AS 5329:2020 – Workforce data quality
AS 5329:2020 – Workforce data quality.
Section 2 Defining workforce data quality
A high level of workiorce 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 o( nwtrics for reporting internally and/or externally.
NOTE 2 See Appendix A for Impacts of poor data quality.
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.
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.
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.
Consistency in collecting data are essential for comparative analysis. Comparative analysis highlights the direction ofan 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.
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 using the performance audit framework of input, process, output and outcome to allow the metric to beclustered across the activities within the workforce lifecycle.”
NOTE This guideline for internal and external human capital reporting is in accordance with ISo 30414.This framework focuses on a grouping within an activity. Some examples of this include 一(a) inputs for recruitment and talent management;
(b) grouping across activities; and
(c) the entire workforce lifecycle.
An example of this grouping can be seen through the following recruitment process:0
Imput —Resumes received.
(ii) Process — Interviews with prospective employees.(i)
Output — Hiring of an employee, contractor etc.
(iv) Outcome — Impact of hire, i.e. comparing the desired outcome, e.g.sales dollars, customerservice scores etc. against the actual outcome of what the new recruit delivered in thenominated timeframe.
Using the above grouping,the relevant data to use for various metric and assessments shouldbe determined.
3.2 Using the framework
The content In the expansion of the workforce ilfecycle framework will vary across Industries. Organization size will have an impact on what services are undertaken and what data needs to be captured. This framework should be used to document the activities undertaken within the organization.
NOTE Some organizations may not manage an alumnus or have a need for vendor management. Other organizations may break recruitment Into several subsets due to the volume of recruits.
While each organization will determine what exactly is relevant to include in the workforce lifecycle framework the similarities across industries and size of organization will typically outweigh the dl (Terences.
3.3 Generic workforce data framework
The relevant human resource information shall be part of an assurance regime for data quality.
NOTE I The relevant human resource Information to be collected will be determined by each organization.
NOTE 2 Table..13 shows examples of generic data groups to capture.
The following responsibilities shall be allocated:
(a) Setting the timeframes.
(b) Volume of data to be sampled.
(c) Accuracy requirements.
The key workforce data shall be user-defined.
Current and relevant Australian and/or international human resource management standards cover many of the core components of the workforce lifecycle, such as recruitment, learning and development. workforce planning. These standards highlight the most appropriate measures and data points to capture.