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HR and analytics: why HR is set to fail the big data challenge
Open accessAbstract
The HR world is abuzz with talk of big data and the transformative potential of HR analytics. This article takes issue with optimistic accounts, which hail HR analytics as a ‘must have’ capability that will ensure HR's future as a strategic management function while transforming organisational performance for the better. It argues that unless the HR profession wises up to both the potential and drawbacks of this emerging field and engages operationally and strategically to develop better methods and approaches, it is unlikely that existing practices of HR analytics will deliver transformational change. Indeed, it is possible that current trends will seal the exclusion of HR from strategic, board‐level influence while doing little to benefit organisations and actively damaging the interests of employees.
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“AI-driven algorithmic monitoring thus allows to collect and record a vast array of new information and metrics, such as emotions (Chan, 2018;Pinheiro, Ramos, Donizete, Picanço, & De Oliveira, 2017), movements, sleep time, physical and health condition, social media activity, internet browser history (Angrave, Charlwood, Kirkpatrick, Lawrence, & Stuart, 2016;Leicht-Deobald et al., 2019)”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Angrave et al., 2016
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In the citing paper
“Descriptive algorithms are likely to reinvent the world of performance management (PM) in multiplying the metrics available to precisely quantify various facets of employees' actions, emotions, performance, behaviors, attitudes and physical state (Angrave et al., 2016;Tambe et al., 2019), but also customer-related information such as satisfaction and traffic (Evans & Kitchin, 2018;Levy & Barocas,”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Angrave et al., 2016
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“Moreover, predictive artificial intelligence algorithms, like the famous IBM Watson, are also used to forecast the future performance of employees based on multiple data sources such as past performance, existing and potential projects, and recent and future training opportunities (Angrave et al., 2016;Edwards & Edwards, 2019;Greenfield, 2018;Kirimi & Moturi, 2016;Sajjadiani, Sojourner, Kammeyer-M”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Angrave et al., 2016
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“This forecasted performance is often used as the basis to set future goals or expectations (Angrave et al., 2016;Edwards & Edwards, 2019), to estimate the training needs (Horesh, Varshney, & Yi, 2016), to determine pay raise (Greenfield, 2018) or to promote employees to managerial positions (Mallafi & Widyantoro, 2016).”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Angrave et al., 2016
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BackgroundComment tirer le meilleur parti des données ?
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“However, this approach requires greater short-term investment and a slower implementation process (Angrave et al., 2016).”
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“In addition, HR professionals lack the skills, abilities, and competencies (Angrave et al., 2016; McCartney et al., 2021) needed to better implement and use analytics in HR (Mondal et al., 2022).”
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“It poses a challenge as numerous relevant data sets for HR big data are not integrated within HR systems but are rather scattered across different areas of the organization (Angrave et al., 2016; Marler & Boudreau, 2017).”
BackgroundIntelligent HR Systems in Global Organizations: Embracing Industry 4.0 for a Smart Future
Pandey, P., Pandey, A., Srivastava, A., Naqvi, S., Bisht, S., & Mahajan, N. (2024) · International Conference on Signal Processing and Advance Research in Computing (SPARC) · doi:10.1109/sparc61891.2024.10829147 · Cited by 1
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“Still, problems like a skill gap, an absence of well-established apps, or integration capabilities have been noticed (Angrave, D, Charlwood 2016).”
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Managing academic performance by optimal resource allocation
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“First, how often do we need to measure performance and adjust the developed model? To answer this question, it would be reasonable to refer to the planning horizon, the expected time period for obtaining the results, as well as the degree of digitalization within the organization (Angrave et al., 2016; Levenson & Fink…”
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“’ Many organisations are facing the double challenge of integrating the right technology and having the right people in place to use it effectively and ethically (Angrave et al., 2016; Chan et al. 2017).”
BackgroundSmith, P. E., Yellowley, W., & McLachlan, C. (2020) · Organizational Behaviour · doi:10.4324/9780429279560 · Cited by 1
Análise bibliométrica da produção científica internacional sobre people analytics
da Silva, A. L. I. F., Oliva, E. D. C., & Kubo, E. K. D. M. (2019) · Future Studies Research Journal Trends and Strategies · doi:10.24023/futurejournal/2175-5825/2019.v11i3.467 · Cited by 5
Tres claves para afrontar la transformación digital: organización, desempeño y gestión de datos
Villaplana, F., & Stein, G. (2019) · doi:10.15581/018.op-326 · Cited by 1
Štilić, A. (2019) · Knowledge International Journal · doi:10.35120/kij320151s · Cited by 1
“Nasuprot tome, (Angrave et al. 2016) dovodi u pitanje HR analitiku kao „must have“ sredstvo koje će osigurati budućnost ljudskih resursa unutar strateškog menadžmenta i transformaciju organizacionog učinka na bolje, tvrdeći da ukoliko se stručnjaci za ljudske resurse ne pozabave razvojem…”
BackgroundAnalytique et big data en ressources humaines
Coron, C. (2019) · Revue française de gestion · doi:10.3166/rfg.2019.00319 · Cited by 7
Bhokare, A. S., Hakweenda, H., Kulkarni, D. P. M., Thanekar, S. S., & Dayma, H. S. (2026) · SHS Web of Conferences · doi:10.1051/shsconf/202623005001 · Cited by 1
Reconciling the Debate on People Analytics in Academia and Practice
Hüllmann, J. (2022) · Bled eConference · doi:10.18690/um.fov.4.2022.34 · Cited by 1
“…planning (Angrave et al., 2016; Marler & Boudreau, 2017; McIver et al., 201 8); (Bachman et al., 2015; Fineman, 2016) Digital transformation (Angrave et al., 2016; Simón & Ferreiro, 2018 (Levenson, 2018) ; (Guenole & Feinzig, 2016) Video/Audio (Hüllmann et al., 2021) ; (Collins et al.,…”
InfluentialHuman Resource Analytics as a Life-Jacket for Organizations Amid Crisis
Ertürk, A., & Alkhayyat, R. (2021) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-7998-7210-8.ch003
Maximizing Human Resource Development in Sub-Saharan Africa in the Digital Era
Abugre, J. (2021) · Advances in Theory and Practice of Emerging Markets · doi:10.1007/978-3-030-70538-1_3 · Cited by 1
Exploring Recruitment and Selection Practices of Ghanaian Organisations
Ansoglenang, G. (2020) · European Journal of Business and Management · doi:10.7176/ejbm/12-12-02 · Cited by 1
“Arguing through the lens of contingency theory, the researcher found HRM Vol.12, No.12, 2020 policies and strategies in organisations to be contingent on both internal and external contextual factors (Posthumus et al., 2018; Angrave et al., 2016).”
Informalisation in Work and Employment: A Permissive Visibility or Another (Hidden) Inequality?
Clark, I. (2019) · Inequality and Organizational Practice · doi:10.1007/978-3-030-11647-7_9 · Cited by 1
Orwat, C., & Schankin, A. (2018) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ksp/1000086677 · Cited by 1
“…selecting applicants, incentivise employee performance, or promoting or binding employees (Rosenblat, Kneese and boyd 2014; Burdon and Harpur 2014; Marler and Boudreau 2017; 5 Chamorro-Premuzic et al. 2016; Chamorro-Premuzic et al. 2017; Dzida 2017; Weichert 2018, pp. 59-61; Angrave et al. 2016).”
BackgroundSantiago-Torner, C., & Henao, A. P. M. (2026) · Administrative Sciences · doi:10.3390/admsci16110539
Mansi Arora Kapoor, D. S. V. (2026) · International Journal of Research & Technology · doi:10.64882/ijrt.v14.i4.2041
Abylaikhanova, T., Domalatov, Y., Rakhmetulina, Z. B., Kenzhin, Z. B., Rakhmetulina, A., Bukhatova, A., & Narimbetova, A. (2026) · Problems and Perspectives in Management · doi:10.21511/ppm.24(4).2026.16
Parashar, D. R. B. A. M. S. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-40377
Omukpai, C. (2026) · Iconic research and engineering journals · doi:10.64388/irev10i4-1724242
“…analytics, also referred to as HR analytics or workforce analytics, involves the systematic application of analytical methods to human capital data to improve talent-related decision-making (Marler & Boudreau, 2017; Tursunbayeva et al., 2018; Angrave et al., 2016; van den Heuvel & Bondarouk, 2017).”
Revillod, G. (2026) · Yearbook of Swiss Administrative Sciences · doi:10.5334/ssas.244
Cultural Fit Analytics for Sustainable Risk Management in Multicultural IT Teams
Mazur, N. Y., Chukhray, N., & Chukhrai, A. (2026) · Sustainability · doi:10.3390/su182110719
Effectiveness of human resource management in IT enterprises: the HREII-IT evaluation methodology
Prints, I. (2026) · Management and Entrepreneurship Trends of Development · doi:10.26661/2522-1566/2026-3/37-28
Vardanyan, L. (2026) · Journal of Business Innovation and Accounting Research · doi:10.56442/szzkpa24
“Because OFP is measured through managerial perceptions at one point in time, stronger performance may also enable organizations to invest in HRA capabilities, or both variables may reflect unmeasured managerial competence (Marler & Boudreau, 2017; Angrave et al., 2016).”
InfluentialXin, R., Dong, X., & Chen, J. (2026) · International Journal of Responsible Artificial Intelligence Research · doi:10.67298/paper/870017
Khader, A. G. P. A., & Sreejith, S. S. (2026) · IIM Kozhikode Society & Management Review · doi:10.1177/22779752261485386
Jahan, U., Bhaskar, P., Tiwari, C. K., & Bhat, M. A. (2026) · Human Systems Management · doi:10.1177/01672533261484231
Ratkai, M., & Zimmermann, L. (2026) · Administrative Sciences · doi:10.3390/admsci16090426
“Without these conditions, organisations may collect more data without improving strategic decision-making or workforce outcomes (Angrave et al., 2016; Gupta et al., 2023).”
Ritish, P. V., Johar, A., & A.Srikanth (2026) · American Journal of AI Cyber Computing Management · doi:10.64751/ajaccm.2026.v6.n3.865
“Such approaches often fail to capture complex relationships among employee characteristics, work behavior, training effectiveness, and productivity, limiting the organization's ability to make informed HR decisions [18], [19].”
Onward and upward: strategies for overcoming challenges in algorithmic HR implementation
Qamar, Y., Ture, R. S., & Samad, T. A. (2026) · Global Knowledge, Memory and Communication · doi:10.1108/gkmc-06-2025-0482
Luzi, E., Ndou, V., Ndrecaj, V., & Teta, J. (2026) · Administrative Sciences · doi:10.3390/admsci16080387
Al-Ismaili, N. S. (2026) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2026.v08i04.85228
Budak, M. C., & Soyer, A. (2026) · Quality & Quantity · doi:10.1007/s11135-026-03005-4
Metadata
- Authors
- Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M.
- Affiliations
- University of Sheffield (GB)
- Loughborough University (GB)
- University of Leeds (GB)
- Independent Strategic HR Analytics Consultant
- Corresponding author
- Charlwood, Andy
- Venue
- Human Resource Management Journal
- Publisher
- Wiley
- Published
- 2016-01
- Volume
- 26
- Issue
- 1
- Pages
- 1-11
- ISSN
- 0954-5395 (ISSN-L)
- 1748-8583
- OpenAlex
- W2222612072
- Cited by
- 633
- References
- 0
- Citations by year
- 2026: 71 · 2025: 74 · 2024: 77 · 2023: 87 · 2022: 77 · 2021: 79 · 2020: 61 · 2019: 48 · 2018: 24 · 2017: 19 · 2016: 14
- FWCI
- 68.00
- Type
- journal article
- Language
- en
- Open access
- green
- dspace.lboro.ac.uk
- Licence
- Locations
- Human Resource Management Journal (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Loughborough University Institutional Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
- Loughborough University Research Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Human Resource and Talent Management
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- HR analytics, big data, human resource management, strategic management, organizational performance, transformative change, employee interests
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