quality, draw diagnosed results and analyze them.

After analyzing current level of quality by profiling target database, it manages data by analyzing results of registering (business rule) and scheduling, and by using error correction process.

System Architecture
  • QS-Base
    As a Java-based quality management framework, it supports linkage between UI and servers as well as linkage and integration with quality analysis engine. As it is managed by integrated repository, it can be completely linked with company’s products.
  • QS-Broker
    Consisted of modules for analyzing quality of data, it is a core batch module composed of modules of meta data linking and meta information collection engine, and engines of analyzing each function.
  • QS-Base-UI
    It provides user screen for quality analysis and control that displays a variety of functions, such as defining function and scheduling for analysis, process for control, and managing maintenance plans and results.

For improving quality of data, QualityStream™ provides basis for verifying quality of data, focused on quality management index, data quality verification, statistics of quality verification results and maintenance processes.

Meta data management
• Support management of meta information in database that is basis for quality diagnosis, and change management
Profiling management
• Manage major base information for quality diagnosis and verification target information in conjunction with schedulers
Rule management
• Support management and analysis of complicated business rules in conjunction with schedulers
Management of verification results
• Comprehensively manage results of quality diagnosis, and provide diagnosis results by certain criteria and statistical information
Maintenance management
• Support verification and analysis of error-data as well as continued assurance of data quality by supporting maintenance processes
Real-time quality management
• Support real-time quality management by linking with company’s tool (CDC)
Support of quality Potal
• Support a separate Potal system, company’s integrated system of controlling quality management solution

It manages data quality index (DQI) and extracts and analyzes error-data based on it to manage quality of data in data governance.

  1. Support to build quality management system by establishing data quality management processes
  2. For management of quality of customer’s data, select verification criteria index and core target data of management to continuously manage them
  3. Able to accurately identify errors by creating more precise statistical index by managing statistics based on 6 sigma criteria
  4. Innovate quality management by supporting analysis of profiling business rule
  5. Comprehensively manage quality of structure and data in integrated repository by linking with Stream product group
  6. Expect to continuously reduce error rate by supporting maintenance plan and control function of error-data
  7. For quality management of customer’s data, select verification criteria index and target data of management, and then continuously manage them