Here's how you can maintain or ensure data accuracy: Implement data quality frameworks. Regular data audits. Automated validation checks. Training and education. Feedback mechanisms. Data source verification. Use data cleansing tools. Maintain documentation.

Those are excellent strategies for maintaining data accuracy! Validating data sources and ensuring a culture of accountability are key to keeping the integrity of your analytics intact. I’d add a few practices to that mix as well. One of my go-to strategies is using automated data validation tools. These tools can help flag inconsistencies or outliers in the data, so you don’t have to manually comb through everything. Additionally, I prioritize regular audits of the data—especially when new data streams or systems are introduced. It’s a good way to catch any gaps or shifts in quality early on. Another approach is to establish clear guidelines for data entry and consistency. When everyone is aligned on formatting, units of measurement,

Strategies for improving data quality Establish data governance policies. Offer data quality training. Keep documentation accurate and up-to-date. Implement data validation techniques. Implement feedback loops. Use data cleansing tools. Monitor data quality metrics.

Con una solución de business inteligente que automatice la recogida de datos, evitando errores humanos o poco fiables. Plataformas como Tableau disponen además de una funcionalidad que te permite limpiar y homogeneizar los datos previamente.