IS/ISO 8000-1 : 2022 Data Quality - Part 1 Overview
NATIONAL FOREWORD
This Indian Standard which is identical to ISO 8000-1 : 2022 'Data quality - Part 1: Overview' issued by the InternationalOrganization for Standardization (ISO) was adopted by the Bureau of Indian Standards on recommendation of the Industrial Automation Systems and Robotics Sectional Committee had been approved by the Production and General Engineering Division Council.
Other parts in this series are:
Part 2 Vocabulary
Part 8 Information and data quality - Concepts and measuring
Part 60 Data quality management - Overview
Part 61 Data quality management - Process reference model
Part 62 Data quality management - Organizational process maturity assessment: Application ofstandards relating to process assessment
Part 63 Data quality management - Process measurement
Part 64 Data quality management - Organizational process maturity assessment: Application of the testprocess improvement method
Part 65 Data quality management - Process measurement questionnaire
Part 66 Data quality management - Assessment indicators for data processing in manufacturing operations
Part 81 Data quality assessment - Profiling
Part 82 Data quality assessment - Creating data rules
Part 100 Master data - Exchange of characteristic data: Overview
Part 110 Master data - Exchange of characteristic data: Syntax, semantic encoding, and conformance todata specification
Part 115 Master data: Exchange of quality identifiers - Syntactic, semantic and resolution requirements
Part 116 Master data - Exchange of quality identifiers - Application of ISO 8000-115 to authoritative legalentity identifiers
Part 120 Master data - Exchange of characteristic data - Provenance
Part 130 Master data - Exchange of characteristic data - Accuracy
Part 140 Master data - Exchange of characteristic data - Completeness
Part 150 Data quality management - Roles and responsibilities
Part 311 Guidance for the application of product data quality for shape (PDQ-S)
A list of all parts in the IS/ISO 8000 series can be found on the BIS website.
Through widespread adoption of digital computing and associated communication technologies, organizations become dependent on digital data. This dependency amplifies the negative consequences of lack of quality in these data. These consequences are the decrease of organizational performance.
The biggest impact of digital data comes from two key factors:
a)the data having a structure that reflects the nature of the subject matter; and
b)the data being computer processable (machine readable) rather than just being for a person to read and understand.
IS/ISO 9000 explains that quality is not an abstract concept of absolute perfection. Quality is the conformance of characteristics to requirements. This actuality means that any item of data can be of high quality for one purpose but not for a different purpose. The quality is different because the requirements are different between the two purposes.
Data quality management covers all aspects of data processing, including creating, collecting, storing, maintaining, transferring, exploiting and presenting data to deliver information. Effective data quality management is systemic and systematic, requiring an understanding of the root causes of data quality issues. This understanding is the basis for not just correcting existing nonconformities but also implementing solutions that prevent future reoccurrence of those nonconformities.
The text of ISO standard has been approved as suitable for publication as an Indian Standard without deviations. Certain conventions are, however, not identical to those used in Indian Standards. Attention is particularly drawn to the following:
a) Wherever the words 'International Standard' appear referring to this standard, they should be read as 'Indian Standard';and
b) Comma (,) has been used as a decimal marker while in Indian Standards, the current practice is to use a point (.) as thedecimal marker.