AS 5329:2020 pdf free.Workforce data quality.
2.1 General
A high level of workforce data quality ensures the basis of decision-making is complete. When considering what data to capture, refer to relevant Australian and international human resource management standards.
NOTE 1 Refer to ISO 30414:2018 Table 2 which highlights and recommends a number of metrics for reporting internally and/or externally.
NOTE 2 See Appendix A for impacts of poor data quality.
2.2 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 and should be aligned to relevant industry guidelines. The current accuracy should also form the basis for future targeted accuracy.
2.3 Timeliness
Timeliness of data capture and the availability of the data for decision-making and reporting should be taken into account. All required recruitment activities, e.g. resume validation, reference checking, psychological assessment, should be completed prior to employment being offered and commencing.
2.4 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 sighted or verified;
(b) referees have not been contacted; and/or
(c) information has not been recorded.
The risks to the organization and the individual in these instances cannot be underestimated. Mandatory completion of all data collecting, e.g. employment forms, before proceeding to the next stage of the process, may alleviate many of these types of issues.
2.5 Consistency
Consistency in collecting data are essential for comparative analysis. Comparative analysis highlights the direction of an issue being monitored such as an increase in workforce turnover.
To be consistent, consideration should be given to —
(a) timing of the data collecting, e.g. monthly, bi-monthly;
(b) formula used in any calculation not varying; and
(c) data points used not varying.
2.6 Relevance
Not all data collected is valuable. Many data points currently being measured are interesting but not significant. One way to understand the importance of data and measurement is to use a framework that allows for the grouping of data points and/or measurements. For simplicity, group the resulting metric.AS 5329 pdf download.