Introduction

In the dynamic realm of data-driven decision-making, selecting the right Business Intelligence (BI) tool is a pivotal strategic choice that profoundly impacts an organization's ability to extract value from its data, democratize insights, and foster innovation. BI Tools Comparison: Power BI vs Tableau vs Looker offers an in-depth exploration of the industry's leading platforms, empowering professionals to make informed decisions tailored to their specific business needs, technical capabilities, and data ecosystem. These tools, while all designed for data visualization and analysis, possess distinct strengths in areas such as data modeling, user experience, scalability, integration, and pricing, making a comprehensive comparative understanding essential for optimal deployment. This training course is meticulously designed to equip data architects, BI developers, data analysts, IT managers, and business leaders with cutting-edge knowledge and practical skills in evaluating the core functionalities of Power BI, Tableau, and Looker, mastering their unique approaches to data connectivity and transformation, understanding their visualization capabilities and data governance frameworks, analyzing their performance and scalability, and strategically assessing their total cost of ownership to select and implement the most effective BI solution for their organization. Participants will gain a holistic perspective on the BI landscape, enabling them to confidently navigate the complexities of tool selection and build a robust analytics foundation.

Duration

10 days

Target Audience

• Data Architects
• Business Intelligence (BI) Developers
• Data Analysts
• Solution Architects
• IT Managers & Directors
• Enterprise Architects
• Project Managers overseeing BI initiatives
• Data Strategy Consultants
• Business Leaders evaluating BI investments
• Anyone involved in BI tool selection and implementation

Objectives

• Understand the core philosophies and strengths of Power BI, Tableau, and Looker.
• Master the data connectivity and ingestion capabilities of each tool.
• Learn to compare and contrast their data modeling approaches (e.g., DAX, VizQL, LookML).
• Develop proficiency in evaluating their data visualization and dashboarding functionalities.
• Understand their respective strengths in scalability, performance, and real-time analytics.
• Explore their data governance, security, and administration features.
• Learn about their pricing models and total cost of ownership (TCO).
• Develop skills in assessing user experience and learning curves for different personas.
• Understand their integration capabilities within broader tech ecosystems.
• Formulate a strategic framework for evaluating and selecting the most appropriate BI tool.
• Apply comparative analysis to real-world scenarios and make data-driven tool recommendations.

Course Content

Module 1. Introduction to Business Intelligence Tools Landscape

• Defining BI Tools: Role in data-driven decision-making
• Overview of the modern BI ecosystem and its evolution
• Introduction to Power BI, Tableau, and Looker: Their market positions and general strengths
• Key criteria for evaluating BI tools: Data, Visualization, Governance, Performance, Cost
• Setting the stage for a comprehensive comparison

Module 2. Power BI Deep Dive: Architecture and Core Features

• Power BI Desktop, Service, Gateway, Report Server: Understanding the ecosystem
• Data Connectivity in Power BI: Extensive range of connectors, Power Query
• Data Modeling with DAX: Calculated columns, measures, relationships
• Power BI Visualization Capabilities: Charts, maps, custom visuals
• Integration with Microsoft Ecosystem (Azure, Excel, Teams)

Module 3. Tableau Deep Dive: Architecture and Core Features

• Tableau Desktop, Server, Cloud, Prep: Understanding the ecosystem
• Data Connectivity in Tableau: Wide range of connectors, live vs. extract
• Data Modeling with VizQL: Drag-and-drop interface, calculated fields
• Tableau Visualization Capabilities: Visual storytelling, advanced charting
• Strengths in ad-hoc analysis and data exploration

Module 4. Looker Deep Dive: Architecture and Core Features

• Looker Platform: Cloud-native, in-database architecture
• Data Connectivity in Looker: Primarily SQL databases, data warehouses
• Data Modeling with LookML: Semantic layer, single source of truth
• Looker Visualization Capabilities: Customizable, drill-down features
• Integration with Google Cloud Ecosystem (BigQuery, GCP)

Module 5. Data Connectivity and Integration Comparison

• Breadth of Connectors: Out-of-the-box vs. custom connectors
• Live Connection vs. Data Extraction: Performance implications, freshness
• Data Blending Capabilities: How each tool handles combining disparate sources
• ETL/ELT Integration: Power Query vs. Tableau Prep vs. Looker's SQL generation
• Scalability of data ingestion for large datasets

Module 6. Data Modeling and Transformation Comparison

• Approaches to Data Modeling: DAX (Power BI), VizQL (Tableau), LookML (Looker)
• Semantic Layer: How each tool defines and manages business logic
• Handling Complex Transformations: ETL capabilities within the tools
• Reusability of data models and calculations
• Impact on data consistency and governance

Module 7. Data Visualization and Dashboarding Comparison

• Range of Visualization Types: Standard, advanced, custom visuals
• Interactivity and User Experience: Filters, drill-downs, dynamic dashboards
• Design Flexibility and Customization: Aesthetic control, branding
• Data Storytelling Capabilities: Guiding users through insights
• Ease of use for creating and consuming visualizations

Module 8. Performance and Scalability Comparison

• In-Memory vs. In-Database Processing: VertiPaq, SPICE, direct query
• Handling Large Datasets: Performance with big data volumes
• Query Optimization: How each tool optimizes queries to the data source
• Scalability of the platform: From departmental to enterprise-wide deployments
• Real-time analytics capabilities and latency

Module 9. Data Governance and Security Comparison

• Centralized Data Governance: Defining metrics, access control
• Row-Level Security (RLS) and Column-Level Security (CLS)
• User Authentication and Authorization: Integration with enterprise directories
• Audit Trails and Usage Monitoring
• Data Lineage and Metadata Management

Module 10. Collaboration and Sharing Features Comparison

• Sharing Reports and Dashboards: Internal and external sharing options
• Collaboration Workflows: Comments, subscriptions, alerts
• Embedding Analytics: Integrating BI content into other applications
• Mobile Accessibility and Experience
• Version Control and Deployment Management

Module 11. User Experience and Learning Curve Comparison

• Target Audience: Business users, data analysts, data engineers
• Ease of Use for Beginners: Drag-and-drop interfaces, intuitive design
• Learning Curve for Advanced Features: DAX, complex calculations, LookML
• Community Support and Learning Resources
• Training availability and skill development paths

Module 12. Pricing Models and Total Cost of Ownership (TCO) Comparison

• Licensing Models: Per-user, capacity-based, enterprise agreements
• Cloud Infrastructure Costs: For Looker, and cloud-hosted Power BI/Tableau
• Maintenance and Support Costs
• Hidden Costs: Development time, training, data storage
• Case studies on TCO for different organizational sizes

Module 13. Ecosystem Integration and Extensibility Comparison

• Integration with Cloud Providers: Azure, AWS, GCP
• API Availability and Customization: Extending functionality
• Integration with Data Science Tools (Python, R)
• Embedding and OEM opportunities
• Partner ecosystems and third-party solutions

Module 14. Real-World Use Cases and Best-Fit Scenarios

• Power BI Best Use Cases: Microsoft-centric organizations, self-service BI
• Tableau Best Use Cases: Visual data exploration, advanced analytics, data storytelling
• Looker Best Use Cases: Centralized data governance, modern data stack, embedded analytics
• Industry-specific applications of each tool
• Understanding organizational requirements to match with tool strengths

Module 15. Strategic Selection and Implementation

• Developing a BI Tool Evaluation Framework: Scoring, weighting criteria
• Proof-of-Concept (POC) Planning and Execution
• Migration Strategies from existing BI tools
• Change Management and User Adoption in BI implementation
• Building a long-term BI strategy around the chosen tool(s).

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.