Robust legal and regulatory systems provide institutional support for big data applications. Tax authorities should be granted full rights to collect and access tax-related information, with clearly defined scopes of data collection and usage. Additionally, legal frameworks should mandate that information providers submit accurate tax-related data, allowing tax authorities to fully understand taxpayer's business operations, reduce information asymmetry, enhance data analysis, and strengthen tax administration.

The primary task of tax data quality management is standardization. Defining unified and standardized metadata—achieving the principle of "same track for vehicles, same script for documents" in the big data era—forms the foundation for tax information interoperability, exchange, centralization, and sharing, as well as for data analysis and mining. By improving the data asset management system, strengthening data quality audits, and establishing a chain-based management of tax administration data, the overall quality of tax system business data can be gradually enhanced.

To leverage big data effectively, tax authorities should expand data collection, integrating tax-related information from multiple departments such as commerce, land, national taxation, and social security. Big data technologies can establish a unified underlying data storage and processing center, enabling batch collection, centralized storage, intelligent analysis, and categorized applications. This allows seamless integration of third-party tax information with tax risk management systems, providing comprehensive services such as real-time tax monitoring, policy compliance supervision, tax forecasting, and risk early warning.

Utilizing data mining and machine learning, multi-dimensional taxpayer characteristics can be summarized and integrated, creating comprehensive profiles of taxpayers and their tax-related behavior, thereby reinforcing supervision. By building a big data and AI platform, a full-chain risk management system can be established. Machine learning algorithms can assess potential risks of invoice fraud, shifting risk management upstream and enabling precise detection and prevention of fraudulent tax behavior.

Tax authorities should utilize big data, internet, and cloud computing technologies to develop a secure tax information system. By creating a rational data analysis index system and conducting industry-specific risk assessments with corresponding risk models, taxpayer-reported data can be cross-checked against administrative systems to mitigate risks at the source. Staff should receive specialized training on tax information security, supported by management regulations and technical safeguards to ensure the protection of tax-related data.