Introduction

Digital data deliver value by enhancing all aspects of organizational performance including:

— operational effectiveness and efficiency;
— safety;
— reputation with customers and the wider public;
— compliance with statutory regulations;
— innovation;
— consumer costs, revenues and stock prices.

In addition, many organizations are now addressing these considerations with reference to the United Nations Sustainable Development Goals1.

The influence on performance originates from data being the formalized representation of information. This information enables organizations to make reliable decisions. This decision making can be performed by human beings directly and also by automated data processing including artificial intelligence systems.

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:

— the data having a structure that reflects the nature of the subject matter;

EXAMPLE 1

A research scientist writes a report using a software application for word processing. This report includes a table that uses a clear, logical layout to show results from an experiment. These results indicate how material properties vary with temperature. The report is read by a designer, who uses the results to create a product that works in a range of different operating temperatures.

— the data being computer processable (machine readable) rather than just being for a person to read and understand.

EXAMPLE 2

A research scientist uses a database system to store the results of experiments on a material. This system controls the format of different values in the data set. The system generates an output file of digital data. This file is processed by a software application for engineering analysis. The application determines the optimum geometry when using the material to make a product.

ISO 9000 explains that quality is not an abstract concept of absolute perfection. Quality is actually the conformance of characteristics to requirements. This actuality means that any item of data can be of high quality for one use but not for another. This difference occurs when the requirements are different between the two uses.

EXAMPLE 3

Time data are processed by calendar applications and also by control systems for propulsion units on spacecraft. These data include start times for meetings in a calendar application and activation times in a control system. These start times require less precision than the activation times.

The nature of digital data is fundamental to establishing requirements that are relevant to the specific decisions made by an organization.

EXAMPLE 4

ISO 8000‑1 identifies that data have syntactic (format), semantic (meaning) and pragmatic (usefulness) characteristics.

To support the delivery of high‑quality data, the ISO 8000 series addresses:

— data governance, data quality management and maturity assessment;

EXAMPLE 5

ISO 8000‑61 specifies a process reference model for data quality management.

— creating and applying requirements for data and information;

EXAMPLE 6

This document specifies how to exchange characteristic data that are master data.

— monitoring and measuring data and information quality;

EXAMPLE 7

ISO 8000‑8 specifies approaches to measuring data and information quality.

— improving data and, consequently, information quality;

EXAMPLE 8

ISO/TS 8000‑81 specifies an approach to data profiling, which identifies opportunities to improve data quality.

— issues that are specific to the type of content in a data set.

EXAMPLE 9

ISO/TS 8000‑311 specifies how to address quality considerations for product shape data.

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.

EXAMPLE 10

If a data set includes dates in multiple formats including “yyyy‑mm‑dd”, “mm‑dd‑yy” and “dd‑mm‑yy” then data cleansing can correct the consistency of the values. Such cleansing, however, requires additional information to resolve ambiguous entries (such as, “04‑05‑20”). The cleansing also cannot address any process issues and people issues, including training, that have caused the inconsistency.

As a contribution to this overall capability of the ISO 8000 series, this document supports the creation and exchange of high-quality data. This document contains requirements necessary but not sufficient to achieve data quality with respect to the exchange of master data. The requirements do not cover issues such as addressing the accuracy, provenance and completeness of master data. These issues need to be part of an overall data quality strategy adopted by each organization.

"Organization" does not necessarily mean a single, complete company or corporation. The organization can be a subdivision or branch that covers some distinct area of business operation.

When different business units of a company exchange master data or when a business unit exchanges master data with headquarters, these business units are organizations for the purposes of this document.

Organizations can use this document on its own or in conjunction with other parts of the ISO 8000 series.

This document supports activities that affect:

— one or more information systems;
— data flows within the organization and with external organizations;
— any phase of the data life cycle.

By implementing parts of the ISO 8000 series to improve organizational performance, an organization achieves the following benefits:

— objective validation of the foundations for digital transformation of the organization;
— a sustainable basis for data in digital form becoming a fundamental asset class the organization relies on to deliver value;
— securing evidence‑based trust from other parties (including supply chain partners and regulators) about the repeatability and reliability of data and information processing in the organization;
— portability of data with resulting protection against loss of intellectual property and reusability across the organization and applications;
— effective and efficient interoperability between all parties in a supply chain to achieve traceability of data back to original sources;
— readiness to acquire or supply services where the other party expects to work with common understanding of explicit data requirements.

ISO 8000‑1 provides a detailed explanation of the structure and scope of the whole ISO 8000 series.

ISO 8000‑2 2 specifies the single, common vocabulary for the ISO 8000 series. This vocabulary is ideal reading material by which to understand the overall subject matter of data quality. ISO 8000‑2 presents the vocabulary structured by a series of topic areas (for example, terms relating to quality and terms relating to data and information).

Annex A of this document contains an identifier that unambiguously identifies this document in an open information system.

Annex B of this document references an example of a schema to enable exchange of master data that are characteristic data.

BS ISO 8000-110:2021

Data quality - Master data: Exchange of characteristic data: Syntax, semantic encoding, and conformance to data specification

Current

Published:

30 Nov 2021