The frequency of online lending big data cleanup depends on the specific platform and data processing process. In general, here are some factors that may affect the frequency of online lending big data cleansing:

1. Data volume: if the online lending platform generates a large amount of data every day, the cleansing frequency may be more frequent to ensure the efficiency and accuracy of the system.

2. Data importance: some data may be more important than others and require more frequent cleansing and updating. This can be determined based on business needs and criteria set for decision making.

3. Data quality: If there are issues with the quality of the data on the online lending platform, such as duplicate, incorrect or outdated data, then the frequency of cleansing may increase to ensure the accuracy and reliability of the data.

4. Legal and regulatory requirements: Depending on the relevant laws and regulations in China, online lending platforms may need to comply with specific data protection and privacy policies, which may affect the frequency of purging.

Summary: The frequency of online lending big data cleansing depends on factors such as the volume of data, data importance, data quality, and legal and regulatory requirements. Different platforms may have different cleansing strategies aimed at ensuring data accuracy and reliability.

Extended information:

Big data cleansing is a complex and critical task that involves multiple aspects of data collection, storage, cleansing, and analysis. A reasonable cleaning strategy can improve the quality and value of data and provide a reliable basis for decision-making. In addition, with the continuous development of technology, automated cleansing tools and algorithms are becoming more and more mature, which helps to improve the efficiency and accuracy of big data cleansing.