Product
Reliable Data Quality Management Solution
Data Quality Trust
WiseDQ
Data Quality Management Solution that Systematically Ensures Data Accuracy, Consistency, and Completeness
Key Features
01
AI-based Quality Diagnostics
By analyzing data characteristics, AI automatically recommends and maps optimal validation rules
02
Database Diagnostics
Unrestricted diagnostics across commercial DBMS (Oracle, Tibero, PostgreSQL) as well as Open-Source Databases
03
Public Data Quality Level Assessment
Ensuring data reliability for public Institutions and enterprises by providing diagnostic functions optimized for evaluation indicators
04
Integrated Management of Structured and Unstructured Rules
In addition to the standard validation rules provided by default, the system integrates and manages unstructured rules (SQL) that reflect an organization’s specific business logic
05
Metadata System Integration
Integrates with the metadata management system to enable real-time sharing of data standards, structures, and value information
06
Quality Improvement Process
Supports the entire quality management lifecycle from error data detection and root cause analysis to improvement planning and verification of corrective actions through a workflow-based approach
Experience data quality management for AI-Ready data
AI-based validation rules
AI analyzes data characteristics to automatically recommend and map appropriate validation rules
Value diagnostics and validation
Precisely inspects data value formats such as date formats, number patterns, and code validity and immediately detects discrepancies by comparing standard data with actual data
Profiling analysis
Analyzes inter-table relationships (referential integrity) and duplicate data, and comprehensively examines column-level data frequency, NULL values, and minimum/maximum values to identify structural data issues
Real-time monitoring and scheduling
Provides scheduling capabilities for periodic quality measurements and proactively prevents quality degradation by monitoring diagnostic progress and error occurrences through real-time dashboards
Assess quality levels using objective metrics and provide recommendations for improvement
Data Quality Index (DQI) Management
Define and manage data quality metrics—such as accuracy, completeness, and consistency—by establishing target levels and performance standards
Focused Management of Critical-to-Quality (CTQ) Information
Identify and closely manage critical data elements (CTQs) that could have a significant impact on the business
Quality Level Assessment Report
Evaluate overall quality levels by comprehensively analyzing data standard compliance rates, structural consistency rates, and value error rates, and automatically generate detailed assessment reports
Error Data Tracking and Improvement
Provide functionality to track detailed information on detected data errors and analyze their root causes
A quality management environment that is easy and convenient for anyone to use
Web-Based Integrated Portal
Provide an integrated portal environment that enables users to access anytime, anywhere through a web browser—without installing additional software—to perform quality diagnostics, review results, and carry out improvement activities
Intuitive User Interface
Through a user-friendly UI/UX, users can easily understand the status of data quality even without specialized knowledge
Robust Security and Access Control
Granularly control menu and data access permissions by user and group, and prevent data leakage and misuse through personal data encryption and comprehensive activity log management
Flexible System Scalability
Supports both cloud and on-premises deployments, and enables flexible integration with existing systems through compatibility with the e-Government Standard Framework
Drive data quality innovation with WiseDQ
WiseDQ, Proven in a wide range of industries
Finance
- Data Migration through Data Error Identification and Cleansing
- Financial Data Consistency Validation
Public Sector & Welfare
- Error Diagnosis of Sensitive Administrative Data
- Response to Public Data Quality Level Assessment
Safety & Infrastructure
- Integrated Maintenance and Management of Safety Information Systems in Response to Regulatory and Policy Changes
- Improvement of Data Redundancy, Inconsistency, and Inefficiency
Life & Environment
- Data Harmonization of Forest Regions to Reduce Fine Particulate Matter
- Automated Data Validation Based on Consistency Rules