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GB/T 36344-2018Information technology—Evaluation indicators for data quality (English PDF)

信息技术数据质量评价指标

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Issued by

SAMR; SAC

Level / Type

National · Recommended

Issue date

June 7, 2018

Implementation date

January 1, 2019

Scope

GB/T 36344-2018 is the English-translated version of 信息技术数据质量评价指标.

GB/T 36344-2018 gives a framework of indicators for evaluating the quality of data, together with a description of each indicator. It defines terms such as data, metadata, data quality, raw data, data lifecycle, data set, data model and data standard.

The indicators cover, among others, compliance with the data model, with metadata, with business rules and with authoritative reference data, data format compliance, the data repetition rate, data uniqueness and the occurrence rate of dirty data, most of them measured as a ratio X = A/B between the elements that meet a requirement and the elements evaluated.

An informative annex outlines the data quality evaluation process. It applies to the evaluation of data quality at every stage of the data lifecycle, was issued on 7 June 2018 and has applied since 1 January 2019.

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Document preview — GB/T 36344-2018

National Standard of the People's Republic of China

ICS: 35.240.01
Classification: L 70

Issued by: State Administration for Market Regulation; Standardization Administration of the PRC

Foreword3
1 Scope4
2 Terms and definitions4
3 Frame of indicators5
4 Overview6
5 Indicator description6
Annex A (informative) Data quality evaluation process13
Bibliography14

1 Scope

This Standard specifies the frame and description of evaluation indicators for data quality.

This Standard is applicable to data quality evaluation of data lifecycle at each stage.

2 Terms and definitions

For the purposes of this document, the following terms and definitions apply.

2.1 Data

formal representation of reinterpretable information so as to suit communication, interpretation or processing

NOTE: Data can be processed manually or automatically. [GB/T 5271.1-2000, Definition 01.01.02]

2.2 Meta data

data about data or data elements (possibly including their data descriptions), and data about data ownership, access paths, access rights, and data variability [GB/T 5271.17-2010, Definition 17.06.05]

2.3 Data quality

the extent to which the characteristics of data meet clear and implied requirements when used under specified conditions

2.4 Raw data

various unprocessed or simplified data stored by the end user

NOTE: Raw data has multiple forms of existence, such as text data, image data, audio data, or a mixture of several types of data.

2.5 Data lifecycle

a set of processes that turn raw data into actionable knowledge

2.6 Data set

a collection of data that has a certain theme that can be identified and can be computerized

2.7 Data model

expression of images and texts for analysis; the analysis identifies the data the organization needs to complete its mission, function, goals, objectives, and strategy, as well as manage and evaluate the organization

NOTE 1. When representing data at different levels of abstraction from high to low, it is often distinguished between conceptual models (models composed of concepts related to certain efforts), logical models, and physical models.

NOTE 2. The formal description of the boundary of the use of the data model used, is called the context mode.

NOTE 3. The data model identifies entities, domains (attributes), and relationships (associated) with other data, providing a conceptual view of the relationship between data and data.

EXAMPLE 1. For semantic data model consisting of block diagrams, this box represents a set of transactions that are meaningful to the business, such as "people" or "actions", and lines that describe the relationships between pairs of such entities.

EXAMPLE 2. A relational table that applies specific data management techniques or an extensible markup language XML is a logical data model.

2.8 Data standard

rules and benchmarks for naming, definition, structure, and value specification of data

3 Frame of indicators

Refer to Figure 1 for the frame of evaluation indicators of data quality.

5 Indicator description

0103 Meta data The measurement that data meets meta data.

NOTE: Meta data labels, describes, or portrays other data to make it easier to retrieve, or use, information. When evaluating data quality, it needs to check if an interpretable metadata document is available.

Example: A meta data dictionary containing the contents of each field name, description, type value field, etc. is a meta data document.

X=A/B, where:

A = The number of elements in the data set that meet the meta data requirements.