People Analytics Framework: Application of Descriptive, Diagnostic, Predictive and Prescriptive Maturity Levels in People Management
People Analytics, Analytics Maturity Levels, People Management, Business Intelligence, Prescriptive Analytics
This dissertation presents the development of a framework for applying the levels of analytical maturity (Descriptive, Diagnostic, Predictive,
and Prescriptive) in people management, highlighting the growing relevance of a data-driven approach in Human Resources. The work is based on the premise
that the adoption of data analysis techniques, combined with predictive and prescriptive models and Business Intelligence tools, can significantly support
decision-making processes in the attraction, succession, and retention of talent within corporate environments.
In addition to discussing the potential benefits, the dissertation provides case studies that illustrate the practical applicability of the framework
across different organizational contexts. These case studies demonstrate how all levels of analytical maturity have the capacity to generate value for
organizations in workforce-related processes, bringing objectivity to decisions that are often subjective in the realm of people management.
The proposed framework not only optimizes processes but also promotes more effective human resource management by aligning people management practices with
the strategic objectives of organizations.