Summary of "Analytical data modeling" course

As we reach the conclusion of this data modeling course, we've provided a comprehensive wrap-up of everything we've covered below. In case you didn't go through the (full) course, this article will provide you the most important details to keep in mind during an analytical data modeling exercise!

Transactional data models, and why they're suboptimal for analytical consumption

Transactional data models are ideal for day-to-day operations, organizing data to efficiently handle CRUD tasks. However, they struggle with analytical queries due to the need for complex joins and aggregations across large datasets. Pre-aggregating data can offer quick insights but limits scalability. For long-term efficiency, use dedicated analytical models like the Star Schema.

Designing analytical data models

Optimized analytical data storage and retrieval using Star schemas

A Star Schema features a central fact table (storing measurements of business processes), typically linked to multiple dimension tables (storing attributes related to those measurements). This structure simplifies analytical querying and enhances performance, offering a flexible and efficient foundation for data analysis across industries.

Gathering initial requirements to kickstart analytical data modeling journey

To ensure you can define clear analytical requirements, it's recommended to follow the following steps to successfully kickstart an analytical data modeling project:

Identify key business processes

1. Engage with stakeholders - Gather input from relevant teams to identify business needs and expectations for insights.
2. Understand the business context - Grasp the strategic goals, key processes, and challenges to align the data model with business objectives.
3. Map out core processes - Visualize the business process flows to understand data generation and its role in key activities.

Define analytical requirements

1. Determine the scope of analysis - Define the key business questions, data timeframes, and dimensions required for analysis.
2. Identify data sources - List internal and external data sources that will feed into the analytical model.
3. Data quality assessment - Ensure the data is complete, accurate, consistent, and timely for analysis.
4. Define key metrics and dimensions - Identify important metrics and dimensions to slice-and-dice insights in data visualizations.

Evaluating and prioritizing business processes

It's recommended to prioritize low-hanging fruit (high-impact, low-risk processes) to build momentum with quick wins. Focus on processes that boost revenue, efficiency, or customer satisfaction (impact), while considering challenges like data availability, complexity, and potential technical limitations (risk).

Tips & tricks when creating analytical data models

Following these practices will help create robust and efficient data models that support effective decision-making and insights:

Designing an Analytical Data Model

• Keep it simple - Simplify your model to avoid complexity and ease maintenance.
• Handle multi-tenant data - Use logical or physical separation to manage data for multiple tenants securely.
• Use locally uploaded mock data for quick prototyping - Prototype with mock data to refine your model before using live data.
• Regularly validate with stakeholders - Engage stakeholders to ensure the model meets business needs and adjust as required.
• Establish naming conventions and ensure consistent data types - Use clear naming and consistent data types to improve readability and integration.
• Document your fact and dimension tables - Document tables thoroughly to support understanding and maintenance.

Implementing the Data Model

• Use version control - Track changes and collaborate effectively with version control systems.
• Configure dataset schema updates - Ensure that changes to columns in existing datasets are appropriately reflected in Luzmo.
• Incorporate security and access control measures - Protect data to ensure privacy and security.
• Optimize for query performance - Enhance performance by indexing, partitioning, and denormalizing to handle large datasets efficiently.

Next steps

Improving the consumption of analytical data models

To streamline data consumption and enhance the user experience of (less technical) business users when building insightful data visualizations, consider the following strategies to optimize your data in Luzmo:

• Denormalized Views: Flatten star schemas to simplify data access, reducing the need for complex joins. Create these views in your data source or with Luzmo’s SQL datasets.
• Dataset Fine-tuning: Adjust column settings in Luzmo for better usability, including date-time formatting, currency, and multilingual support.
• Predefined Formulas: Implement complex business logic inside aggregation formulas to ensure consistent metrics and reduce user errors.
• Share Datasets: Make datasets accessible to relevant users for effective querying.
• Query Caching: Improve performance by caching frequently accessed data, balancing freshness with speed.
• WARP Synchronization: Use WARP to sync data periodically for efficient handling of large analytical loads.

Maintaining your analytical data model(s)

Regular updates and optimization are essential for keeping your data models effective and aligned with business goals. Some recommended points to keep in mind:

• Extending your data models - Adapt to changing business needs by gradually adding new elements and managing changes with version control.
• Regularly audit your data models - Periodically review models to ensure alignment with current processes, performance, and data accuracy.
• Performance optimization - Enhance model efficiency by indexing, partitioning, and aggregating data to handle larger volumes.
• Data quality assurance - Maintain data integrity through automated validation, regular cleaning, and error monitoring.
• Keep documentation up-to-date - Keep documentation up-to-date to reflect model changes and ensure clarity for all stakeholders.
• Security and Compliance - Ensure adhering to data privacy and security at any point in time.

By now, you should have a solid understanding of how to effectively design analytical data models and keep them aligned with your business requirements. From leveraging Star schemas for efficient analysis to implementing best practices for maintenance and optimization, these strategies will ensure your data models remain robust and insightful to continue driving informed decision-making!