AI-Assisted Data Analytics and Storytelling

Course Description

Organizations need to gain insight into past trends, predict future behaviors, and identify opportunities that will allow them to stay ahead of their competition. Accomplishing this requires developing insights from large stores of data. Data analytic techniques are used to collect, analyze, and interpret data to find patterns, trends, and correlations that support better business decision-making.

Artificial intelligence can help analysts accelerate data analytics work. However, effective data analytics still depends on human judgment, business context, reliable data, sound analysis techniques, and careful interpretation of results.

This course provides a foundation in practical analytics tasks, including problem definition, data collection, data cleansing, data manipulation, analysis, visualization, and storytelling. Participants learn how to define the problem to solve, form a working hypothesis, identify and prepare analytical datasets, apply analysis techniques, and communicate findings in a clear and compelling way.

Using a case study or real-life initiative, participants engage in hands-on workshops throughout the course. AI is used as an integral part of the workshops to accelerate and enhance analysis work. Participants apply the B2T AI Review Framework to evaluate AI-generated content before it is used to support analytical conclusions, visualizations, recommendations, or business decisions.

NOTE: If you prefer our version of this course that teaches Data Analytics and Storytelling independent of AI, please review our original course outline.

Learning Objectives

• Understand the strategic and operational value of data and data analytics
• Use AI to accelerate problem definition, data preparation, analysis, visualization, and storytelling activities
• Leverage data analysis to improve the decision-making process
• Define the problem to solve and form a working hypothesis
• Identify data sources and create analytical datasets
• Recognize data quality issues that may affect analysis results
• Perform statistical analysis to test the hypothesis
• Distinguish between AI-generated suggestions and evidence-based analytical conclusions
• Develop a compelling visual representation to share analytical results
• Build a story around the visualization that draws attention to the right message
• Ensure integrity in data findings, visualizations, and recommendations
• Apply the B2T AI Review Framework to validate AI-generated content before it influences analytical conclusions or business decisions

Intended Audience

This course is designed for business analysts, project managers, decision-makers, systems analysts, data administrators, data scientists, database administrators, business intelligence analysts, product owners, product managers, and other project team members involved in business analysis, data analysis, reporting, analytics, or decision support.

This course may also be appropriate for individuals who manage or mentor business analysts or data analysts. Access to an AI tool such as Microsoft Copilot or ChatGPT is needed.

Prerequisites

While there are no specific course prerequisites, students should be familiar with basic concepts related to capturing, storing, and retrieving data from corporate data sources such as relational databases and spreadsheets. Students should be comfortable manipulating data and developing charts in Excel.

B2T AI Review Framework

Throughout this course, participants apply the B2T AI Review Framework whenever AI is used to support analysis activities. The framework helps analysts use AI to accelerate analysis work while ensuring that business decisions are grounded in human judgment, business context, and appropriate governance.

The framework follows a four-step cycle:

1. AI Generates – AI produces draft content, analysis artifacts, recommendations, or alternative approaches.
2. Human Checkpoint – The output is evaluated for accuracy, completeness, business context alignment, and practical usability.
3. Refine – The analyst improves the output through additional prompting, analysis, stakeholder input, and professional judgment.
4. Approve – The appropriate stakeholder reviews the information and determines the next course of action. The analyst provides the context and supporting information needed to enable effective decision-making.

Each AI-Assisted Workshop includes opportunities to apply this framework so participants learn to use AI as an analysis accelerator while providing the judgment, context, and expertise needed to deliver effective business solutions.