Introduction

The convergence of Artificial Intelligence (AI) and Business Intelligence (BI) is fundamentally reshaping how organizations extract value from their data, moving beyond descriptive analytics to unlock predictive insights, automate discovery, and enhance decision-making with unprecedented speed and precision. Artificial Intelligence in Business Intelligence empowers professionals to leverage advanced algorithms and machine learning capabilities embedded within modern BI platforms, transforming raw data into smarter, more actionable intelligence and enabling proactive strategies that drive innovation and competitive advantage. This training course is meticulously designed to equip BI professionals, data analysts, data scientists, IT managers, and business leaders with cutting-edge knowledge and practical skills in understanding the symbiotic relationship between AI and BI, mastering the application of machine learning concepts (e.g., forecasting, anomaly detection, predictive analytics) within BI tools, utilizing natural language processing (NLP) for conversational analytics, exploring automated insights generation, and addressing the ethical and governance considerations of integrating AI into BI workflows. Participants will gain a comprehensive understanding of how to harness AI to augment human intelligence, automate analytical tasks, and deliver deeper, more impactful insights that accelerate business growth and optimize operations.

Duration

10 days

Target Audience

• Business Intelligence Professionals
• Data Analysts
• Data Scientists (interested in BI application)
• IT Managers & Directors
• Solution Architects
• Business Leaders & Executives
• Advanced Excel Users aspiring to BI/AI
• Consultants in BI & Analytics
• Product Managers of data-driven products
• Anyone looking to leverage AI in their analytical workflows

Objectives

• Understand the foundational concepts of Artificial Intelligence and Machine Learning relevant to BI.
• Master the integration points and symbiotic relationship between AI and modern BI platforms.
• Learn to apply AI-driven features in leading BI tools (e.g., automated insights, natural language query).
• Develop proficiency in building and interpreting predictive models within BI environments.
• Understand anomaly detection and forecasting techniques for business use cases.
• Explore the ethical considerations and governance frameworks for AI in BI.
• Develop skills in preparing and feature engineering data for AI/ML models.
• Learn about robotic process automation (RPA) and its application in BI workflows.
• Understand how AI can automate data preparation and data quality processes.
• Formulate strategies for implementing and scaling AI-driven BI initiatives.
• Apply AI techniques to solve complex business problems and generate proactive insights.

Course Content

Module 1. Introduction to AI and its Relevance to Business Intelligence

• Defining Artificial Intelligence (AI): Machine Learning, Deep Learning, NLP
• The Evolution of BI: From descriptive to diagnostic, predictive, and prescriptive analytics
• The Convergence of AI and BI: How AI augments human intelligence
• Benefits of AI in BI: Automated insights, enhanced predictions, efficiency
• Overview of common AI applications in business

Module 2. Foundational Machine Learning Concepts for BI

• Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
• Common ML Algorithms: Regression, Classification, Clustering
• Understanding Training Data, Validation Data, Test Data
• Feature Engineering: Preparing data for ML models
• Model Evaluation Metrics (e.g., Accuracy, Precision, Recall)

Module 3. AI-Powered Features in Modern BI Tools (Overview)

• Automated Insights Generation: Identifying trends, outliers, key drivers
• Natural Language Query (NLQ): Asking questions in plain English
• Smart Narratives: AI-generated text descriptions of visuals
• Anomaly Detection capabilities
• Forecasting and Predictive capabilities within BI platforms (e.g., Power BI, Tableau)

Module 4. Data Preparation and Feature Engineering for AI in BI

• Advanced Data Cleaning and Transformation: Handling messy data for ML
• Feature Selection and Extraction: Identifying relevant variables
• Data Scaling and Normalization
• Handling Categorical Data: One-hot encoding, label encoding
• Using BI tools' data prep features (e.g., Power Query, Tableau Prep) for ML readiness

Module 5. Predictive Analytics in BI Tools

• Introduction to Predictive Modeling: Concepts and use cases
• Building Regression Models: Predicting continuous values (e.g., sales forecasting)
• Building Classification Models: Predicting categories (e.g., customer churn)
• Interpreting Model Results: Coefficients, p-values (conceptual)
• Visualizing predictions and confidence intervals

Module 6. Anomaly Detection and Outlier Analysis

• What are Anomalies?: Identifying unusual patterns or events
• Common Anomaly Detection Techniques: Statistical methods, clustering-based
• Applying anomaly detection features in BI tools
• Use cases: Fraud detection, operational fault detection, sudden spikes/drops
• Alerting and monitoring for anomalies

Module 7. Forecasting and Time Series Analysis

• Introduction to Time Series Data: Components (trend, seasonality, cycle, randomness)
• Forecasting Models: Simple moving average, exponential smoothing (conceptual)
• Using built-in forecasting features in BI tools
• Evaluating forecast accuracy
• Visualizing forecasts and actuals for performance monitoring

Module 8. Natural Language Processing (NLP) in BI

• Understanding NLP: Processing and understanding human language
• Natural Language Query (NLQ) in Power BI Q&A, Tableau Ask Data
• Semantic search and knowledge graphs in BI
• Text Analytics for unstructured data (e.g., customer feedback, social media)
• Extracting sentiments and keywords from text

Module 9. Automated Insights and Explainable AI (XAI)

• Automated Discovery: BI tools identifying hidden patterns and relationships
• Explainable AI (XAI): Understanding why AI made a certain prediction
• Feature importance and drivers analysis
• Building trust and transparency in AI-driven insights
• Communicating AI results to non-technical audiences

Module 10. Robotic Process Automation (RPA) and BI Automation

• Introduction to RPA: Automating repetitive, rule-based tasks
• RPA in BI: Automating data extraction, report generation, data validation
• Integrating RPA with BI workflows
• Benefits of RPA for efficiency and accuracy in BI operations
• Case studies of RPA in data processes

Module 11. Ethical AI and Data Governance in BI

• Ethical Considerations of AI: Bias, fairness, transparency, accountability
• Data Governance for AI: Ensuring data quality, privacy, and security for ML models
• AI Model Governance: Versioning, monitoring, retraining
• Compliance with AI regulations and guidelines
• Building a responsible AI strategy for BI

Module 12. Cloud AI Services Integration with BI

• Overview of Cloud AI Services: AWS AI/ML, Azure AI/ML, Google Cloud AI
• Integrating custom ML models from cloud platforms into BI tools
• Using pre-built AI services for text analytics, image analysis, sentiment analysis
• Leveraging cloud-based data science platforms for deeper analysis
• Cost implications of cloud AI services

Module 13. Advanced Analytics with AI and BI

• Customer Segmentation: Using clustering algorithms for market analysis
• Churn Prediction Models: Identifying customers at risk
• Recommendation Engines: Personalizing user experiences
• Integrating advanced statistical methods with BI visuals
• Building custom AI solutions within BI environments (e.g., using R/Python scripts)

Module 14. Implementing and Scaling AI in BI Initiatives

• Strategy for AI Adoption in BI: Pilot projects, phased rollout
• Team Roles: Data Scientists, BI Developers, MLOps Engineers
• Data Infrastructure for AI in BI: Scalable storage and compute
• User Adoption and Change Management for AI-powered insights
• Measuring the ROI and impact of AI in BI

Module 15. The Future of AI in Business Intelligence

• Augmented Analytics Evolution: Towards fully autonomous insights
• Conversational AI: More sophisticated natural language interfaces
• Real-time AI for operational intelligence
• AI-driven Data Fabric and Data Mesh architectures
• The expanding role of the Citizen Data Scientist with AI tools.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.