International Journal of Science, Engineering and TechnologyAuthors: Professor Yoshua Bengio, Professor Geoffrey Hinton, Professor Fei-Fei Li, Chaitanya Srinivas, Niharika
Abstract: The rapid growth of heterogeneous enterprise data, including text documents, scanned records, images, audio, video, and structured business information, has created significant challenges in data integration, information extraction, semantic understanding, and large-scale analytics. Traditional data engineering approaches often process these modalities independently, limiting their ability to capture cross-modal relationships and generate comprehensive business insights. This research proposes an Integrated Framework for Intelligent Multimodal Data Engineering and Enterprise Document and Media Analytics to facilitate the unified ingestion, preprocessing, transformation, enrichment, and analysis of heterogeneous enterprise information. The proposed framework integrates artificial intelligence, natural language processing, optical character recognition, computer vision, speech recognition, semantic representation, and machine learning techniques to extract meaningful information from diverse data sources. A modular architecture is designed to support multimodal data ingestion, metadata management, quality assessment, feature extraction, cross-modal alignment, knowledge representation, and intelligent information retrieval. Furthermore, the framework incorporates scalable data processing, interoperability, security, governance, and explainability mechanisms to improve the reliability and usability of analytical outcomes in enterprise environments. By connecting unstructured documents and multimedia content with structured organizational data, the framework aims to enhance information discovery, automate document-intensive workflows, reduce manual processing effort, and support evidence-based decision-making. The proposed approach provides a foundation for developing intelligent enterprise analytics systems capable of managing growing data volumes while maintaining consistency, traceability, and operational efficiency. The framework's effectiveness can be evaluated using document extraction accuracy, classification performance, retrieval relevance, cross-modal alignment quality, processing latency, scalability, and resource utilization. The research contributes an integrated architectural approach for transforming fragmented enterprise documents and media assets into actionable, context-aware information, supporting the evolution of data-driven and intelligent enterprise operations.
