January 30, 2026
The role of People Analytics in the strategic evolution of People Management
Ana Caroline Marçal Rezende; Simone Kunde
Summary prepared by the ResumeAI tool, an artificial intelligence solution developed by the Pecege Institute focused on synthesis and writing.
This research analyzed how the use of People Analytics contributes to the evolution of the Human Resources role from an operational function to a strategic one, identifying the main benefits, barriers, and critical success factors from the perception of market professionals. The investigation sought to understand how data analysis reconfigures HR practices, shifting the focus from administrative activities to a measurable contribution to business results. The central premise is that, by replacing intuition-based decisions with evidence-based ones, HR optimizes its processes and legitimizes itself as a strategic partner, influencing high-impact decisions on talent, productivity, and competitiveness.
Historically, the Human Resources area was perceived as a cost center with reactive performance focused on bureaucratic tasks, which limited its capacity to influence corporate strategy (Becker et al., 2001). The growing complexity of the business environment, however, imposed on organizations the need to manage their human capital more proactively. In this scenario, the demand emerged for an HR that would become a strategic partner, capable of aligning people management practices with the company’s long-term objectives, as advocated by theorists such as Ulrich (1997).
The transition to a strategic role was hindered by the difficulty of measuring the impact of HR initiatives on organizational results. The absence of robust metrics weakened HR’s ability to prove its value, perpetuating a cycle of decisions based on subjective perceptions (Marler and Boudreau, 2016). People Analytics emerges as a transformative approach in this context. Defined as the systematic application of statistical and technological methods to analyze people data, it offers HR the tools to generate actionable insights, predict trends, and base its recommendations on concrete evidence, increasing its credibility with senior management (Marr, 2018).
The implementation of People Analytics is a journey of analytical maturity that involves cultural, technical, and strategic dimensions. The evolution occurs in progressive stages: descriptive analytics, which monitor operational indicators such as absenteeism; diagnostic analytics, which identify causes and correlations; predictive analytics, capable of anticipating scenarios such as the risk of talent turnover; and prescriptive analytics, which recommend actions to optimize results (Davenport et al., 2010). Achieving the more sophisticated stages depends on an adequate technological infrastructure, an organizational culture that values data-driven decision-making, and clear alignment with the business’s strategic priorities (Van den Heuvel and Bondarouk, 2017).
Despite its potential, the consolidation of People Analytics faces considerable challenges. The cultural resistance of managers accustomed to relying on intuition, the low quality or fragmentation of data, and the scarcity of HR professionals with analytical skills are frequently cited barriers (Angrave et al., 2016). Overcoming these obstacles requires an effort that goes beyond software acquisition, demanding the development of an analytical mindset, sponsorship from senior leadership, and continuous training of HR teams. This study aimed to investigate how these elements manifest in practice, exploring professionals’ perceptions and mapping the current state of People Analytics in the Brazilian organizational context.
To meet the objective, the study was characterized as an applied research of a descriptive nature, with a survey design, a suitable method for collecting information from a specific group, capturing their attitudes and practices (Gil, 2019; Yin, 2015). The descriptive approach was used to map the practices, perceptions, and challenges related to the use of data in people management in different organizations, providing a detailed overview of the investigated reality.
Data collection was carried out using a structured questionnaire on Google Forms. The instrument, based on literature on People Analytics and strategic HR management, was organized into thematic blocks: respondent and organization profile; data usage practices; impacts on decision-making and HR positioning; and barriers and critical success factors. An open-ended question allowed for the collection of qualitative perceptions. Dissemination occurred online, on professional networks such as LinkedIn and specialized communities, for 37 days.
The sample was non-probabilistic by convenience, composed of 127 HR, People Analytics, or people management professionals. Although it does not allow for statistical generalization, the sampling was adequate for the descriptive objectives of the study, aiming to identify emerging trends and patterns (Lakatos and Marconi, 2017). All participants agreed to a Free and Informed Consent Term (TCLE), which guaranteed the anonymity and confidentiality of the responses for exclusive use for academic purposes.
The data analysis used a mixed approach. The quantitative analysis involved descriptive statistics to generate frequency tables, graphs, and variable cross-tabulations. This step mapped the general trends of the sample. Complementarily, the qualitative analysis of the open-ended responses was carried out through content analysis, following Bardin’s (2011) steps: pre-analysis, material exploration with categorization, and results treatment. This approach allowed for a deeper interpretation of the data, capturing the nuances of the professionals’ experiences and broadening the validity of the findings (Creswell, 2014).
The analysis of the adoption stage showed that 48% of respondents consider that their companies have structured People Analytics practices, while 30.7% reported an initial application. A portion of 15% stated they are limited to operational indicators, and 6.3% admitted not using data analysis. This distribution reflects the different maturity stages described by Davenport et al. (2010). The qualitative responses illustrate this disparity, with reports of predictive model usage in large companies and difficulties with spreadsheets in small ones. Although 63% cite the use of Business Intelligence software, 86.6% still rely on spreadsheets, evidencing that technology does not guarantee, by itself, methodological maturity, a challenge pointed out by Levenson (2015).
Despite the heterogeneity, the strategic impact of data usage is widely perceived. A total of 74% of respondents stated that HR data is used “always” or “frequently” to support strategic decisions. This perception is stronger among leaders/managers (82%) and People Analytics specialists (77%). The impact on HR’s perception is also notable: 75.4% of professionals in companies with structured use of People Analytics stated that the area has come to be seen as more strategic, a percentage that drops to 38% among those who only use basic indicators. This data reinforces that the ability to present concrete evidence catalyzes the appreciation of HR, aligning with the view that its influence depends on measurable contribution to the business (Ulrich et al., 2009).
The benefits of People Analytics were clearly identified. “Support for leadership in decision-making” was the most cited (81.1%), followed by “improvement in hiring assertiveness” (45.7%), “increased productivity” (29.9%), and “reduced turnover” (26%). These results are in line with the literature, which highlights the ability of talent analytics to optimize resources and mitigate risks (Levenson, 2011). Qualitative reports complement this, stating that “HR gained a voice in strategic discussions by presenting evidence instead of opinions.” The most impacted areas were Recruitment and Selection (79.5%), Performance Evaluation (67.7%), and Compensation (62.2%).
The implementation of People Analytics, however, is marked by cultural and organizational barriers rather than technological ones. The “absence of a data-driven culture” was the main obstacle for 68.5% of participants, followed by the “lack of skilled professionals” (56.7%) and “lack of integration between systems” (51.2%). Other challenges include low data quality (44.9%) and budget constraints (43.3%). These findings echo the warnings of Angrave et al. (2016), who argue that the success of the practice is compromised if there is no analytical mindset and leadership support. Qualitative testimonials reinforce the point: “We have the tools, but we don’t have people who know how to interpret the data.”
Cross-analysis reveals that the perception of barriers varies with context. Large companies reported more problems with system integration and data quality. Small and medium-sized enterprises emphasized budget constraints. Sectors such as industry and healthcare pointed to greater cultural resistance, while the technology sector, although more advanced, highlighted the difficulty in finding qualified professionals. This differentiation shows that there is no single solution, making it necessary to adapt strategies to each organizational reality.
Faced with these challenges, the critical success factors identified reinforce the centrality of the human factor and leadership. An “engaged leader with data usage” was considered the most important factor by 79.5% of participants. This was followed by “data quality and reliability” (69.3%) and the construction of a “data-driven organizational culture” (63.8%). This triad — leadership, data, and culture — is consistent with the models of Davenport et al. (2010), which emphasize executive sponsorship, and Minbaeva (2018), which highlights methodological credibility as a condition for insights to be used. The perception is clear: technology is a tool, but transformation depends on leadership commitment and people’s empowerment.
The results demonstrate that People Analytics is acting as a transformative agent for HR, although its consolidation is a gradual and uneven process. The benefits in terms of decision support, process optimization, and strategic legitimization are evident. However, the full realization of this potential is conditioned by overcoming structural and, mainly, cultural barriers. The transition from an operational HR to a data-driven strategic partner is not just a matter of implementing technologies, but of fostering a new mindset, empowering teams, and ensuring the engagement of senior leadership.
The analysis of the collected data allows us to conclude that People Analytics represents a fundamental path for the evolution of HR from an administrative function to a strategic business partner. The results showed that, in organizations where the practice is more consolidated, HR gains greater credibility, participates more actively in strategic decisions, and demonstrates its value objectively. Benefits, such as improved decision-making processes and greater assertiveness in recruitment, directly impact competitiveness. However, the study made it clear that technology alone is insufficient. Success depends on an ecosystem that includes engaged leadership, a data-driven culture, and professionals with analytical skills. The identified barriers, especially the absence of a data-driven culture and lack of training, reinforce that the transformation is human and organizational, requiring continuous investment in development and change management. The limitations of this study, such as the non-probabilistic sample, suggest caution in generalizing the findings, but the identified patterns offer valuable insights for managers. It is concluded that the objective was achieved: it has been demonstrated that the use of People Analytics contributes to the evolution of HR’s role from an operational function to a strategic role, conditioned by overcoming cultural, technological, and leadership barriers.
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Executive summary from the Final Project of the Specialization in People Management from the MBA USP/Esalq
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