Havering a very clear vision for what you plan to do with the data is a useful cue, to hold in mind, while you go through a data mining project. This helps you define relevant selection criteria for that data you collect and set you up for success in every downstream step of the process.

When clear rules are established, along with objective documentation that addresses potential questions, the likelihood of quality loss during the stages of data mining is significantly reduced. In my opinion, this is a conceptual issue. Projects designed without proper standardization and clear, well-defined rules allow data to move through stages with a decline in quality, ultimately compromising the final outcomes.

Mantener la calidad de los datos a lo largo de un proyecto de minería de datos es crucial para obtener resultados confiables. Para asegurarme de que los datos sean consistentes y precisos, suelo empezar implementando reglas de validación desde el primer punto de entrada. Esto ayuda a detectar y corregir errores rápidamente, antes de que se acumulen. La limpieza y estandarización de datos es otra práctica clave. Dedico tiempo a depurar datos, eliminando duplicados y garantizando que todo esté en el mismo formato, lo cual facilita su análisis posterior.

You will have to implement a comprehensive approach that includes data validation, cleaning, normalization, and continuous monitoring, with a focus on accuracy, completeness, and consistency.

To maintain quality: Define goals clearly. Use reliable data. Clean and analyze the data. Select important features. Choose and test models. Validate results. Deploy and monitor regularly. Stay consistent and review at every step.