Data Engineering Challenges in AI-Driven Predictive Analytics Systems: Overcoming Scalability, Data Quality, and Real-Time Processing Hurdles

Abstract

The integration of artificial intelligence (AI) into predictive analytics systems has revolutionized decision-making across industries, enhancing forecasting accuracy and operational efficiency. However, the successful deployment of AI-driven predictive models relies heavily on the underlying data engineering infrastructure. This paper explores the major challenges in data engineering for AI-driven predictive analytics, focusing on scalability, data quality, and real-time processing. Through a detailed analysis of these hurdles, the paper presents strategies to overcome them, emphasizing scalable architectures, automated data quality mechanisms, and innovations in real-time data processing.