To address data quality issues on your BI dashboard, start by identifying root causes, such as inconsistent data sources or errors in ETL processes. Collaborate with data owners to establish clear standards and implement validation checks at each stage. Regularly monitor data quality metrics and automate alerts for anomalies. Simplify data pipelines to reduce errors and ensure clear documentation. Engage stakeholders to prioritize fixes, aligning efforts with business objectives. Continuous improvement and proactive communication will restore dashboard performance and trust.

Para resolver problemas de qualidade nos painéis de BI, é essencial focar em três pilares: governança de dados, validando a precisão e confiabilidade das fontes; implementação de regras rigorosas para tratar inconsistências e automatização de processos com ferramentas como SQL e Power Query. Esses passos garantem dados confiáveis e dashboards eficientes para uma análise estratégica.

Turning around data quality issues on a BI dashboard starts with identifying root causes—whether in source systems, ETL processes, or data modeling. Implement automated validation checks to catch errors early and maintain consistent updates. Collaborate with stakeholders to ensure the data aligns with business needs. Regularly review and optimize data pipelines for accuracy and efficiency. A proactive approach transforms data quality challenges into opportunities for improved performance and trust.

Algo que sempre procuro fazer é a sanitização dos dados, hoje com auxílio de IA fica ainda mais fácil de encontrar padrões de inconsciência e limpar os dados no ETL, eu acho ainda essencial mudar a origem caso seja possível, o difícil é convencer o dono do dado a fazer isso, por isso muitas vezes o dado corrigido fica disponível apenas no BI!

In the beginning, frequently check and validate from source data summary (i.e excel data) vs result table, visual chart in PBI. Check for abnormality and trouble shoot, find rootcause and solve..

-Create data quality checkpoints in your ETL process . -Ensure that all quality checks are being executed when creating data integration utilities . -Keep an eye out for any red flags(unusual spikes or drops) that might suggest that the data might be incorrect and take necessary corrective actions to deal with those quality issues. -Always have a robust regeneration/refill option in your ETL piplelines so you can refresh the data with the correct rules once the issue is fixed

Segue algumas estratégias que são essenciais: - Confiabilidade de dados; - Dados bem estruturados, seguindo regras validação, consistência e integridade; - Fontes seguras; - Ter políticas que garantam regras claras da cultura de segurança, qualidade e responsabilidade quanto aos dados gerados e geridos na empresa.

Continuous improvement- 1. Regularly review data quality metrics; Monitor progress and identify areas for improvement. 2. Iterate and refine processes; Continuously update and refine data quality management processes. 3. Encourage feedback;Foster a culture of continuous feedback and improvement.

I always encourage our clients to proactively address data governance head on. Building data governance dashboards, setting up anomaly detection alerts and even forming a data governance task force can all be game changers! It’s also important to communicate with end users about issues you find and resolution ETA. This helps to build and maintain data trust so you don’t run into adoption issues down the road.