# A Study on Data Profiling: Focusing on Attribute Value Quality Index
## Materials and Methods  
### Structured Data Quality Factors  
Data quality is defined as a level that can provide useful value to users by securing data accuracy, interconnection, and up-to-date data [1; 5]. In addition, it is defined as a level that can be continuously satisfied for various purposes or for the satisfaction of users who utilize data [6; 7]. Data quality management refers to a series of activities performed to maximize user satisfaction by continuously maintaining and improving the quality of data [5; 8]. The data quality error rate is calculated by applying the weight by area according to the importance of the value (70%), structure (20%), and standard (10%) of the data in the public data quality management manual [8]. This study provides seven big data quality indexes to measure the quality level of public institution data and 24 detailed indexes which reflect the detailed characteristics by index [8; 9]. The seven indexes are readiness, completeness, consistency, accuracy, security, timeliness, and usability. Readiness is an index related to the preparation of policies, organizations, and procedures for quality control of data and it measures whether the data are faithfully managed with the latest contents. As a logical and physical structure in establishing a database, completeness is an index that measures whether data are stored in accordance with business requirements. Consistency is an index that measures whether data that have the same meaning conform to the standard with a consistent name and format and whether data sharing and linking remain consistent. Accuracy is an index that measures whether the stored data are in a range and format of values that fit the defined criteria and whether the data reflect the most recent value. Security is an index that measures whether data generation management subjects are managed and whether the security measures such as data access management level, authority of data, and encryption of important data are performed. Timeliness is an index that measures whether procedures for collection, processing, and provision according to data requirements are managed based on the level of response time, as well as on data requirements for the level being satisfied. Usability is an index that measures the level of data that users are satisfied with, the level of their convenience when accessing data, and whether they are working to improve the convenience of data usage. In a study on the quality factor of big data, it was suggested that it is desirable to determine the quality of data according to whether it is suitable for the purpose of use from the viewpoint of the user based on big data processing technology [10]. In terms of users, data types are classified into intrinsic, accessibility, contextual, and representational quality [10]. In order to secure data quality, it is said that the quality factors should be defined in consideration of data values, business rules, data standards, data hierarchy, and characteristics of industrial sites, and that continuous management is required through data quality management infrastructure (tool/system), quality management policy, and organization [11]. This study aims to find the quality index centered with a data value which is relatively important for big data quality measurement, which is rapidly increasing.