In the era of the digital economy, the accumulation of vast amounts of data and the concentration of high-value data provide the foundational elements for the intelligent transformation of the economy and society. Data power—built on the elements of data, algorithms, and computing power—leverages digital platforms and environments, advanced and precise algorithms, and robust computing power to fully harness big data resources. It can empower the development of China’s new quality productive forces by effectively facilitating the amplification of innovation momentum, synergy among innovation factors, resource allocation optimization, and supply-demand matching.

Theoretical logic

Today, data has become the resource foundation for cultivating new quality productive forces, and the rapid digitalization reshaping industries and production stages now supplies an increasingly abundant base for the intelligent integration of production processes. As the digital economy continues to expand, the volume of data elements is growing exponentially. According to the theory of resource endowment, when factor prices are equalized and technological levels are comparable, an economy’s competitive advantage is established based on its own production factor endowment.

Algorithms now function as a pivotal engine for optimizing the allocation of production factors. Joseph Schumpeter’s theory of innovation argues that the reorganization of factors and optimization of resource allocation prompted by technological innovation help break established market equilibrium, creating new market order and economic growth drivers. New quality productive forces rely on advanced algorithms to accelerate the realization of intelligent attributes, substantially improving the efficiency of allocating traditional tangible production factors such as labor and capital, as well as emerging intangible production factors like data and technology.

Computing power provides the technological backbone that sustains the integrated development of “data–algorithms.” Innovation diffusion theory posits that the diffusion, promotion, and application of technological innovations depend on their feasibility and effectiveness. Through large-scale data processing and high-performance computing capabilities, computing power effectively enhances the potential of intelligent technologies to empower industrial technological innovation.

Empowering mechanism

With data constituting a new means of production, algorithms functioning as new production tools, and computing power operating as new production technology, data power is becoming a core element driving the development of new quality productive forces.

First, data power enables the amplification of innovation momentum for the development of new quality productive forces. Its rapid iteration in technology and business models nurtures the emergence of cutting-edge technological innovations, allowing innovators to maintain sensitivity and foresight.

Second, data power enables synergy among innovation factors for the development of new quality productive forces. Under the constraints of innovation complexity and limited resources, synergy among innovation factors brings together the advantages of various innovation resources. The diversity and dynamism of innovation factors make the innovation process unpredictable—a challenge that the integrated value of data, algorithms, and computing power helps overcome by enabling this synergy.

Third, data power enables the optimization of resource allocation for the development of new quality productive forces. The new production relations that align with the development of new quality productive forces place more emphasis on the free and smooth flow of resources. The fusion of “data + algorithms + computing power” allows innovators to better manage and allocate innovation resources.

Fourth, data power enables the matching of supply and demand for the development of new quality productive forces. The development of data power can help both sides better understand market dynamics, avoid delays and misalignments, and achieve dynamic matching.

Ye Meilan (president), Liu Bei, and Zhu Weiwei (professor) are from Nanjing University of Posts and Telecommunications. This article has been edited and excerpted from Jianghai Academic Journal, Issue 1, 2025.