Vol. 4 No. 1 (2024): Hong Kong Journal of AI and Medicine
Articles

Harnessing Generative AI for Automated Data Analytics in Business Intelligence and Decision-Making

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  • Prabu Ravichandran,
  • Jeshwanth Reddy Machireddy,
  • Sareen Kumar Rachakatla
Prabu Ravichandran
Sr. Data Architect, Amazon Web services, Inc., Raleigh, USA
Jeshwanth Reddy Machireddy
Sr. Software Developer, Kforce INC, Wisconsin, USA
Sareen Kumar Rachakatla
Lead Developer, Intercontinental Exchange Holdings, Inc., Atlanta, USA
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Published 21-03-2024

Keywords

  • Generative AI,
  • business intelligence,
  • synthetic data,
  • predictive modeling,
  • Generative Adversarial Networks,
  • Variational Autoencoders,
  • data augmentation,
  • decision-making,
  • market dynamics,
  • strategic insights
    • ...More
    Less
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
P. Ravichandran, J. Reddy Machireddy, and S. Kumar Rachakatla, “Harnessing Generative AI for Automated Data Analytics in Business Intelligence and Decision-Making”, Hong Kong J. of AI and Med., vol. 4, no. 1, pp. 122–145, Mar. 2024, Accessed: Sep. 18, 2024. [Online]. Available: https://hongkongscipub.com/index.php/hkjaim/article/view/25

Abstract

In the realm of business intelligence (BI), the capacity to leverage data for strategic decision-making has increasingly become a competitive differentiator. As organizations are inundated with vast quantities of data, traditional analytics methodologies often fall short in addressing the dynamic and complex nature of contemporary market conditions. This paper explores the transformative potential of Generative Artificial Intelligence (AI) in automating data analytics processes to enhance business intelligence and decision-making frameworks. Generative AI, with its ability to model and synthesize high-dimensional data, represents a paradigm shift in how businesses can harness data for actionable insights.

At the core of this investigation is the application of generative models to the generation of synthetic data. Traditional data analytics often relies on historical datasets, which may be incomplete or biased, limiting the scope and accuracy of insights derived. Generative AI, by contrast, can create synthetic datasets that augment existing data or fill in gaps, thereby enabling more robust predictive modeling and scenario analysis. This synthetic data generation not only facilitates a more comprehensive understanding of potential outcomes but also supports the simulation of diverse market conditions, offering valuable foresight into strategic decisions.

The paper delves into various generative models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), and their respective roles in automating data analytics. GANs, with their adversarial framework, are particularly adept at producing high-fidelity synthetic data that mirrors real-world distributions. VAEs, on the other hand, offer a probabilistic approach to data generation, enabling nuanced insights through the exploration of latent variables. The interplay between these models and traditional analytics tools is examined, highlighting how generative AI can enhance predictive accuracy and uncover patterns that may not be immediately evident from conventional data analysis methods.

Predictive modeling, a cornerstone of business intelligence, benefits significantly from the integration of generative AI. By leveraging synthetic data, organizations can train models on diverse datasets, improving their generalization capabilities and resilience to overfitting. The paper explores how generative models can augment predictive analytics by simulating various scenarios and forecasting outcomes with greater precision. This capability is particularly pertinent in volatile markets where traditional predictive models may struggle to account for rapid changes and emerging trends.

The discussion extends to the strategic implications of adopting generative AI for business decision-making. The ability to generate synthetic data and conduct robust predictive modeling facilitates a deeper understanding of market dynamics and consumer behavior. This, in turn, supports more informed decision-making processes, enabling organizations to adapt swiftly to changing conditions and capitalize on emerging opportunities. The paper highlights case studies where generative AI has been successfully implemented, demonstrating its impact on improving strategic decision-making and operational efficiency.

Furthermore, the paper addresses the challenges and limitations associated with integrating generative AI into existing BI frameworks. These include considerations related to data quality, model interpretability, and the ethical implications of synthetic data use. It also examines the computational resources required for deploying generative models at scale and the implications for organizational infrastructure.

The research underscores the transformative potential of generative AI in automating data analytics within business intelligence. By generating synthetic data and enhancing predictive modeling, generative AI offers a powerful tool for uncovering insights and supporting strategic decision-making. The paper calls for further exploration into the integration of generative AI with traditional analytics practices and advocates for continued research into overcoming the associated challenges to fully realize its potential.

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