Robotic Process Automation (RPA) In Legacy System Migrations: Reducing Operational Inefficiencies In Digital Transformation

Abstract

Generative Artificial Intelligence (AI) is revolutionizing the utilization of large-scale data lakes, significantly enhancing business decision-making processes. As enterprises increasingly depend on vast volumes of data for strategic insights, the challenge remains in efficiently analyzing and extracting actionable intelligence from these expansive repositories. This review explores recent advancements in applying generative AI to manage and interpret data lakes, emphasizing their role in improving data quality, optimizing data governance, and enabling predictive and prescriptive analytics. By automating data preparation, validation, and synthesis, generative AI techniques facilitate the rapid transformation of raw data into valuable business insights. Furthermore, these AI-driven methodologies empower organizations to overcome traditional bottlenecks related to data complexity and heterogeneity, leading to more agile and accurate decision-making frameworks. Case studies across various sectors, including retail, finance, and healthcare, highlight how generative AI enhances forecasting accuracy, customer segmentation, personalized marketing, and risk assessment. Despite considerable advantages, the deployment of generative AI faces challenges such as ethical considerations, computational intensity, and the necessity of robust frameworks to maintain data privacy and compliance. Addressing these  barriers through effective model governance and interpretability is critical for successful adoption. Ultimately, this paper underscores generative AI's transformative potential in data lake environments, driving efficiency and innovation in business analytics