AI-Powered Data Analytics

Description

Background / Overview

Data analytics has long been central to business intelligence and decision-making, but traditional analytics approaches often require technical expertise, significant time, and predefined models. Generative AI (GenAI) is transforming this landscape by enabling businesses to analyze large, complex datasets in real time, automate insights generation, and democratize access to analytics through natural language interaction.

From self-service analytics to predictive forecasting, GenAI enhances the ability of organizations to uncover hidden trends, personalize decision support, and create new business opportunities. This training program equips participants with the knowledge and skills to apply generative AI tools for advanced data analytics—covering both conceptual foundations and hands-on practice.

Agenda / Content

Day 1 – Foundations of Generative AI & Data Analytics

• Introduction to Generative AI in the context of data analytics

• Traditional vs. AI-powered analytics: key differences

• Core technologies: Large Language Models (LLMs), NLP, multimodal AI

• Data sources for GenAI analytics (structured, unstructured, streaming data)

• Case Studies: Real-world use of GenAI in business analytics (finance, healthcare, retail)

• Hands-on Session: Exploring GenAI tools for data queries

Day 2 – Applications of GenAI in Business Analytics

• Automating data preparation and transformation with GenAI

• Conversational data exploration (using natural language for querying databases)

• Predictive & prescriptive analytics with GenAI

• AI-driven dashboards & visualization

• Reducing bias and error in AI-generated insights

• Workshop: Using a GenAI platform to analyze business datasets and generate reports

Day 3 – Implementation, Governance, and Future Trends

• Deploying GenAI-powered analytics in organizations

• Integrating with BI tools (Power BI, Tableau, Looker, etc.)

• Data privacy, security, and compliance considerations (GDPR, HIPAA, industry standards)

• AI ethics: Explainability, bias, and responsible analytics

• Future trends: Autonomous analytics, AI copilots for decision-making, multimodal analytics

• Group Project: Designing a GenAI-powered analytics use case for a specific business problem

• Wrap-up & Action Planning

Objectives

By the end of the program, participants will:

1. Understand the role of Generative AI in reshaping data analytics.

2. Learn how to use GenAI for querying, analyzing, and interpreting complex data.

3. Gain hands-on experience with GenAI-driven analytics platforms.

4. Explore real-world applications of GenAI across industries.

5. Recognize risks, ethical issues, and governance challenges in AI-powered analytics.

6. Develop a roadmap for implementing GenAI-powered analytics in their organizations.

Outcomes

Participants will leave with:

• A solid foundation in Generative AI techniques for analytics.

• The ability to use GenAI tools for data querying, reporting, and forecasting.

• Experience in building AI-driven dashboards and insights reports.

• A framework for integrating GenAI into existing data and BI systems.

• An understanding of responsible AI practices in analytics.

• A draft action plan/use case for applying GenAI analytics to their business context.