Optimising a BI estate isn’t just about improving report speed, although that’s a valuable outcome. It’s also about creating a robust, efficient, scalable reporting ecosystem that can grow alongside your organisation. More than ever, Business Intelligence performance optimisation is crucial.
Whether you’re just embarking on your BI journey or managing a mature analytics ecosystem, we’ve found by working with our clients that there are key strategies that can optimise portfolios and ensure that our solutions create and maintain value, whatever their tech stack.
Building and maintaining scalable BI performance
1. Streamline your data model
Make sure that the type and amount of data you use is appropriate for the purpose. While including only necessary data for specific reporting goals is crucial, it’s equally important to establish and adhere to design standards. These standards should guide when and how to add data. A robust data governance framework is essential to prevent ad-hoc additions leading to bloated, non-performant models. When faced with tight timelines or source data limitations, opting for quick, tactical solutions to existing models is tempting. However, while appearing to solve immediate problems, this approach often results in long-term performance issues and increased technical debt.
Instead, focus on regular model reviews and optimisation, establishing transparent processes for data addition requests, and maintaining thorough documentation of data lineage and usage. This proactive approach ensures that your data model remains lean and efficient over time. Leverage server-side optimisations like custom SQL for pre-filtering and proper indexing. Aggregate data to reduce volume while still meeting analytical needs, but always within the framework of your established design standards.
Implement efficient data refresh strategies. Depending on your BI tool’s capabilities, use features such as data extracts or incremental refreshes for large datasets. These approaches reduce the amount of data processed during refreshes, improving performance for complex data sets. Additionally, make use of data modelling features. Always consider the performance implications when adding complexity to your model, ensuring that each addition provides significant value to justify its impact on overall system performance.
2. Optimise calculations
Simplify complex expressions by breaking them down into smaller, more manageable parts. Create intermediate calculations to build up to more complex ones gradually. This modular approach not only improves readability and eases troubleshooting but often enhances overall performance. By breaking calculations into logical steps, you allow the BI tool to optimise each component, leading to more efficient query execution.
Use appropriate calculation types based on your specific BI tool’s capabilities. Most modern BI platforms offer various methods for performing calculations, each optimised for different scenarios. Understand the distinctions between row-level calculations, aggregations, and more complex analytical expressions within your chosen tool. For instance, in Power BI, leverage DAX measures for dynamic, context-aware calculations that respond to user interactions, and M and SQL for fixed dimensions. Choose the most suitable method for each task to optimise performance and ensure accurate results.
Be mindful of calculation context. Pay close attention to how your calculations interact with filters and dimensions in your reports. Understanding the scope and context of each calculation is crucial for maintaining consistency and performance across your dashboards. In Tableau, for example, use Level of Detail (LOD) expressions to control the granularity of your calculations independently of the view’s level of detail. Proper context management can significantly impact query performance and ensure that your calculations produce the expected results in all scenarios.
3. Design efficient visualisations
Prioritise quality over quantity in your dashboard design. Focus on critical metrics and aim for a clean, uncluttered layout. Remember that each visual adds processing time, so include only those that provide meaningful insights. Combine visuals where appropriate to streamline your designs, resulting in faster loading times and more precise insights. This approach enhances performance and improves user comprehension and engagement with your BI solution.
Optimise filtering and interactions to reduce the amount of data being processed. Implement efficient filtering strategies that allow users to quickly narrow down data to relevant information without overloading the system. Consider the performance implications of cross-filtering and synchronised selections across multiple visuals. In Power BI, for instance, be cautious with bi-directional relationships, as they can significantly impact query performance and accuracy if not used correctly. Strive for a balance between interactivity and performance to ensure a smooth user experience.
Leverage advanced features appropriately to enhance user experience without compromising performance. Many BI tools offer functionalities that can provide rich, interactive experiences while minimising initial load times. For example, in Tableau, utilise dashboard actions to create guided analytics experiences, allowing users to drill down into details without loading unnecessary data upfront. Similarly, consider using features like parameterised queries or incremental loading to manage large datasets efficiently.
Elevating BI performance: A strategic imperative
Optimising a BI estate isn’t just a technical exercise; it’s a strategic initiative that positions organisations for data-driven success, regardless of where they are in their BI journey. For organisations just starting their BI implementation, these principles provide a robust framework to build upon, helping to avoid common pitfalls and ensure scalability from day one. For those with established BI solutions experiencing performance issues, such as slow reports or decreased user engagement, applying these optimisation strategies can revitalise existing systems and unlock new potential.
The benefits of a well-designed and optimised BI environment extend far beyond mere performance improvements. Organisations can expect increased user engagement, higher return on investment, and improved data literacy. As users interact with faster, more responsive dashboards, they will uncover more insights and develop greater trust in the data. This leads to better decision-making, a more data-driven culture, and improved business outcomes.
Whether you’re laying the groundwork for a new BI implementation or refining an existing one, these strategies provide a clear path to maximising the value of your data and analytics investments across platforms like Tableau, Power BI, or any other analytics tool.
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