Neil McCormick and Dr. Chris Andrews discuss the need for a globally recognized, structured approach, to managing and maintaining the overall workforce data quality in organizations.

HR Data Quality

This article is co-authored by Dr. Chris Andrews and Neil McCormick.

It is apparent that the ongoing globalization of Human Resource Standards and Human Capital reporting, in particular, will drive the need for better quality HR data.

In late 2018 John Sumser published an interesting article on ‘Value & data’ aspects of which go to the heart of HR data quality. His statement that ‘Data depreciates. Usually rapidly’ is a key issue for most organizations.

In ‘The Global movement for Human Resource Standards’ the authors discussed the importance of global standards and, in particular, the Human Capital Reporting Standard. Just last week, Neil published the latest news and his analysis on where Human Capital Reporting (ISO DIS 30414) stands today. All through this time the authors of this article engaged in a deep discussion about a ‘Workforce Data Quality Standard.’ Today, we want to go back and dig into the critical issue of HR data quality.

There is currently no globally recognized, structured approach to managing and maintaining the overall workforce data quality in organizations. Additionally, workforce data is often poorly organized, and there is complexity over the ownership/responsibility for this data which is segmented and siloed across most organizations. The function of HR is typically responsible for some but not all relevant data.

In a proposal put forward to Standards Australia Committee MB-009 on the need for a workforce data quality standard, the submission focused on the benefits of high-quality data to organizational performance and the potential costs to organizations of poor-quality data. The quality of data sits at the very heart of evaluating and elevating performance and for understanding the outcomes achieved from HR initiatives.

Friedman & Smith (2011) stated that ‘research shows that 40% of the anticipated value of all business initiatives is never achieved. Poor data quality in both the planning and execution phases of these initiatives is a primary cause.’ For HR staff involved in change management, this should be a significant concern. It was also reported that ‘data quality effects overall labor productivity by as much as 20%.’ Even if we moderate that number to just 5%, it is a material amount of lost productivity.

As organizations become more automated the importance of data quality increases. With a focus on business intelligence, the significance of accurate baseline data also increases. Historic HR data is also an issue; for example, organizations wanting to contact past employees need to be sure those who are deceased are removed from the data set, emergency contacts in HR systems need to reflect current household arrangements rather than past relationships. Immigration status and visa restrictions are other areas where it can be essential to establish an employee’s ‘right to work’ for the organization.

It is not uncommon to find that an organization does not know how many employees it has and in what locations or job functions. There can also be differences between HR reports, finance reports and line management reports relating to headcount and full-time-equivalence (FTE) definitions. Often the total workforce, which includes contractors, consultants, and volunteers, is a very different figure to those on the payroll report. When calculating profit-per-employee and evaluating performance, these matters are material.

Some examples where data quality affects productivity would include wrongly addressed communications in the midst of a significant change program (e.g. where a relevant employee group is left off the distribution list), the misclassification of low performing staff as average performers, or a high performing staff member classified as an average performer, presenting the organization with a retention risk.

Central to the issue of data integrity is the need to establish an organizational process in which HR data is considered to be a key governance issue (Young & McConkey, 2012). Some of the considerations include: privacy, security, responsibility for data quality, data storage and retention, the preservation of data from previous HR systems, data integration across HR and other systems, key data collections, access to data by non-HR staff, methods for fixing data errors or collecting missing data, data dictionaries (explaining the meaning of common terms), and the consistent time frame for cleansing data sets. HR professionals and internal auditors should have the benefit of a Workforce Data Quality Standard to guide better practice – hopefully, the work with Standards Australia will be beneficial for practitioners in other locations.

Some of the dimensions of data quality (Bovee et al, 2003) include accessibility, interpretability, relevance, and integrity. Integrity can be sub-divided into accuracy, consistency, completeness, and existence. Relevance can be sub-divided into datedness, including age and volatility, and various user-defined criterion. An example of user-defined criteria might be establishing the minimum qualification, licensing or registration requirements for a job function.

Our modest conclusion from considering the issue of HR data quality is that high-quality HR data pays off in improved productivity while poor quality HR data costs an organization in lost opportunity.


Bovee et al. (2003), A conceptual framework and belief-function approach to assessing overall information quality, International Journal of Intelligent Systems, Vol 18, 51-74.

Friedman & Smith (2011), Measuring the business value of data quality, a Gartner Publication

Young & McConkey (2012), Data governance and data quality: Is it on your agenda? Journal of Institutional Research, Vol 17 no.1, p69-77.


Author Bios


Neil McCormick


Neil McCormick

Neil McCormick has a broad history of international management experience. He has held board and senior leadership positions in both domestic and international organizations for over 30 years.

For the past 23 of those, Neil has worked in the area of human resources and consulting services building a repertoire covering the spectrum of human resource management. Neil is the founder and principal shareholder of HRM Advisory Pty Ltd (HRMA). A recognized presenter and guest lecturer on the subject, Neil has a detailed understanding of, and passion for, the application of talent management principles and processes for the ongoing success of organizations.

His enterprise, HRMA assists clients better align Human Resources to deliver the specific goals of organizations. This work has resulted in the publication of “Lean but Agile” ~ “Rethink workforce planning to gain a true competitive edge.”

Neil is a published author, also an Editorial Advisor for The HR Examiner. He is a recognized global expert in areas covering workforce strategy, planning, and analytics. Neil is a Committee member of Standards Australia MB009 tasked to develop Standards for human resource management. Neil represents Australia, and is an active participant, on the ISO TC 260 Human Resource Management Committee. He is the convener of Working Group 2 Metrics for this committee and a contributing member of other Working Groups within ISO TC 260.

Read Neil’s Posts on HRExaminer

Neil’s Company HRM Advisory

Neil on LinkedIn

Dr. Chris Andrews


Dr. Chris Andrews

Dr. Chris Andrews is the Director of Human Resources for an Australian private university, Bond University, located on the Gold Coast in eastern Australia. He completed his doctorate in human resource performance auditing in 2008. You can find Dr. Andrews on LinkedIn here and on twitter he can be found at @HR_Standards and also @HR_Auditing. A selection of his published works can be found online at: https://works.bepress.com/chris_andrews/.

Read Dr. Andrew’s posts on HRExaminer.com here.

Bond University is located at https://bond.edu.au/


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