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

• Explain the end-to-end Business Intelligence lifecycle and architecture.
• Design effective data models and schemas for analytical reporting.
• Connect to various data sources and perform necessary data cleansing and transformation (ETL/ELT).
• Create impactful, interactive reports and dashboards using leading BI tools.
• Apply visualization best practices to communicate data insights clearly.
• Utilize advanced analytical functions (DAX, calculated fields) for complex metrics.
• Implement data security and access controls within BI environments.
• Deploy and manage reports and dashboards for enterprise-wide consumption.

• Business Analysts and Data Analysts
• IT and Business Managers who consume and require reports
• Data Science and Analytics Team Members
• Financial and Marketing Professionals
• Anyone seeking to implement or manage an organizational BI solution
• Executive Leadership interested in data visualization

• Hands-on workshops using a chosen BI tool (e.g., Power BI) to build dashboards.
• Group activities to design a data model for a specific business process.
• Critiques of poorly designed reports and redesign exercises.
• Individual exercises in writing DAX/calculated fields for advanced metrics.
• Case studies on successful BI implementation driving strategic change.

Unit 1: Fundamentals of Business Intelligence

• Defining Business Intelligence and its strategic importance.
• Overview of the BI architecture (Data Source, ETL, Data Warehouse, BI Tool).
• The role of data marts and OLAP vs. OLTP systems.
• Key BI capabilities: reporting, dashboarding, ad-hoc analysis.

• Introduction to Dimensional Modeling (Fact and Dimension Tables).
• Star and Snowflake schemas and their application in reporting.
• Best practices for data granularity and time intelligence.
• Understanding slowly changing dimensions (SCDs).

Unit 2: Data Preparation and ETL Processes

• Connecting BI tools to various data sources (databases, APIs, files).
• Identifying and handling missing values, outliers, and duplicates.
• Techniques for data transformation and harmonization.
• Implementing data governance and quality checks during ETL.

• Overview of ETL/ELT processes and tools.
• Designing efficient data loading strategies (incremental vs. full).
• Managing data lineage and tracking transformation history.
• Scheduling and monitoring the data pipeline performance.

Unit 3: Report and Dashboard Design

• Selecting the appropriate chart type for the data and objective.
• Principles of visual perception and cognitive load.
• Effective use of color, typography, and layout in dashboards.
• Avoiding misleading visualizations and ensuring data integrity.

• Developing interactive filters, drill-down capabilities, and action links.
• Designing dashboards for different user profiles (executive, operational).
• Techniques for storytelling with data in presentations.
• Optimizing dashboard performance and loading speed.

Unit 4: Advanced Analysis and Calculations

• Creating complex calculated fields and derived metrics.
• Introduction to DAX (Data Analysis Expressions) or similar languages.
• Implementing time intelligence functions (Year-over-Year, Rolling Averages).
• Cohort analysis and customer segmentation metrics.

• Establishing a self-service BI capability for business users.
• Governance model for managing decentralized report creation.
• Training and support strategies for self-service users.
• Ensuring data consistency and preventing conflicting reports.

Unit 5: BI Deployment and Management

• Managing user roles, permissions, and row-level security.
• Deploying reports to a central server or cloud environment.
• Monitoring platform usage and resource capacity.
• Setting up automated report subscriptions and alerts.

• The role of BI in fostering a data-driven organizational culture.
• Measuring the ROI and business impact of BI projects.
• Integrating BI with predictive analytics and AI tools.
• The future of augmented analytics and automated insight generation.