Informed HR decision making is a critical factor in optimizing operations and efficiently managing talent within an organization. However, a considerable gap in this process arises due to insufficient analyzed data and the absence of systematic processes. This gap can be attributed to several factors, including a lack of adequate analytical tools, resistance to change towards digitization of processes, and a deficit in analytical competencies within HR staff.
Poor data analysis leads to a partial and often subjective view of employee performance, training needs, and predicting labor market trends. Without data analytics, HR strategy can be based on outdated assumptions and practices that do not reflect current workforce realities and market dynamics. In addition, the lack of systematic processes means that important decisions are made without a consistent frame of reference, which can result in inconsistencies and talent management that does not make the most of individual capabilities or promote career development.
In the current context of human resource management, strategic and operational decisions play a crucial role in staff performance and satisfaction. The introduction of analytical tools to support these decisions is a step forward in the innovation of HR processes. This side project proposes the development of an application with hybrid architecture that integrates and analyzes relevant data to improve decision making with functionalities such as filters and filters with AI-generated data.
Next post I will share the progress to receive your technical and functional feedback. 🐙