Advanced Natural Language Processing for SAP Data Insights

Sridhar Jampani¹, Sunil Gudavalli², Vamsee krishna Ravi³, Prof. (Dr) MSR Prasad⁴, ​Dr. Sanjouli Kaushik⁵ & Prof. (Dr) Punit Goel⁶

¹Acharya Nagarjuna University, Guntur, Andhra Pradesh, India, jampani.sridhar@gmail.com

²Jawaharlal Nehru Technological University, Kukatpally, Hyderabad – 500 085, Telangana, India gudavallisunil4@gmail.com

³International Technological University, Santa Clara, CA, USA , ravivamsee8@gmail.com

⁴Koneru Lakshmaiah Education Foundation Vadeshawaram, A.P., India , email2msr@gmail.com

^5​Dr. Sanjouli Kaushik Research Supervisor , MAHGU, Uttarakhand , sanjolikaushik8@gmail.com

⁶Maharaja Agrasen Himalayan Garhwal University, Uttarakhand, drkumarpunitgoel@gmail.com

Abstract

The increasing volume and complexity of data generated by SAP systems present significant challenges in extracting actionable insights. Advanced Natural Language Processing (NLP) techniques offer powerful tools to bridge the gap between unstructured and structured data, unlocking new opportunities for data analysis and decision-making. This paper explores the integration of advanced NLP methodologies into SAP data systems to enhance insights derived from enterprise data. By leveraging techniques such as sentiment analysis, entity recognition, and topic modeling, organizations can gain a deeper understanding of both structured SAP data and unstructured content, such as reports, emails, and customer feedback. We discuss the potential of NLP in automating data categorization, improving data accessibility, and delivering real-time insights for business intelligence. Furthermore, the paper highlights practical applications in financial analysis, supply chain management, and customer experience optimization, demonstrating how NLP can drive operational efficiency and strategic decision-making in SAP-powered enterprises. The study also addresses the challenges of data privacy and system integration, proposing solutions for seamless NLP adoption in complex SAP environments.

Keywords

Advanced Natural Language Processing, SAP data, enterprise data insights, sentiment analysis, entity recognition, topic modeling, unstructured data, business intelligence, financial analysis, supply chain management, customer experience, data automation, system integration, real-time insights, data privacy.

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