Advancing Enterprise Intelligence: Integrating AI-Driven Predictive Analytics within SAP HANA for Real-Time Business Decision Optimization

Authors

  • Surya Narayana Chakka Independent Researcher, USA
  • Shib Kumar Kanungo Independent Researcher, USA
  • Venu Gopal Avula Independent Researcher, USA

Keywords:

artificial intelligence-enabled analytics, SAP HANA, predictive analytics, , real-time decision-making, business intelligence, machine learning, enterprise optimization, data-based decision

Abstract

This paper explores the integration of AI-driven predictive analytics into SAP HANA, examining how it can transform real-time decision-making. As organizations face increasing amounts of data and a need for faster decision-making, leveraging artificial intelligence (AI) to predict trends, behaviors, and outcomes has become essential. The powerful in-memory computing platform of SAP HANA presents the perfect premise to integrate AI tools. It provides a business with the possibility to process massive amounts of data in real time. The paper discusses how the integration enhances decision processes, leading to efficient operations, resource utilization, customer satisfaction, and an increase in income. Under a well-documented research methodology encompassing case studies as well as sourcing of evidence in the form of data provided by the vanguard enterprises, the paper establishes that the introduction of a combination of AI with SAP HANA can be one of the most effective solutions enabling the generation of actionable insights even in a dynamic business environment. In this study, the need to adopt AI technologies to innovate enterprise intelligence and improve decision-making in general is highly emphasized.

Published

28-03-2023

How to Cite

Surya Narayana Chakka, Shib Kumar Kanungo, & Venu Gopal Avula. (2023). Advancing Enterprise Intelligence: Integrating AI-Driven Predictive Analytics within SAP HANA for Real-Time Business Decision Optimization. Well Testing Journal, 32(1), 53–67. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/197

Issue

Section

Original Research Articles