All suggestions by other contributors are great. Another option is to base the dashboard on a materialized view that summarizes data and is being refreshed outside of the dashboard tool on predefined intervals. This is an excellent option to reduce online data processing and to increase dashboard performance for the end users.

Slow dashboards frustrating your users? Let's fix that without compromising on insights! First, optimize your data model. Simplify complex calculations and use pre-aggregated data where possible. This reduces processing time dramatically. Next, implement caching strategies. Store frequently accessed data in memory for quick retrieval. Consider using asynchronous loading techniques. Don't overlook the basics: compress images, minimize CSS/JS, and use efficient queries. Lastly, educate users on best practices. Remember, a fast dashboard isn't just about speed—it's about delivering value efficiently. How will you balance performance and functionality in your next BI project?

There are countless things you can do prior to even thinking of sacrificing user experience. Start by applying Roche's Maxim: Does this metric really need to be calculated ad-hoc? Push computations upstream where possible and pre-aggregate data to the granularity users actually need. Only import the necessary data and avoid overloading with unused details. Optimize your data model using best practices like the star schema, which improves performance in tools like Power BI. These steps alone often address performance issues without sacrificing user experience. There’s much more to explore, but these foundational steps are a strong starting point.

Por ejemplo, en Power BI podemos aplicar diferentes estrategias: ✔️Plegado de consultas: Configurar Power BI para que el procesamiento de datos se realice en la base de datos de origen. Esto reduce la carga en el motor de análisis y acelera la carga de datos. ✔️Optimización del ETL: Eliminar columnas innecesarias y reducir el tamaño de los datos. Utilizar índices para acelerar la extracción y carga de datos. ✔️Modelado de datos eficiente: Diseñar un modelo optimizado en esquema estrella, evitar relaciones innecesarias. Priorizar medidas en lugar de columnas calculadas. No solo vas a mejorar la velocidad de carga y la fluidez de interacción del panel, sino que también va a ser más escalable para manejar grandes volúmenes de datos.

A BI dashboard should facilitate decision-making, not delay it. Simplicity is key. By removing unnecessary elements, we focus on data that truly matters. With optimized SQL queries and caching, speed is ensured, while regular reviews guarantee the dashboard's continuous improvement. For me, every detail, from design to functionality, must serve analysis. The goal is immediacy and efficiency without unnecessary delays. A well-designed dashboard is a decision-making tool, not an obstacle.

A sluggish dashboard widget can slow down the entire dashboard. Review each dashboard widget’s performance individually, and consider moving out the slow ones to a different dashboard.

Man sollte so viele Berechnungen wie möglich direkt in der Datenquelle vornehmen und nicht durch berechnete Felder im Dashboard. Das hat uns immer sehr geholfen.

Faço isso a muitos anos. Otimizar a performance é uma arte em cada tecnologia. Em bancos de dados sql, que são os mais comuns, o problema é na modelagem de dados que é pensada para oltp e não para olap onde os painéis de bi devem se apoiar. Então aqui vai o grande segredo de melhorar a performance de qualquer coisa. Reduza o número de dados. Pense em balde de agua. Pegar ele cheio é pesado. Como fazer ele ficar mais leve? Retire água dele. O mesmo princípio se aplica a dados. Quer mais performance reduza os dados. Para dashboards crie uma arquitetura triangular de etl que gere automaticamente dados agregados conforme sai da base até o topo do triângulo. Dados detalhados na base com tabelas e indicadores pré calculados no topo.