Paper Title
CONVERSION OF UNSTRUCTURED DATA TO STRUCTURED BUSINESS DATABASES FOR IT COMPANIES

Abstract
In the digital age, organizations produce large amounts of unstructured data, including emails, documents, social media posts, customer feedback, and multimedia content. For IT companies, extracting useful insights from this unstructured data poses a significant challenge. Traditional databases mainly work with structured data that has clear formats. The rapid growth of Artificial Intelligence (AI), Natural Language Processing (NLP), and Data Mining techniques has opened new opportunities to turn unstructured data into structured business databases. This change allows for better decision-making and operational intelligence. This research looks into methods and frameworks for systematically transforming unstructured data into formats that can integrate into business databases. We explore techniques like text mining, entity recognition, sentiment analysis, and knowledge graph construction. We highlight how NLP helps in understanding the meaning behind text. We also examine AI-driven machine learning models for their ability to classify, cluster, and predict data categories. Data mining algorithms are reviewed for spotting patterns and hidden connections that lead to structured representations. The paper discusses architectural models, including ETL (Extract, Transform, Load) pipelines that use AI/NLP and link to cloud-based data warehouses. We provide a comparative study of open-source tools and commercial solutions to assess their performance, scalability, and accuracy. Additionally, the study highlights real-world applications in IT companies, such as customer relationship management, project documentation analysis, software quality assessment, and business intelligence reporting. By connecting unstructured data sources with structured database systems, IT organizations can strengthen data-driven strategies, improve customer experience, and streamline decision-making. This research shows that AI and NLP not only automate the conversion process but also add meaningful value to structured datasets, resulting in smarter and more responsive business systems. Keywords - Unstructured Data, Structured Databases, Natural Language Processing (NLP), Artificial Intelligence (AI), Data Mining, Large Language Models (LLMs), Business Intelligence (BI)