Automated data quality management tool that introduced artificial
intelligence algorithm for the first time in Korea

Quality of big data can be improved by setting quality indicators, measuring quality, analyzing and monitoring results based on artificial intelligence algorithms.

The need for constant data quality management

In order to take full advantage of the massive amount of data that is accumulated every day, the quality of the data must be excellent.

First, data quality can be secured only when data quality management system is established by defining data standards and rules, and continuous data quality monitoring and improvement work is performed based on this.

100% web-based emphasis on user convenience

It is 100% web-based, emphasizing user accessibility and convenience, as well as supporting various browsers.

All standard information such as DQI (quality index), CTQ (quality item), BR (work rule), and Profile (attribute information) can be registered in Excel, and various status information such as quality status and registration status is provided.

1st in domestic market share, official support tool for data quality certification by Korea Data Industry Promotion Agency

It is an official support tool for the data quality management sector of the Korea Data Industry Promotion Agency (Kdata) and is being applied in various fields with the largest market share in Korea.

In addition, it was recognized for its technological prowess by being listed in the Gartner Newsletter in 2018 and by acquiring the GS Certification Level 1 in 2019.

Acquired 1st grade GS certification
Selected as an innovative product by the Public Procurement Service

System configuration diagram

  • Data maintenance
    STEP1.데이터 탐색, STEP2.이상값 감지, STEP3.데이터 분석, STEP4.데이터 오류 정제 STEP1.데이터 탐색, STEP2.이상값 감지, STEP3.데이터 분석, STEP4.데이터 오류 정제
  • Quality management
    STEP1.품질, STEP2.규칙 기반 데이터 품질 측정, STEP3.측정 결과값 분석, STEP4.데이터 품질 개선 STEP1.품질, STEP2.규칙 기반 데이터 품질 측정, STEP3.측정 결과값 분석, STEP4.데이터 품질 개선

WiseDQ™ Main Features

  • 01
    Rule-based quality measurement
    • Measure the quality of patterns such as table relationship, data duplication, code, date, range, etc. based on business rules
  • 02
    Improving data quality
    • Improve data quality through systematic quality control process and constant monitoring
    • Inquire about data quality status and statistics for each standard information through management of standard information (quality indicators, quality items), etc.
  • 03
    Various platforms
    • Diagnose the quality of various source data such as relational databases and big data platforms
  • 04
    Easy data maintenance
    • Utilizing improvement plans and result management functions, data maintenance work (error data detection and purification)
  • 05
    AI-based quality management
    • Automatic domain determination by analyzing the meaning of the name, data format and pattern
    • Detect text separation errors using text clustering technology
    • Detect abnormal values even without accurate data range or business rules

WiseDQ™ Main Features

  • Improving the efficiency of data quality management activities
    • Improve the efficiency of data quality management activities through convenient functions such as identification of quality status through data quality diagnosis results and data quality diagnosis reports
  • Securing performance and providing up-to-dateness related to data quality diagnosis
    • Secure diagnostic performance by processing quality diagnostic tasks in parallel
    • Securing up-to-dateness of data quality results through periodic data quality measurement through scheduling function
  • Securing the flexibility of diagnostic functions (diagnosing various DBMSs)
    • Supports various DBMSs such as Oracle, Tibero, MYSQL, MARIADB, and CUBRID to measure data quality independent of specific DBMS
  • Automated data quality management by applying AI algorithm
    • Increasing the level of automation in the overall data quality from data preprocessing to quality diagnosis and recommendation for improvement data lowers the time and human cost of data quality managers and improves productivity