Data Modeling Strategies for Smarter Business Intelligence Systems Training Course

Introduction

Data modeling is a cornerstone of effective business intelligence systems, ensuring that complex data is organized, structured, and optimized for reporting and analytics. Organizations that depend on data-driven decision-making require robust data models to transform raw information into meaningful insights that align with strategic objectives. This training program introduces participants to the principles and best practices of data modeling, enabling them to design models that power efficient BI systems and deliver accurate, actionable intelligence.

By blending theoretical foundations with practical applications, the course covers conceptual, logical, and physical data modeling, schema design, and the use of modeling tools. Participants will gain the expertise to create scalable, high-quality data models that support dashboards, reports, and predictive analytics. Through hands-on sessions and case studies, learners will develop the confidence to apply data modeling techniques in real-world BI environments.

Duration: 10 Days

Target Audience

  • Business intelligence professionals and analysts
  • Data architects and database administrators
  • Data engineers and software developers
  • IT professionals managing BI projects
  • Managers and decision-makers working with BI systems

10 Objectives

  1. Understand the fundamentals of data modeling for BI
  2. Explore conceptual, logical, and physical data models
  3. Design star and snowflake schemas for BI systems
  4. Apply normalization and denormalization techniques
  5. Use metadata effectively in BI environments
  6. Gain practical skills in data modeling tools
  7. Build scalable and high-performance BI data models
  8. Integrate data modeling with data warehouses and ETL processes
  9. Apply governance and quality standards in data models
  10. Explore advanced modeling techniques for predictive and real-time BI

15 Course Modules

Module 1: Introduction to Data Modeling in BI

  • Importance of data modeling in BI systems
  • Core concepts and terminology
  • Data modeling lifecycle overview
  • BI challenges solved by data modeling
  • Case examples of BI data models

Module 2: Conceptual Data Modeling

  • Purpose and scope of conceptual models
  • Identifying entities and relationships
  • High-level diagrams for BI projects
  • Stakeholder communication with conceptual models
  • Tools for conceptual modeling

Module 3: Logical Data Modeling

  • Role of logical models in BI
  • Attributes, keys, and relationships
  • Normalization principles
  • Logical model validation techniques
  • Transition from conceptual to logical models

Module 4: Physical Data Modeling

  • Translating logical models into physical designs
  • Defining tables, columns, and constraints
  • Indexing strategies in physical models
  • Physical storage considerations
  • Case examples of physical models

Module 5: Star Schema Design

  • Structure of star schema in BI
  • Fact and dimension tables explained
  • Advantages of star schema in reporting
  • Performance considerations
  • Practical examples of star schema

Module 6: Snowflake Schema Design

  • Structure and purpose of snowflake schema
  • Key differences from star schema
  • Benefits and drawbacks of snowflake models
  • Normalization in snowflake schema
  • Real-world use cases

Module 7: Normalization and Denormalization

  • Fundamentals of normalization
  • Different normal forms explained
  • When to apply denormalization
  • Balancing efficiency and performance
  • Examples in BI projects

Module 8: Metadata and Data Dictionaries

  • Role of metadata in BI systems
  • Creating and managing data dictionaries
  • Metadata-driven modeling approaches
  • Enhancing model transparency with metadata
  • Governance through metadata

Module 9: Dimensional Modeling for BI

  • Principles of dimensional modeling
  • Time dimension and slowly changing dimensions
  • Role-playing dimensions in BI
  • Hierarchies and drill-down paths
  • Dimensional modeling best practices

Module 10: Fact Table Design

  • Types of fact tables (transactional, snapshot, accumulating)
  • Choosing the right fact table design
  • Granularity in fact tables
  • Fact table keys and measures
  • Optimization of fact table queries

Module 11: Data Modeling Tools and Techniques

  • Overview of popular modeling tools
  • Model visualization and documentation
  • Automated model generation
  • Collaborative modeling techniques
  • Best practices for tool selection

Module 12: Data Governance in Modeling

  • Importance of governance in data models
  • Standards and compliance requirements
  • Version control in modeling projects
  • Ensuring consistency across BI systems
  • Roles of data stewards and architects

Module 13: Integrating Models with ETL and Warehousing

  • Linking models with ETL pipelines
  • Ensuring consistency during extraction and transformation
  • Staging areas and their role in modeling
  • Data warehouse alignment with BI models
  • Real-world integration case studies

Module 14: Performance Optimization in Data Models

  • Identifying modeling bottlenecks
  • Indexing and partitioning strategies
  • Query performance tuning with models
  • Caching and pre-aggregation techniques
  • Monitoring and maintaining model performance

Module 15: Advanced Data Modeling Trends

  • Real-time and streaming data modeling
  • Data lakehouse and hybrid approaches
  • Modeling for predictive and prescriptive analytics
  • AI-assisted data modeling techniques
  • Future-proofing BI models for scalability

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

 

Data Modeling Strategies For Smarter Business Intelligence Systems Training Course in Kenya
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