AI for Business Intelligence Data-Driven Decisions for Success #254039

Upon completion of this course, participants will be able to:

• Integrate AI into the BI Workflow: Understand how AI and ML models enhance traditional reporting and dashboarding to provide predictive and prescriptive insights.

• Master AI-Powered Analytics Tools: Gain practical experience with AI features in leading BI platforms (e.g., Power BI, Tableau, ThoughtSpot) to automate analysis and generate natural language insights.

• Build Predictive and Prescriptive Models: Develop and interpret key AI models for business forecasting, customer segmentation (clustering), and anomaly detection.

• Automate Insight Generation: Implement processes for automated report generation, alerting, and distribution of AI-driven insights to stakeholders.

• Cultivate a Data-Driven Culture: Develop strategies to communicate AI-generated insights effectively to influence decision-making across all levels of the organization.

• Evaluate AI BI Solutions: Assess and select the right AI-powered BI tools and technologies based on organizational needs and data maturity.

This course is designed for professionals who use data to guide business strategy and operations:

• Business Intelligence Analysts & Developers

• Data Analysts & Reporting Specialists

• Business Analysts & Product Analysts

• Marketing & Sales Analysts

• Financial Analysts & FP&A Professionals

• Operations & Supply Chain Analysts

• BI Managers & Heads of Analytics

• IT Professionals supporting BI and analytics infrastructure

• Department Heads and Managers who rely on data for strategic decisions

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: The New Era of Intelligent BI

• AM: From Descriptive to Predictive and Prescriptive

  • The evolution of BI: Reporting -> Analytics -> AI-Driven Intelligence.

  • Key AI concepts for BI: Machine Learning, Natural Language Processing (NLP), and forecasting.

  • The AI-BI ecosystem: Understanding the tool landscape (e.g., Power BI, Tableau CRM, Qlik Sense, specialized AI platforms).

• PM: The Foundation: Data Strategy for AI

  • Ensuring data quality and governance for reliable AI outputs.

  • Connecting to diverse data sources: Data warehouses, lakes, and cloud services.

  • Workshop: Assessing the AI readiness of a sample dataset.

Day 2: AI-Powered Analytics & Automated Insights

• AM: Augmented Analytics in Practice

  • Hands-on with AI features in BI tools: Power BI’s Quick Insights or Tableau’s Ask Data.

  • Automated pattern recognition and anomaly detection.

  • Hands-on Lab: Using a BI tool to automatically find insights in a sales dataset.

• PM: Natural Language Generation (NLG)

  • How AI writes summaries and explanations of charts and data.

  • Creating dynamic, narrative-driven reports that update automatically.

  • Workshop: Configuring an NLG feature to explain a key performance metric.

Day 3: Building Predictive Capabilities

• AM: Forecasting and Predictive Modeling

  • Introduction to time-series forecasting for demand planning, revenue prediction, and resource allocation.

  • Using built-in AI capabilities in BI tools (e.g., Power BI’s AI Insights) to generate forecasts without coding.

  • Hands-on Lab: Building and interpreting a forecast for future sales.

• PM: Segmentation with Clustering

  • Moving beyond manual rules: Using AI (e.g., k-means clustering) to automatically segment customers, products, or markets based on behavior.

  • Interpreting cluster results and applying them to strategy.

  • Case Study: Using customer clustering to design a targeted marketing campaign.

Day 4: From Insights to Action: Deployment and Integration

• AM: Operationalizing AI Insights

  • Setting up automated alerts and triggers based on AI-driven thresholds (e.g., inventory alert, churn risk alert).

  • Integrating AI insights into business workflows and applications (e.g., CRM, ERP).

• PM: Data Storytelling for Impact

  • Designing dashboards and presentations that effectively communicate AI-driven insights to executives and non-technical stakeholders.

  • Crafting a compelling narrative around the data to drive action.

  • Workshop: Building a executive-level dashboard that tells a story with AI-generated insights.

Day 5: Strategy, Ethics, and Capstone

• AM: Building an AI-Driven BI Strategy

  • Developing a roadmap for implementing AI-powered BI in your organization.

  • Measuring the ROI of AI in BI: From efficiency gains to revenue impact.

  • Addressing ethical considerations: Bias in AI models, data privacy, and explainability.

• PM: Capstone Project: The AI BI Sprint

  • Final Exercise: Teams are given a business scenario and a complex dataset. They must:

    1. Use a BI tool to perform automated analysis.

    2. Build a predictive forecast.

    3. Create a segmenting model.

    4. Design a final dashboard with AI-generated narratives and alerts.

  • Presentations and peer feedback.

  • Course Wrap-Up: Creating a personal action plan for implementation.