How to handle duplicate or invalid data in monitoring tools?

When duplicate or invalid data appears in monitoring tools, it usually needs to be handled through a combination of data cleaning, rule optimization, and source control to ensure the reliability of data analysis. Data cleaning: Prioritize identifying and removing duplicate records through unique identifiers (such as user IDs, timestamps); for invalid data (such as outliers, format errors, null values), filtering rules (such as numerical range verification, format validation) can be set to eliminate or mark them. Rule optimization: Check data collection rules to avoid duplicate data caused by repeated crawler crawling or sensor reporting; for invalid data, preset thresholds (such as excluding traffic data below a reasonable range) or logical verification (such as filtering non-target user behaviors) in the tool. Source control: Investigate the data generation process, such as fixing collection script vulnerabilities and calibrating sensor accuracy, to reduce the generation of duplicate or invalid data from the source. It is recommended to audit data quality regularly (e.g., weekly) and use automated tools (such as Excel deduplication functions, Python scripts) to improve processing efficiency; if the data volume is large, professional data governance tools can be considered to assist in optimizing data accuracy.