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For decades, Human Resources (HR) departments relied on intuition, experience and, at best, isolated indicators for making decisions about people. However, the dynamics of current business—marked by digitalization, the scarcity of specialized talent and the demand for measurable results—has focused on a new paradigm: People Analytics. This discipline combines statistical methods, data science and artificial intelligence to transform employee data into actionable knowledge. The purpose is not to “dehumanize” people management, quite the contrary: to offer fairer, more personalized and, of course, profitable work experiences.
In this article, we'll explore why People Analytics is key to modern organizations, the concrete benefits it provides, and the steps needed to successfully implement it. In the end, we'll tell you how you can train at MBIT School to become a cutting-edge professional.
Companies today generate more information about their employees than ever: hiring records, online learning platforms, climate surveys, performance systems, corporate chats... These “digital traces” allow us to objectively measure phenomena that were previously subjective. In addition, competitiveness in attracting and retaining talent forces HR to justify its decisions, just as Finance does with financial statements or Marketing with conversion KPIs.
Another decisive factor is technological maturity: the cloud has democratized storage and computing, and visualization tools allow any manager to understand a dashboard in seconds. Finally, the pandemic accelerated the adoption of hybrid and remote working models, adding complexity to management and increasing the need to rely on data to monitor well-being, productivity and collaboration.
People Analytics can be defined as the process of collecting, integrating and analyzing workforce data with the goal of improving both business performance and the employee experience. It's not just a matter of “having a lot of reports” or an Excel with eye-catching graphics. It involves connecting multiple sources of information —ATS, ERP, LMS, satisfaction surveys, emails, IoT sensors, etc.—through automated pipelines and applying descriptive, predictive and prescriptive analysis techniques.
The key is in the last mile: turning findings into action. A model that predicts the probability of turnover is useless if a retention plan is not deployed; a performance score loses meaning if it is not translated into a development program. People Analytics is, therefore, a combination of science, business and cultural change.
When HR presents robust metrics — turnover percentage, cost per hire, average time spent filling vacancies, commitment scores — every decision ceases to be a hunch and becomes a calculated investment. This strengthens the credibility of the people area before the management committee and makes it easier to allocate resources.
Through sentiment analysis, interaction heat maps and recommendation algorithms, it is possible to detect friction points in the working day and propose personalized interventions: from adjusting the workload to redesigning reskilling programs.
A predictive model capable of reducing voluntary turnover by 5% can generate millions of dollars in savings in hiring, training and loss of productivity. In addition, adjusting human resources to the commercial pipeline avoids overhiring that hampers profitability.
Data analysis makes it possible to identify biases in the selection, promotion or compensation processes. Correcting them not only improves the corporate image, but also expands the 'pool' of talent and promotes innovation.
Every project must start with a clear challenge: reducing sales turnover, accelerating the time to fill vacancies or designing a succession plan. Without a well-defined question, metrics turn into a sea of meaningless numbers.
The immutable rule of analysis is “garbage in, garbage out”. It is essential to ensure that the data is clean, standardized and, of course, complies with the protection regulations (GDPR in Europe). Data engineers and governance specialists participate in this phase.
Depending on the complexity of the problem, descriptive techniques (KPIs dashboards), predictive (abandonment models) or prescriptive (shift optimization) will be applied. Visualization must answer questions intuitively: a sales manager should understand graphics without the need for a master's degree in statistics.
Success is not measured by the beauty of the model, but by the change it generates. If, after identifying an exit risk factor, a mentoring program is implemented and the turnover is low, we can talk about victory. Continuous measurement — before and after action — is the closing of the cycle.
The use of sensitive data, such as medical records or chat conversations, requires extreme precautions: anonymization, minimization and obtaining express consent. In addition, the organization must be transparent about what is being measured and for what purpose.
Models learn from historical data that may reflect past discriminations. Auditing algorithms, incorporating correction variables and maintaining an ethics committee reduces the likelihood of perpetuating injustices.
The biggest obstacle is usually not technical, but human. Managers and middle managers must trust analytics and be willing to modify processes. Including them from the start and showing 'quick wins' makes it easy to change.
If you want to move from theory to practice and lead the data revolution in HR, we invite you to learn about the MBIT School's People Analytics Program. An executive format, with active teachers and a final project based on your own data, that will turn you into a professional capable of translating numbers into strategic decisions.
Sign up now and transform your career (and that of your company) with the power of data!
For decades, Human Resources (HR) departments relied on intuition, experience and, at best, isolated indicators for making decisions about people. However, the dynamics of current business—marked by digitalization, the scarcity of specialized talent and the demand for measurable results—has focused on a new paradigm: People Analytics. This discipline combines statistical methods, data science and artificial intelligence to transform employee data into actionable knowledge. The purpose is not to “dehumanize” people management, quite the contrary: to offer fairer, more personalized and, of course, profitable work experiences.
In this article, we'll explore why People Analytics is key to modern organizations, the concrete benefits it provides, and the steps needed to successfully implement it. In the end, we'll tell you how you can train at MBIT School to become a cutting-edge professional.
Companies today generate more information about their employees than ever: hiring records, online learning platforms, climate surveys, performance systems, corporate chats... These “digital traces” allow us to objectively measure phenomena that were previously subjective. In addition, competitiveness in attracting and retaining talent forces HR to justify its decisions, just as Finance does with financial statements or Marketing with conversion KPIs.
Another decisive factor is technological maturity: the cloud has democratized storage and computing, and visualization tools allow any manager to understand a dashboard in seconds. Finally, the pandemic accelerated the adoption of hybrid and remote working models, adding complexity to management and increasing the need to rely on data to monitor well-being, productivity and collaboration.
People Analytics can be defined as the process of collecting, integrating and analyzing workforce data with the goal of improving both business performance and the employee experience. It's not just a matter of “having a lot of reports” or an Excel with eye-catching graphics. It involves connecting multiple sources of information —ATS, ERP, LMS, satisfaction surveys, emails, IoT sensors, etc.—through automated pipelines and applying descriptive, predictive and prescriptive analysis techniques.
The key is in the last mile: turning findings into action. A model that predicts the probability of turnover is useless if a retention plan is not deployed; a performance score loses meaning if it is not translated into a development program. People Analytics is, therefore, a combination of science, business and cultural change.
When HR presents robust metrics — turnover percentage, cost per hire, average time spent filling vacancies, commitment scores — every decision ceases to be a hunch and becomes a calculated investment. This strengthens the credibility of the people area before the management committee and makes it easier to allocate resources.
Through sentiment analysis, interaction heat maps and recommendation algorithms, it is possible to detect friction points in the working day and propose personalized interventions: from adjusting the workload to redesigning reskilling programs.
A predictive model capable of reducing voluntary turnover by 5% can generate millions of dollars in savings in hiring, training and loss of productivity. In addition, adjusting human resources to the commercial pipeline avoids overhiring that hampers profitability.
Data analysis makes it possible to identify biases in the selection, promotion or compensation processes. Correcting them not only improves the corporate image, but also expands the 'pool' of talent and promotes innovation.
Every project must start with a clear challenge: reducing sales turnover, accelerating the time to fill vacancies or designing a succession plan. Without a well-defined question, metrics turn into a sea of meaningless numbers.
The immutable rule of analysis is “garbage in, garbage out”. It is essential to ensure that the data is clean, standardized and, of course, complies with the protection regulations (GDPR in Europe). Data engineers and governance specialists participate in this phase.
Depending on the complexity of the problem, descriptive techniques (KPIs dashboards), predictive (abandonment models) or prescriptive (shift optimization) will be applied. Visualization must answer questions intuitively: a sales manager should understand graphics without the need for a master's degree in statistics.
Success is not measured by the beauty of the model, but by the change it generates. If, after identifying an exit risk factor, a mentoring program is implemented and the turnover is low, we can talk about victory. Continuous measurement — before and after action — is the closing of the cycle.
The use of sensitive data, such as medical records or chat conversations, requires extreme precautions: anonymization, minimization and obtaining express consent. In addition, the organization must be transparent about what is being measured and for what purpose.
Models learn from historical data that may reflect past discriminations. Auditing algorithms, incorporating correction variables and maintaining an ethics committee reduces the likelihood of perpetuating injustices.
The biggest obstacle is usually not technical, but human. Managers and middle managers must trust analytics and be willing to modify processes. Including them from the start and showing 'quick wins' makes it easy to change.
If you want to move from theory to practice and lead the data revolution in HR, we invite you to learn about the MBIT School's People Analytics Program. An executive format, with active teachers and a final project based on your own data, that will turn you into a professional capable of translating numbers into strategic decisions.
Sign up now and transform your career (and that of your company) with the power of data!
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