Business Intelligence Training Service | in Dammam - Riyadh - Jeddah - Makkah
Business intelligence training covering data analysis, visualization, dashboards & reporting using modern BI tools for actionable insights.
Course Title
Business Intelligence
Course Duration
1 Day
Competency Assessment Criteria
Practical Assessment and Knowledge Assessment
Training Delivery Method
Classroom (Instructor-Led) or Online (Instructor-Led)
Service Coverage
Saudi Arabia - Bahrain - Kuwait - Philippines
Course Average Passing Rate
96%
Post Training Reporting
Post Training Report(s) + Candidate(s) Training Evaluation Forms
Certificate of Successful Completion
Certification is provided upon successful completion. The certificate can be verified through a QR-Code system.
Certification Provider
Tamkene Saudi Training Center - Approved by TVTC (Technical and Vocational Training Corporation)
Certificate Validity
2 Years (Extendable with additional training hours)
Instructors Languages
English / Arabic / Urdu / Hindi / Pashto
Training Services Design Methodology
ADDIE Training Design Methodology
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Course Overview
This intensive Business Intelligence training course equips professionals with essential skills to transform raw data into meaningful insights that drive strategic decision-making. Participants learn data analysis techniques, visualization best practices, dashboard design principles, and reporting methodologies using industry-standard BI tools and platforms.
The course emphasizes practical applications through hands-on exercises with real business datasets, covering data connectivity, transformation techniques, interactive visualizations, and self-service analytics. Participants develop competency in creating compelling dashboards, performing trend analysis, building KPI scorecards, and delivering data-driven insights that enable informed business decisions across organizational functions.
Key Learning Objectives
Apply fundamental business intelligence concepts including data warehousing, OLAP, dimensional modeling, and analytics terminology
Connect to multiple data sources and perform data preparation including cleaning, transformation, and integration techniques
Create effective data visualizations using appropriate chart types, formatting techniques, and design principles for clarity
Design interactive dashboards with filters, parameters, and drill-down capabilities for dynamic business reporting
Implement calculated fields and measures using DAX or similar formula languages for advanced analytics
Analyze business performance through KPI development, trend analysis, variance reporting, and comparative metrics
Apply data storytelling techniques to communicate insights effectively to stakeholders and decision-makers
Develop self-service analytics capabilities enabling users to explore data independently and generate ad-hoc reports
Group Exercises
Dashboard critique workshop including (evaluating sample dashboards, identifying strengths and weaknesses, proposing improvements, redesigning collaboratively)
Data story development including (analyzing provided dataset, identifying key insights, structuring narrative, creating supporting visualizations)
Requirements gathering roleplay including (interviewing business stakeholders, documenting requirements, defining KPIs, prioritizing features)
KPI definition exercise including (selecting business metrics, establishing targets, determining calculation methods, designing scorecard layout)
Knowledge Assessment
Written examination including (multiple-choice questions on BI concepts, scenario-based visualization selection, calculation logic problems)
Data modeling quiz including (identifying appropriate relationships, star schema design, hierarchy creation, optimization techniques)
Chart selection exercises including (matching data types to visualizations, identifying design flaws, recommending improvements)
DAX formula challenges including (writing basic calculations, interpreting complex formulas, troubleshooting calculation errors)
Course Outline
1. Business Intelligence Fundamentals
BI concepts and terminology including (data warehouses, data marts, OLAP cubes, dimensional modeling principles)
BI architecture components including (data sources, ETL processes, analytical layer, presentation layer)
Business intelligence lifecycle including (requirements gathering, data modeling, development, deployment, maintenance cycles)
Key performance indicators including (lagging indicators, leading indicators, SMART criteria, balanced scorecard methodology)
2. Data Connectivity and Preparation
Data source connections including (relational databases, Excel files, cloud services, web APIs and feeds)
Data import methods including (live connections, extract refresh schedules, incremental loads, performance optimization)
Data cleaning techniques including (removing duplicates, handling null values, standardizing formats, error correction)
Data transformation operations including (filtering rows, pivoting and unpivoting, merging tables, creating calculated columns)
3. Data Modeling and Relationships
Dimensional modeling concepts including (fact tables, dimension tables, star schema, snowflake schema design)
Table relationships including (one-to-many, many-to-many, relationship cardinality, cross-filter direction)
Data model optimization including (reducing data volume, column elimination, relationship efficiency, calculation placement)
Hierarchies and groupings including (date hierarchies, organizational hierarchies, custom grouping, drill-down paths)
4. Visualization Design and Best Practices
Chart type selection including (bar charts for comparisons, line charts for trends, pie charts for composition, scatter plots for correlation)
Visual design principles including (color theory application, whitespace utilization, typography selection, visual hierarchy establishment)
Dashboard layout including (grid alignment, logical flow, information grouping, responsive design considerations)
Accessibility considerations including (color blindness accommodation, contrast ratios, alternative text, screen reader compatibility)
5. Creating Interactive Dashboards
Dashboard planning including (audience identification, key metrics selection, layout wireframing, interaction design)
Filter implementation including (visual-level filters, page-level filters, report-level filters, filter hierarchies)
Parameters and dynamic content including (user input controls, what-if parameters, dynamic titles, conditional formatting)
Drill-through and drill-down including (detail page creation, navigation buttons, tooltip customization, breadcrumb implementation)
6. Advanced Analytics and Calculations
Calculated fields including (arithmetic operations, text concatenation, conditional logic, type conversions)
Aggregate functions including (SUM, AVERAGE, COUNT, MIN, MAX with filtering contexts)
Time intelligence calculations including (year-to-date, month-over-month, rolling averages, period comparisons)
Advanced DAX formulas including (CALCULATE function, filter contexts, row contexts, variable usage for optimization)
7. Performance Analysis and Reporting
KPI scorecard development including (metric definition, target setting, status indicators, trend arrows)
Variance analysis including (actual versus budget, current versus prior period, percentage variance, absolute variance)
Trend analysis techniques including (moving averages, forecasting, seasonality identification, growth rate calculations)
Cohort analysis including (customer segmentation, retention analysis, behavioral grouping, time-based cohorts)
8. Data Storytelling and Presentation
Narrative structure including (situation, complication, resolution framework, insight prioritization, call-to-action development)
Annotation techniques including (callout boxes, reference lines, highlighted insights, contextual notes)
Report distribution including (scheduled refresh, email subscriptions, embedded reports, mobile optimization)
Presentation best practices including (walking through insights, highlighting key findings, handling questions, actionable recommendations)
Practical Assessment
Dashboard development project including (connecting to provided dataset, creating data model, building interactive dashboard, implementing filters)
Visualization creation exercise including (selecting appropriate chart types, applying design principles, formatting for clarity, adding interactivity)
Calculated field implementation including (creating time intelligence measures, developing KPI calculations, building conditional logic, testing accuracy)
Presentation simulation including (explaining dashboard insights, walking through visualizations, answering stakeholder questions, recommending actions)
Gained Core Technical Skills
Data connectivity including (connecting multiple sources, import methods, refresh scheduling, connection management)
Data preparation including (cleaning techniques, transformation operations, data quality validation, integration methods)
Data modeling including (relationship creation, dimensional design, hierarchy development, model optimization)
Visualization design including (chart type selection, formatting techniques, design principles, accessibility standards)
Dashboard development including (layout design, filter implementation, parameter creation, drill-through functionality)
Calculations including (calculated fields, aggregate functions, time intelligence, DAX formula fundamentals)
Performance analysis including (KPI scorecards, variance analysis, trend identification, comparative reporting)
Data storytelling including (narrative structure, insight presentation, stakeholder communication, actionable recommendations)
Training Design Methodology
ADDIE Training Design Methodology
Targeted Audience
Business Analysts responsible for reporting, analysis, and insight generation from organizational data
Data Analysts seeking to enhance visualization and dashboard development capabilities
Financial Analysts creating performance reports and budget variance analysis dashboards
Operations Managers requiring KPI tracking and operational performance monitoring tools
Marketing Analysts developing campaign performance dashboards and customer analytics
Sales Managers tracking sales metrics, pipeline analysis, and territory performance
Project Managers needing project status dashboards and resource utilization reporting
Department managers and executives seeking self-service analytics capabilities for decision support
Why Choose This Course
Hands-on training with industry-standard BI tools and real business datasets
Practical focus on creating dashboards and reports immediately usable in workplace
Comprehensive coverage from data preparation through advanced analytics and storytelling
Design principles and best practices for professional, executive-ready visualizations
Self-service analytics skills enabling independent data exploration and insight discovery
Note
Note: This course outline, including specific topics, modules, and duration, can be customized based on the specific needs and requirements of the client.
Course Outline
1. Business Intelligence Fundamentals
BI concepts and terminology including (data warehouses, data marts, OLAP cubes, dimensional modeling principles)
BI architecture components including (data sources, ETL processes, analytical layer, presentation layer)
Business intelligence lifecycle including (requirements gathering, data modeling, development, deployment, maintenance cycles)
Key performance indicators including (lagging indicators, leading indicators, SMART criteria, balanced scorecard methodology)
2. Data Connectivity and Preparation
Data source connections including (relational databases, Excel files, cloud services, web APIs and feeds)
Data import methods including (live connections, extract refresh schedules, incremental loads, performance optimization)
Data cleaning techniques including (removing duplicates, handling null values, standardizing formats, error correction)
Data transformation operations including (filtering rows, pivoting and unpivoting, merging tables, creating calculated columns)
3. Data Modeling and Relationships
Dimensional modeling concepts including (fact tables, dimension tables, star schema, snowflake schema design)
Table relationships including (one-to-many, many-to-many, relationship cardinality, cross-filter direction)
Data model optimization including (reducing data volume, column elimination, relationship efficiency, calculation placement)
Hierarchies and groupings including (date hierarchies, organizational hierarchies, custom grouping, drill-down paths)
4. Visualization Design and Best Practices
Chart type selection including (bar charts for comparisons, line charts for trends, pie charts for composition, scatter plots for correlation)
Visual design principles including (color theory application, whitespace utilization, typography selection, visual hierarchy establishment)
Dashboard layout including (grid alignment, logical flow, information grouping, responsive design considerations)
Accessibility considerations including (color blindness accommodation, contrast ratios, alternative text, screen reader compatibility)
5. Creating Interactive Dashboards
Dashboard planning including (audience identification, key metrics selection, layout wireframing, interaction design)
Filter implementation including (visual-level filters, page-level filters, report-level filters, filter hierarchies)
Parameters and dynamic content including (user input controls, what-if parameters, dynamic titles, conditional formatting)
Drill-through and drill-down including (detail page creation, navigation buttons, tooltip customization, breadcrumb implementation)
6. Advanced Analytics and Calculations
Calculated fields including (arithmetic operations, text concatenation, conditional logic, type conversions)
Aggregate functions including (SUM, AVERAGE, COUNT, MIN, MAX with filtering contexts)
Time intelligence calculations including (year-to-date, month-over-month, rolling averages, period comparisons)
Advanced DAX formulas including (CALCULATE function, filter contexts, row contexts, variable usage for optimization)
7. Performance Analysis and Reporting
KPI scorecard development including (metric definition, target setting, status indicators, trend arrows)
Variance analysis including (actual versus budget, current versus prior period, percentage variance, absolute variance)
Trend analysis techniques including (moving averages, forecasting, seasonality identification, growth rate calculations)
Cohort analysis including (customer segmentation, retention analysis, behavioral grouping, time-based cohorts)
8. Data Storytelling and Presentation
Narrative structure including (situation, complication, resolution framework, insight prioritization, call-to-action development)
Annotation techniques including (callout boxes, reference lines, highlighted insights, contextual notes)
Report distribution including (scheduled refresh, email subscriptions, embedded reports, mobile optimization)
Presentation best practices including (walking through insights, highlighting key findings, handling questions, actionable recommendations)
Why Choose This Course?
Hands-on training with industry-standard BI tools and real business datasets
Practical focus on creating dashboards and reports immediately usable in workplace
Comprehensive coverage from data preparation through advanced analytics and storytelling
Design principles and best practices for professional, executive-ready visualizations
Self-service analytics skills enabling independent data exploration and insight discovery
Note: This course outline, including specific topics, modules, and duration, can be customized based on the specific needs and requirements of the client.
Practical Assessment
Dashboard development project including (connecting to provided dataset, creating data model, building interactive dashboard, implementing filters)
Visualization creation exercise including (selecting appropriate chart types, applying design principles, formatting for clarity, adding interactivity)
Calculated field implementation including (creating time intelligence measures, developing KPI calculations, building conditional logic, testing accuracy)
Presentation simulation including (explaining dashboard insights, walking through visualizations, answering stakeholder questions, recommending actions)
Course Overview
This intensive Business Intelligence training course equips professionals with essential skills to transform raw data into meaningful insights that drive strategic decision-making. Participants learn data analysis techniques, visualization best practices, dashboard design principles, and reporting methodologies using industry-standard BI tools and platforms.
The course emphasizes practical applications through hands-on exercises with real business datasets, covering data connectivity, transformation techniques, interactive visualizations, and self-service analytics. Participants develop competency in creating compelling dashboards, performing trend analysis, building KPI scorecards, and delivering data-driven insights that enable informed business decisions across organizational functions.
Key Learning Objectives
Apply fundamental business intelligence concepts including data warehousing, OLAP, dimensional modeling, and analytics terminology
Connect to multiple data sources and perform data preparation including cleaning, transformation, and integration techniques
Create effective data visualizations using appropriate chart types, formatting techniques, and design principles for clarity
Design interactive dashboards with filters, parameters, and drill-down capabilities for dynamic business reporting
Implement calculated fields and measures using DAX or similar formula languages for advanced analytics
Analyze business performance through KPI development, trend analysis, variance reporting, and comparative metrics
Apply data storytelling techniques to communicate insights effectively to stakeholders and decision-makers
Develop self-service analytics capabilities enabling users to explore data independently and generate ad-hoc reports
Knowledge Assessment
Written examination including (multiple-choice questions on BI concepts, scenario-based visualization selection, calculation logic problems)
Data modeling quiz including (identifying appropriate relationships, star schema design, hierarchy creation, optimization techniques)
Chart selection exercises including (matching data types to visualizations, identifying design flaws, recommending improvements)
DAX formula challenges including (writing basic calculations, interpreting complex formulas, troubleshooting calculation errors)
Targeted Audience
Business Analysts responsible for reporting, analysis, and insight generation from organizational data
Data Analysts seeking to enhance visualization and dashboard development capabilities
Financial Analysts creating performance reports and budget variance analysis dashboards
Operations Managers requiring KPI tracking and operational performance monitoring tools
Marketing Analysts developing campaign performance dashboards and customer analytics
Sales Managers tracking sales metrics, pipeline analysis, and territory performance
Project Managers needing project status dashboards and resource utilization reporting
Department managers and executives seeking self-service analytics capabilities for decision support
Main Service Location
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