The Impact of Predictive Analytics on Employee Performance Evaluation and Succession Planning in Modern Organizations

Abstract

In modern group management, using predictive analytics to evaluate employee performance and plan for leadership succession is changing how companies find talent, evaluate performance, and plan for future leadership needs. Predictive analytics uses data, statistical methods, and machine learning to find trends and guess what will happen in the future. It has become an important tool for improving how human resources (HR) managers make decisions. The main focus of this study is on how predictive analytics can improve accuracy, speed, and fairness in organizational practices by looking at how it affects employee performance review and succession planning. It's not always possible to get a full and fair picture of an employee's skills through the usual ways of evaluating their work, like yearly reviews and subjective tests. Predictive analytics is a more data-driven method that uses large amounts of data from many sources, such as past performance records, comments from peers and managers, and individual job paths. This helps companies learn more about how employees are doing and support them in making choices about raises, prizes, and training needs. Predictive analytics is also very important for succession planning because it finds organizations' future leaders. Models that use performance data, skill sets, behavioral patterns, and even outside factors like market trends and industry changes can predict how well an employee will do in the future and whether they are ready to take on leadership roles. In this way, companies can prepare for future leaders, fill skill gaps, and lower the risks that come with changing leaders. When prediction analytics are used, they also make decision-making more fair and clear. By getting rid of human biases and depending on data-driven insights, companies can make sure that succession planning and performance reviews are more fair. This lowers the chance of favoritism and makes sure that workers are evaluated based on what they actually do and how much they can contribute.