CONVERSATIONAL AI FOR NATURAL LANGUAGE DATA ANALYTICS

Authors

  • Venkata Narasimha Sagar Gandha Missouri University of Science and Technology, USA Author

Keywords:

Conversational AI, Natural Language Processing, Data Democratization, Enterprise Analytics, Machine Learning

Abstract

Conversational AI is transforming how organizations interact with their data through natural language interfaces. As businesses face exponential growth in data volume and complexity, traditional analysis methods create bottlenecks due to technical expertise requirements. This technology democratizes data access by enabling non-technical users to query and analyze data using everyday language. The integration of advanced natural language processing and machine learning capabilities allows for real-time insights, improved decision-making, and enhanced cross-functional collaboration. Through sophisticated query processing and context management, these systems handle complex analytical requests while maintaining high accuracy levels. The impact extends across various business functions, from sales and marketing to operations and finance, enabling rapid data-driven decisions and improved operational efficiency. While the technology presents challenges in security, privacy, and implementation, its ability to bridge the gap between technical and business teams makes it invaluable. The system's capacity to understand domain-specific terminology, maintain conversation context, and generate appropriate visualizations has revolutionized how organizations leverage their data assets. By removing technical barriers and enabling natural language interactions, conversational AI fosters a more inclusive, data-oriented culture while simultaneously improving organizational agility and competitive advantage.

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Published

2025-02-05

How to Cite

Venkata Narasimha Sagar Gandha. (2025). CONVERSATIONAL AI FOR NATURAL LANGUAGE DATA ANALYTICS. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 8(1), 1538-1550. https://ijrcait.com/index.php/home/article/view/IJRCAIT_08_01_113