From Raw Data to Actionable Insights: Leveraging LLMs for Automation

Abstract

This paper explores the transformative role of Large Language Models (LLMs) in automating the data processing lifecycle, from ingestion to insights generation. LLMs streamline data handling by automating ingestion, transformation, and modeling processes, offering efficient, reliable, and timely insights critical for sectors such as healthcare, finance, and telecommunications. This study details the technical architecture of LLM-driven data workflows, addresses challenges in integrating diverse data sources, and emphasizes the necessity of governance frameworks to mitigate ethical concerns about data privacy and bias.

However, the integration of LLMs also presents specific challenges, such as handling unstructured data, ensuring data quality, and managing computational costs. Through case studies across multiple industries, this study illustrates the benefits and limitations of LLMs, highlighting both technical and ethical considerations for deploying these tools at scale. Case studies include a healthcare provider improving patient diagnosis accuracy, a financial institution enhancing fraud detection, and a telecommunications company optimizing network performance. Each case study employed a methodology involving data preprocessing, LLM training, and evaluation metrics to measure performance improvements. The quantitative results show the significant impact of LLMs on the data workflow.