Agentic AI for Deep Data Analysis [White Paper]

In today's data-intensive landscape, organizations face the challenge of extracting timely and actionable insights from increasingly complex datasets. Traditional data analysis methods are often manual, time-consuming, and struggle to scale effectively, limiting the ability to uncover the deep patterns necessary for strategic decision-making.

This white paper presents Agentic AI for Deep Data Analysis, a transformative approach that leverages autonomous AI agents to perform sophisticated, self-directed data exploration. The core of the framework lies in its ability to interpret natural language queries (NLQ) to retrieve relevant data and utilize multiple Large Language Model (LLM) agents for in-depth analysis and the generation of practical action recommendations. A critical component is Expert Alignment, which grounds the AI's processes in industry-specific knowledge and best practices, ensuring relevance, accuracy, and compliance.

The framework operates in two primary stages:

Data Analysis, which focuses on extracting insights from structured and external web data.
Action Recommendation, which generates feasible strategies based on the analysis and expert guidelines.

It addresses key challenges in data identification, understanding data structure, incorporating industry-specific compliance, and integrating external factors for a more comprehensive view.

Example applications demonstrate the framework's versatility across industries, including identifying root causes of sales decline in retail, analyzing network usage and disruptions in telecom, and understanding factors driving customer churn in business.

Initial real-world application results in a retail scenario show promising performance. Through an iterative evaluation process involving human expert baselines, "LLM as a Judge" scoring, and human re-validation, the Agentic AI's capability improved significantly, demonstrating the potential to surpass human performance levels in structured analytical tasks.

Future work will focus on:

Building a robust Agent Framework with expert playbooks
Enabling seamless analysis across mixed data sources (private, public, code-based)
Developing a sophisticated grading framework with detailed metrics (Hallucination Rate, Scalability, Test Score, Overall Score),
Validating the system against global benchmarks like the DABStep and DA-bench leaderboards.

Agentic AI for Deep Data Analysis offers a path towards more efficient, scalable, and insightful data analysis, empowering organizations to make better, data-driven decisions autonomously.

‍Download PDF: Agentic AI for Deep Data Analysis [White Paper]

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