The full-coverage orientation means that in big data auditing, we insist on comprehensive data collection and ensure the completeness of data collection. This means that we need to accurately understand and y grasp the connotation of full coverage. First of all, on the basis of collecting internal data and information on the financial, operational and affiliated organizations of the audited unit, it is necessary to broaden the ability of cross-border thinking and actively adopt a cross-regional, cross-industry, cross-system and cross-departmental approach to collect external related data and information extensively. The purpose of doing so is to ensure the comprehensiveness of data collection and avoid data blind spots. Promoting full coverage of data collection with all efforts can effectively improve the accuracy and reliability of audits and ensure that nothing is omitted or lost in the process of collecting, managing and utilizing the data of the audited unit's finances, operations and affiliated units. Through the full-coverage oriented analysis idea, the big data audit can understand the situation of the audited unit more comprehensively and provide a strong basis for the audit results, thus enhancing the effectiveness and value of the audit.