Maintaining data accuracy in big data poses several challenges, including data volume, velocity, and variety. The sheer amount of data collected can lead to inconsistencies, especially when integrating multiple data sources. Moreover, the rapid speed at which data is generated complicates timely validation processes. Additionally, varying formats and standards across sources can introduce errors, making it essential to implement robust data governance measures and automated data cleaning techniques to ensure ongoing accuracy.