WISESTONE

WISESTONEBUSINESS

Quality Certification

DQ Certification(Data Quality Certification)

What is DQ Certification (Data Quality Certification)?

WISESTONE is accredited by the Ministry of Science and ICT as a data quality certification body.
The Data Quality Certification (DQ Certification) is a system established under Article 20, Paragraph 5 (Data Quality Certification Targets and Quality Standards) of the Framework Act on the Promotion of Data Industry and Utilization.
It certifies data quality by diagnosing and evaluating ① content of the data ② the data management system in accordance with international standards and criteria.

[Relevant Legislation]

Framework Act on the Promotion of the Data Industry and Utilization, Article 20

Framework Act on the Promotion of the Data Industry and Utilization, Articles 20-3 to 20-5

Enforcement Rules of the Framework Act on the Promotion of the Data Industry and Utilization, Articles 4-2 and 4-3

DQ Certification targets

The scope of DQ Certification includes all types of data owned or distributed by companies and institutions. This encompasses data generated across a wide range of domains, including financial information, medical data, manufacturing records, distribution management data, and customer behavior data.

+ ViewLarger

Certification benefits

Achieving rapid improvements in data quality within a short timeframe
Ensuring data trustworthiness through certification endorsed by a nationally accre dited certification body
Utilizing certification in marketing and promotional activity

Key Strengths of WISESTONE

The first internationally accredited testing laboratory in Korea specializing in data quality
Issued Korea’s first Data Quality Certification
Extensive experience in conducting data quality testing and certification across numerous organizations in Korea
Operates a dedicated organizational unit for data quality
Possesses a wide range of data quality verification tools

Data content

■ Certification Targets

Structured data (data records, character or numeric values entered in fields, and data sets) or unstructured data (raw data such as images, videos, audio, text, and associated attribute values)

■ Data Types and Grades

- Data Types Classified based on the complexity of data composition - Data Grades Classified into three grades according to the results of the quality assessment ※ All individual assessment items must score 0.95 or higher

■ Structured data evaluation

- Evaluation targets Data that is systematically organized according to a defined structure (schema), such as RDBs (MySQL, Oracle, PostgreSQL, etc.), spreadsheets (CSV files, Excel files), and structured text data. - Evaluation indicators Structured as assessment items to determine the inherent quality of structured data, based on the data quality standards established by the Ministry of Science and ICT and with reference to the quality measurement metrics defined in ISO/IEC 25024.

■ Unstructured data evaluation

- Evaluation targets Unstructured data refers to object data such as images, videos, audio, and text, along with metadata that includes descriptions or annotations such as tagging and labeling. Semi-structured data refers to data that has a partial organizational structure, typically stored in formats such as text files, XML, or JSON, and consisting of key-value pairs. - Evaluation indicators The criteria assess the quality of unstructured data based on the following standards—ISO/IEC 5259-2, the NIA Guidelines for Managing the Quality of AI Training Data, and TTAK.KO-10.1344-Part2—and are aligned with the Data Quality Certification Guidelines established by the Ministry of Science and ICT.

Data Management System

■ Certification Targets

- Data-driven Projects: Long-term projects conducted to generate, process, analyze, and utilize data - Systems: Data systems or platforms established to process and utilize data - Management Organizations: Organizations (teams, departments, enterprise units) that operate data-driven projects or data systems