Tips & tricks when creating analytical data models

5:00 min

Medium

Creating analytical data models is a foundational step in leveraging data to drive business insights and decision-making. While the process can be complex, employing the right strategies and best practices can streamline your efforts and enhance the quality of your models. This article outlines essential tips and tricks to guide you in designing robust and efficient analytical data models using Luzmo, which have been split into two main categories:

Designing an analytical data model

Implementing the data model

Designing an analytical data model

Keep it simple

Strive for simplicity in your data model design.

Complex models can be difficult to understand, maintain, and scale. Aim for a straightforward structure that accurately represents business processes without unnecessary complexity. A simple data model to start has the chance to stay (more or less) simple over time, while an initially complex data model will surely become more complex!

Handle multi-tenant data

To ensure your customers can only access their own data, you must ensure that the data is logically or physically separated based on tenant identifiers. This can be done using:

A column in each of your tables to identify which rows are generated by which tenant (i.e. one large table with multiple tenants' data inside).
Store each of your tenants data on a separate table / schema / database / etc.
- This has the additional advantage that tenant data is physically isolated from each other, but comes with the cost of requiring additional data pipelines in case you'd like to gain (internal) insights across multiple tenants or calculate company-wide benchmarking aggregations.

Luzmo supports both multi-tenant data structures using Parameterized embed filters to securely filter embedded dashboards to a single tenants' data, and / or Account overrides to securely point the embedded dashboards' queries to a specific tenants' data source. If you require more custom logic to query a specific tenant's data, a custom Plugin might be required (see e.g. this Academy article)

Use locally uploaded mock data for quick prototyping

Use spreadsheets to prototype and test your data model before integrating with live data sources.

Creating mock-up data allows you to experiment with different structures and relationships without the risk of affecting production data. By locally uploading your mock data into Luzmo, you can quickly iterate on the data structure, validate assumptions, and refine your model design. This approach facilitates rapid experimentation and helps identify potential issues early in the modeling process.

Regularly validate with stakeholders

Engage with business users, analysts, and other stakeholders throughout the modeling process to ensure the model meets their needs. Gather feedback on the structure, available metrics, and dimensions to make necessary adjustments. Continuous validation helps align the data model with business requirements and increases user adoption. This is typically something that is still performed periodically after launching your first analytical data model, as insights should be carefully aligned to ever-changing business requirements to provide the most value at any point in time!

Establish naming conventions and ensure consistent data types

Consistent naming conventions improve the readability and maintainability of your data models. Descriptive names help team members understand the purpose of each table and column, reducing confusion and errors. For example, use clear suffixes to indicate the type of data (e.g., _dim for dimension tables, _fact for fact tables) and avoid ambiguous abbreviations.
Consistent data types prevent errors and facilitate seamless data integration. Ensure that related columns in different tables use the same data type and format, which aids in maintaining data integrity and reduces the need for data transformations during analysis.

Document your fact and dimension tables

Comprehensive documentation is crucial for ensuring that everyone involved understands the structure and purpose of the data model. Include names, descriptions, sample values, and important aspects like slowly changing dimensions (e.g. how changing customer addresses are handled by the data model). Well-documented models facilitate onboarding new team members, support ongoing maintenance, and enhance collaboration across departments.

Implementing the data model

Use version control

Version control systems like Git allow you to track changes, collaborate effectively, and revert to previous versions of your data models if needed. Maintaining versions of your data models ensures that you can manage updates systematically, avoid conflicts, and maintain a history of modifications for auditing and troubleshooting purposes.

Incorporate security and access control measures

Protect sensitive data by implementing role-based access controls and data encryption where necessary. Define who can access, modify, and view different parts of the data model to ensure compliance with data privacy regulations and safeguard against unauthorized access. Proper security measures build trust and protect your organization’s data assets.

Optimize for query performance

Consider how users will query the data, and optimize the model to support those queries efficiently. This might involve indexing key columns, partitioning, denormalizing certain tables to reduce join complexity (remember that when it comes to analytical data models, the cost of storage is far cheaper than the cost of slow customer-facing analytics), etc. Performance tuning ensures that your data model can handle large datasets and complex analyses without significant delays.

Designing effective analytical data models requires a combination of best practices, strategic planning, and attention to detail. By following these tips and tricks—ranging from using mock-up data and establishing naming conventions to optimizing for performance and ensuring scalability—you can create robust data models that provide valuable insights and support informed decision-making. Once you've created your first data model, we'd strongly recommend diving into this article around further facilitating the consumption of your analytical data!