Data quality refers to data that is suitable for its intended use and context. It involves measuring and improving attributes like accuracy, timeliness, completeness, and provenance. Ensuring data quality is crucial for digital twins, as the effectiveness of a digital twin is directly linked to the quality of the data input into it.

To enhance data quality, systematic data cleansing processes are recommended to format and structure data for modeling purposes. Additionally, data completeness and accuracy are key Key Performance Indicators (KPIs) for data sharing and collaboration efforts in projects. Overall, data quality is fundamental for reliable and effective data-driven decision-making processes.