Framework for Quick Visuals Insight Discovery in the Data Workflows

Data-driven decision-making growth demands rapid frameworks to discover visual insights which bring value to data pipelines. Decision-making processes require time-sensitive visualization and interpretation because standard data analysis approaches handle limited amounts of data effectively. The survey investigates multiple frameworks and methodologies that improve visual analytics through interactive visualization methods and automated insights generation as well as large-scale data processing platforms. The research investigates modern visualization methods based on deep learning models as well as explainable AI (XAI) systems that produce insights and adaptive dashboards which are evaluated for their advantages and barriers. The survey analyses the problems that stem from handling large-scale data dimensions as well as maintaining interpretability and achieving optimal computation speeds. Future research focuses on generative AI as a tool to improve visualization methods and federated learning to maintain confidential analytics while edge computing provides immediate results. This paper gathers recent innovation progress to deliver researchers and practitioners complete understanding about developing visual insight discovery trends and innovations in data workflow applications.