□ 데이터 품질점검 절차 및 기법
데이터 품질점검 절차 및 기법 Ver1.0 [IT information] Data quality inspection procedure and technique Ver1.0
Data has already become an absolute necessity in daily work, and a lot of data is generated in the process of performing work. How to classify and systematically store and utilize this data is also the biggest issue of information systems.
According to the 2008 IDC report, the amount of digital information in Korea is analyzed to increase at an average annual rate of 57%. are doing
On the other hand, digital information can be divided into unstructured data such as images and voices and general structured text data. Among them, unstructured data accounts for 92% of the total amount of information, and structured text data only accounts for 8%. It can be seen that digital information occupies an overwhelming proportion.
However, until now, quality control of data has been biased toward structured text data, and is very weak for unstructured and multimedia contents such as video, image, and 3D. there is no way.
Accordingly, the purpose of this guideline is to develop and propose an integrated quality management method that encompasses both structured and unstructured data, to promote the quality improvement of the entire information system data, and to contribute to the improvement of organizational work efficiency and customer satisfaction with content utilization.
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| Data quality inspection procedure and technique |
[index]
Chapter 1 Overview of Data Quality Diagnosis
Section 1 Purpose of Data Quality Assessment Guidelines
Section 2 Definition and Types of Data Quality Diagnosis
Section 3 Scope and Composition of Guidelines
Chapter 2 Data Quality Diagnosis Procedure
Section 1 Quality Diagnosis Plan Establishment
1. Data Quality Diagnosis Project Definition
2. Defining the performing organization
3. Definition of quality assessment procedure
4. Finalize the detailed implementation plan
Section 2 Quality Criteria and Definition of Diagnosis Subjects
1. Selection of data quality standards
2. Investigation of quality issues
3. Collection of data management documents
4. Evaluation of the importance of the subject of diagnosis
5. Selection of quality assessment targets
6. Selection of key quality items
7. Data Profiling
8. Derivation of business rules
Section 3 Data Quality Measurement
1. Establishment of quality measurement plan
2. Prepare a quality measurement checklist
3. Perform data quality measurement
4. Report on data quality measurement results
5. Comprehensive report on data quality
Section 4 Analysis of Data Quality Measurement Results
1. Analysis of causes of quality errors
2. Derivation of quality improvement plan
Section 5 Data Quality Improvement
1. Establishment of quality improvement plan
2. Implementation of improvement activities
3. Report of improvement results
Chapter 3 Data Quality Diagnosis Techniques – Structured Data
Section 1 Data Profiling
1. Metadata collection and analysis
2. Analysis of column properties
3. Profiling techniques by type
4. Review and synthesis of profiling results
Section 2 Business Rules
1. Procedure for Deriving Business Rules
2. Guidelines for writing business rules
3. Business rules and BR-SQL examples
Section 3 Data Quality Measurement
1. Measurement of error rate by work rule
2. Measurement of error rate by core data
3. Data Quality Index
Section 4 Error Cause Analysis
1. Causes of error data
2. Error cause analysis method
3. Cases of cause analysis by quality standard
Chapter 4 Data Quality Diagnosis Techniques – Unstructured Data
Section 1 Data Profiling and Business Rule Derivation
Section 2 Checklist Preparation
1. Selection of measurement criteria
2. Calculation of importance
3. Creation of measurement items
4. Preparation of measurement details
Section 3 Quality Measurement and Calculation of Quality Index
1. Calculation of quality score
2. Calculation of quality index
3. Calculation of total quality index
4. Error rate measurement
Section 4 Error Cause Analysis
1. Causes of error data
2. Error cause analysis method
Source: Korea Database Promotion Agency
