A significant challenge faced by data scientists today is dealing with data quality and data integration. High-quality data is essential for generating accurate models and insights, but data can often be incomplete, inconsistent, or biased. Furthermore, data sources may be siloed across different systems, making it difficult to integrate and analyze efficiently. Data scientists must invest time in cleaning, organizing, and reconciling datasets to ensure robust analyses. Addressing these challenges is crucial for deriving meaningful insights and making informed decisions based on data.