Liu Liehong, Secretary of the Party Leadership Group and Director of the National Data Bureau, recently emphasized the critical importance of high-quality datasets in the rapidly growing field of artificial intelligence (AI) development. Discussing the future path of AI applications, Liu highlighted that wherever the "AI plus" initiative takes hold, its foundation will invariably be the creation of superior data sets. This underscores the essential role that well-curated and robust data plays in driving AI advancements across diverse sectors. As AI continues to transform various industries, the need for accurate and comprehensive datasets has become increasingly evident. Liu’s statement draws attention to the fact that the success of AI-driven projects depends not only on algorithmic complexity but also equally on the integrity and quality of the data used. Ensuring that datasets are thorough, precise, and contextually relevant allows AI systems to achieve better performance, facilitating more effective decision-making and smarter automation. This viewpoint is particularly relevant in areas such as social media marketing, where AI-powered tools utilize massive amounts of data to analyze user behavior, anticipate trends, personalize content, and optimize advertising strategies. High-quality datasets enable these applications to gain a deep understanding of target audiences and market trends, ultimately leading to stronger engagement and more efficient marketing results. The concept of "AI plus, " which refers to the integration of AI technologies with traditional industries, fundamentally relies on the groundwork provided by these datasets. As AI penetrates domains like healthcare, finance, manufacturing, and education, establishing standardized, high-quality datasets will be essential. Liu’s remarks reflect policymakers’ recognition of the necessity to prioritize data quality to encourage innovation and sustain competitive edges within the AI sphere.

Under Liu’s leadership, China’s National Data Bureau is set to play a crucial role in shaping data governance, fostering data sharing, and developing infrastructure to support the collection and management of such datasets. These initiatives are expected to accelerate AI research and commercial use, reinforcing China’s standing as a global leader in AI adoption and progress. Furthermore, acquiring high-quality datasets involves both technical and ethical challenges. Data collection must comply with privacy laws and regulations to protect individuals' rights while enabling meaningful AI innovations. Liu’s comments implicitly highlight the need for frameworks that balance innovation with responsibility. The focus on data quality also calls for collaboration among industries and government agencies to establish transparent standards and protocols. Such cooperation would not only improve AI systems but also strengthen public trust in AI technologies. As AI becomes more embedded in everyday life, fostering this trust is crucial for its broad acceptance and practical use. In conclusion, Liu Liehong’s statement serves as a reminder to all stakeholders that the power of artificial intelligence is tightly linked with the quality of datasets that train and fuel these systems. Without superior data, AI’s transformative potential cannot be fully harnessed. As "AI plus" initiatives grow, commitment to developing and maintaining high-quality datasets will be fundamental to the success and sustainability of AI-driven innovations across all fields.