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Data staleness can be defined as the period when the data in a system or a database is no longer accurate, up-to-date, or relevant. This could be due to various reasons like changes in the real-world entities or delays in data updates, which make the information outdated.

There are multiple factors that could cause data to become stale. Some of them include: 1. Data updates not being performed regularly or timely: If the data is not updated regularly, it might not reflect the current state of the real-world entities it represents, making it stale. 2. Inaccurate or incomplete data sources: If the data sources providing the information are inaccurate or incomplete, data can become stale as it will not accurately represent the real-world entities. 3. System or network failures: Failures in the system or network could result in data not being updated or processed correctly, leading to stale data. 4. Inconsistent data updates: When different parts of the data are updated at different rates or times, some parts of the data might become stale. 5. Human errors: When data is inputted or updated manually, errors might occur, leading to stale data.

To prevent data from becoming stale, consider the following steps: 1. Regular and timely data updates: Ensure that data is updated regularly and as soon as changes occur in the real-world entities it represents. 2. Reliable data sources: Verify the accuracy and completeness of the data sources being used. 3. Robust system and network infrastructure: Implement a robust and fault-tolerant system and network infrastructure to minimize the chances of failures affecting the data. 4. Consistency in data updates: Ensure consistency in the timing and frequency of data updates across all aspects of the data. 5. Minimize human errors: Implement proper data entry and update procedures, and provide training to those responsible for data input and updates to minimize errors. By understanding the causes of data staleness and taking preventive measures, it becomes easier to ensure the accuracy and relevancy of the data in a system or database.