Enterprise Data Quality Management Solution
DQMiner¢ç helps increasing organization¡¯s competitive advantage by evaluating and managing data quality, preventing poor data quality problems and improving better decision making.
Overview
Data is the most valuable asset for companies. DQMiner¢ç is an enterprise data quality management solution that provides automatic data profiling, data auditing, data rule management, data quality analysis, and data quality reporting. DQMiner¢ç also supports customer to implement regular data quality monitoring system to accomplish data quality goal by offering unique data quality management methodology.
Differentiators
DQMiner's Differentiators
Data quality management methodology |
DQMiner¢ç is a data quality management solution that has proven data quality management methodology (Sigma+4 DQM) and provides best practices form various project experiences. |
Root cause tracking |
DQMiner¢ç reduces time for checking and removing root cause of error data, programs, by integrating with ChangeMiner¢ç. It provides information on source code line of programs which input wrong data to database. |
Proven solution |
DQMiner¢ç is proven by many customers implemented large volume of data quality projects. It has been selected as an official tool for government organizations¡¯ database quality evaluation by Korea Database Promotion Center. |
Flexibility |
DQMiner¢ç supports quality management for variety of DBMS like Oracle, DB2, MS-SQL server, Sybase and so on. |
Key Features
DQMiner's Key Features
Web Portal |
It allows users to share the result of data quality diagnosis and data quality management process through web environment. |
Data quality management |
It designs detail scenarios of data quality management activities and manages scheduled execution of them. |
Data profiling |
It supports statistic analysis of basic data value and structure. It provides profiling information on column, date, pattern, referential integrity and code and so on. |
Business rule management |
It manages business staffs¡¯ knowledge as data check rules (business rule) and checks data violate the rules. |
Multi-dimension analysis |
It supports multi-dimensional analysis among heterogeneous databases. It also verifies data comparison between source and target and analyzes quality index trend. |
Root cause tracking of error data |
It traces source code which would be the root cause of error data (integrating with ChangeMiner is required) |
Data cleansing |
It supports continuous quality improvement by analyzing root cause of error data and checking result of improvement activities. |
Report |
It generates various reports about quality measurement (error data list) and improvement. |
Benefits
DQMiner's Benefits
Risk management |
DQMiner¢ç reduces risks caused by poor data quality and increases company¡¯s competitiveness. It also maximizes customer satisfaction by maintaining data integrity through continuous monitoring system. |
Time-to-market |
Decision makers can make decisions precisely by utilizing high quality data. Business users can respond to rapidly changing environment through faster business process. |
Cost reduction |
DQMiner¢ç reduces management cost and increases efficiency of enterprise data quality management with clear responsibility/rights about data and procedure guideline. |