What is People Analytics and how are data tools integrated into HR?

With the pace of change that companies have experienced in recent years, human resources (HR) management has become a crucial area, making it important to understand what Human Resources does and the roles that make up this department. Professionals working in this field have become strategic players whose main role today is to foster positive relationships with and among employees. In fact, this is essential for organizations to operate effectively despite their growing complexity. In this context, the use of People Analytics (PA) and Artificial Intelligence (AI) is revolutionizing workforce management processes.

What is People Analytics?

In a recent editorial, Emilio J. Castilla, Professor of Management at the MIT Sloan School of Management, defines People Analytics as a data-driven approach to improving people-related decisions, with the aim of promoting the success not only of the organization but also of its employees. According to Castilla, organizations that invest in creating and developing a strategic, people-centered and long-term data model are better prepared to address the current economic and social environment. 

There are three categories of People Analytics:

• Descriptive analysis (HR Intelligence). Data is used to analyze and describe what happened in the past in order to understand the origins of what is happening now.
• Diagnostic analysis (HR Data Analytics). This analysis aims to identify the reasons why specific events occur through relevant insights.
• Predictive analysis (HR Advanced Analytics). The data collected is processed to create predictive models capable of determining what could happen in the future and which factors may contribute to a particular event.

In this regard, Allied Market Research's HR Analytics Market report confirms that data-driven HR is one of the markets expected to experience rapid growth: according to the reported data, the global HR analytics industry is expected to reach $11 billion by 2031, growing by 16.6% between 2022 and 2031.

Data analytics in HR

The advantages of using People Analytics and Artificial Intelligence are numerous and can improve a wide range of HR processes. For example, analyzing employee data helps uncover trends and patterns in satisfaction, performance and productivity levels, enabling HR teams to develop data-driven solutions that improve the employee experience and reduce turnover rates.

Data models can improve recruitment processes and predict the likelihood of a candidate succeeding in a particular role. This reduces the time and effort spent on simple, repetitive tasks, while predictive analytics can provide insights into future trends and patterns, helping organizations address potential issues proactively.

These are some of the ways in which People Analytics and AI can revolutionize Human Resources:

• Innovation and Trends. By adopting these innovations, HR teams can keep up to date with trends and best practices, helping their companies maintain a competitive advantage.
• Workforce planning and talent management. Predictive analytics can help forecast recruitment needs, identify high-performing employees and streamline the onboarding of new hires.
• Performance management and employee engagement. These technologies help monitor certain employee metrics, such as performance and engagement.
• Talent acquisition. AI can automate certain aspects of the recruitment process, such as scheduling meetings, screening candidates and even providing post-interview feedback.
• Workforce analytics and organizational development. Areas requiring training or employee development can be identified, enabling HR teams to create personalized training plans that improve the overall skills of the workforce.

How to apply analytics in Human Resources

After understanding what People Analytics is and its potential, it is necessary to understand how to apply and implement it, as well as how to monitor it in order to improve and optimize processes.

These are some of the tasks required to successfully apply People Analytics in a Human Resources (HR) department, which can be learned in a Master in Human Resources: 

• Collect and analyze employee data regularly to identify emerging trends or issues. This data can be used to make informed decisions about human resources management.
• Improve data quality, for example by monitoring errors or discrepancies and correcting them quickly. This makes it possible to obtain more accurate and useful information for the organization.
• Data privacy: respect employee privacy and comply with GDPR regulations. This includes limiting access to data to those who need it to perform their work and protecting it against data breaches.
• Train employees and managers in the use of People Analytics and the insights that can be obtained from data. This will help them understand how this information can be used to make strategic decisions.
• Monitor results to ensure that they are aligned with the objectives. This makes it possible to implement any necessary changes or improvements to maximize the effectiveness of analytics in Human Resources.

Integrating a data model into HR

There are numerous real-world examples demonstrating the effectiveness of integrating a data model into HR. For example, information technology company Cisco has used predictive analytics to forecast absenteeism rates, identify factors contributing to employee dissatisfaction and develop programs to improve the situation. Through data analysis, Cisco was able to increase its retention rate by 3% and save $300 million in training costs.

Google has also implemented a People Analytics system called Project Oxygen. The project analyzed employee performance data, identifying the behaviors and skills considered most important by the company. These skills were then used to create a successful leadership model that helped identify and develop internal talent.

Hilton, meanwhile, uses a People Analytics system called OnQ, which analyzes employee data to identify workforce management issues and opportunities. For example, OnQ helped identify employees at high risk of leaving the company and enabled the organization to intervene before they resigned.

Finally, Procter & Gamble has implemented a People Analytics system called TalentWorks, which analyzes employee data to identify the characteristics shared by its highest-performing employees. This data is then used to develop predictive recruitment models and identify areas where employees may need additional support to improve their performance.