ISO 30435:2023
(Main)Human resource management — Workforce data quality
Human resource management — Workforce data quality
This document provides the basis for organizational improvement underpinned by quality workforce data that supports analysis and evidenced-based decision-making. Workforce data quality, for the purpose of this document, is the process of: a) workforce data determination; b) data capture; c) data maintenance; d) data review processes. The focus of this document is on the quality of data itself rather than on other critical aspects relating to the overall management of data, such as privacy and security of personal data, the analysis and reporting of data and the use of technology. This document does not cover the analysis of data and reporting structure or the definition of any metric other than quality of workforce data. Technology platforms in the form of human resource information systems, databases, spreadsheets, and the like, can improve the process of the capture and management of data. The focus of this document is the data itself. While data security and data privacy are also critical to the overall management of data, they are not dealt with in this document. This document is applicable to all types and sizes of organization across all industry sectors and regions.
Management des ressources humaines — Qualité des données de la main d'œuvre
General Information
Standards Content (Sample)
INTERNATIONAL ISO
STANDARD 30435
First edition
2023-07
Human resource management —
Workforce data quality
Management des ressources humaines — Qualité des données de la
main d'œuvre
Reference number
© ISO 2023
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ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Defining workforce data quality . .1
4.1 General . 1
4.2 Accuracy . 2
4.3 Timeliness . 2
4.4 Completeness . 2
4.5 Consistency . 2
4.6 Relevance . 3
5 Workforce life cycle framework .3
5.1 General . 3
5.2 Expanding the workforce life cycle framework . 3
5.3 Using the framework . . 5
5.4 Generic workforce data framework . . 5
6 Data capture and maintenance requirements . 6
6.1 General . 6
6.2 Current and future workforce data capture and maintenance . 7
6.3 Historic data cleansing and collation . 7
6.3.1 General . 7
6.3.2 Historical workforce data management time frames . 8
6.3.3 Cost of historical data cleansing. 8
6.3.4 Historic workforce data project team . 8
7 Data integrity review process .9
7.1 Workforce data quality review process . 9
7.2 Reviewing workforce data management processes report . 9
7.3 Management assurance . 9
7.4 Time frames . 9
Annex A (informative) Impacts of poor data quality .11
Bibliography .13
iii
Foreword
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This document was prepared by Technical Committee ISO/TC 260, Human resource management.
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iv
Introduction
Workforce data quality is concerned with policies, processes and practices for:
— identifying and determining relevant workforce data;
— data capture;
— data maintenance;
— data review.
This document establishes the importance of data quality and the validity and consistency of
comparative data across functions, size, specific segmentation, industry sector, industry and region.
This document also provides a basis for continual improvement in the quality of workforce data that
are available for analysis and to support evidence-based decision-making.
The application of this document’s guidelines assists organizations to make informed decisions based
on high-quality workforce data. The economic and social benefits derived from improved decision-
making are substantial and, along with the impacts of poor workforce data quality, are discussed in
Annex A.
v
INTERNATIONAL STANDARD ISO 30435:2023(E)
Human resource management — Workforce data quality
1 Scope
This document provides the basis for organizational improvement underpinned by quality workforce
data that supports analysis and evidenced-based decision-making.
Workforce data quality, for the purpose of this document, is the process of:
a) workforce data determination;
b) data capture;
c) data maintenance;
d) data review processes.
The focus of this document is on the quality of data itself rather than on other critical aspects relating
to the overall management of data, such as privacy and security of personal data, the analysis and
reporting of data and the use of technology.
This document does not cover the analysis of data and reporting structure or the definition of any
metric other than quality of workforce data. Technology platforms in the form of human resource
information systems, databases, spreadsheets, and the like, can improve the process of the capture and
management of data. The focus of this document is the data itself.
While data security and data privacy are also critical to the overall management of data, they are not
dealt with in this document.
This document is applicable to all types and sizes of organization across all industry sectors and
regions.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
No terms and definitions are listed in this document.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
4 Defining workforce data quality
4.1 General
A high level of workforce data quality provides the foundation for evidence-based decision-making to
support organizational performance and continual improvement.
Key considerations are the identification and capture of relevant data. The organization should
determine what data is material to the purpose of the organization and its strategic objectives.
When considering what data to capture, refer to the relevant national and international human resource
management standards.
This document focuses on key elements of workforce data quality: accuracy, timeliness, completeness,
consistency and relevance. Further information on overall data quality can be found in the ISO 8000
series.
NOTE 1 Refer to ISO 30414:2018, Figure 2, which highlights and recommends a number of metrics for
reporting internally and/or externally.
NOTE 2 See Annex A for the impacts of poor data quality.
NOTE 3 Refer to ISO 8000-1 and ISO 8000-2 for further information on general data quality.
4.2 Accuracy
To determine the level of accuracy required for various data, first consider the nature of the data being
captured. The accuracy of the data determined to be important should be aligned to relevant industry
guidelines. The current accuracy should also form the basis for future targeted accuracy. Table 4 offers
an example of data accuracy requirements.
4.3 Timeliness
Timeliness of data capture and the availability of the data for decision-making and reporting should be
considered. For instance, when considering termination data, the reason can be different at the time of
termination than a few months later, or if engagement data is being collected from one group at the start
of a shift and another group at the end of a shift, the trends can be affected by the timing of collection.
4.4 Completeness
Completeness regularly becomes an issue when forms are partially filled in and/or data partially
collected. Some examples of this include:
a) qualifications have not been regularly sighted or verified;
b) referees have not been contacted;
c) performance information has not been recorded.
The risks to the organization and the individual in these instances cannot be underestimated. Complete
data collection should be mandatory. Further, the responsibility for checking, recording a
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