Smart Workforce: Enhancing Employee Productivity with Real-Time Data Analytics

It is very important to increase employee productivity to achieve success in a highly competitive corporate space. This paper works on a method for using real time data analytics basis to make the employees more productive. Anchor points of the methodology consist of the determination of strategic performance indicators and the collection from trustworthy data sources, automation of data collection, advanced data assignment and standardization. The paper also provides an elegant predictive analytics model with a Hybrid AutoEncoder-RNN Model (HARM). Combining the feature extraction capabilities of Autoencoders with the sequence prediction strengths of Recurrent Neural Networks (RNNs); Long Short-Term Memory (LSTM) networks, the HARM is able to deliver precise forecasts of future productivity trends. The suggested approach not only helps in real-time tracking and proactive decisions, but also streamlines workforce management leading to increased organizational productivity.