When discussing the global expansion of Chinese automobiles, the industry tends to focus on complete vehicles, production capacity, and traditional supply chains. However, according to Chu Huabin, General Manager of Kintarui (China) Information Technology Co., Ltd., these elements are no longer sufficient in the era of intelligentization. What truly determines whether Chinese automakers can maintain their global leadership is another long-overlooked supply chain—data.
At the Conference on Innovative Development Models for the Global Expansion of the Automotive Industry, co-hosted by the China EV100 Research Institute and Hainan Qinglan Zhilian Technology Co., Ltd., Chu stated that Chinese enterprises are transitioning from product export to systemic global expansion, moving from selling cars globally to operating globally. In the past, globalization was supported by components and logistics; at the intelligent stage, the data link must be added.
Global operation implies that some work is completed locally while other parts return to China. Cross-border data flow is essential for R&D data backflow and after-sales data integration.
Data Becomes the Second Supply Chain
Chu defines this round of Chinese enterprises' global expansion as "systemic expansion," with the core shift being from selling cars globally to operating globally. Companies can no longer treat China as the base for all affairs and simply export complete vehicles worldwide; this model is unsustainable. A portion of R&D, operations, and services must be carried out locally, while another portion returns to China for collaboration. He summarizes this approach as "in China for Global."
In this global division of labor, data holds a position equal to that of traditional goods. Whether it is overseas R&D data backflow or the accumulation of maintenance and after-sales data, the core issues are related to data. His judgment is that the product supply chain determines whether cars can go abroad, while the data supply chain determines whether Chinese enterprises can sustain their global leadership.
Constraints on cross-border data flows are rapidly tightening. Europe passed GDPR in 2016, which came into full effect in 2018, and Southeast Asia and South America have also gradually established their own data protection rules. As we enter the intelligent era, the value of data increases, and national regulations on data outflow become more detailed. Enterprises need to establish comprehensive cross-border management mechanisms regardless of whether data is leaving or entering the country.
A new energy vehicle manufacturer conducting autonomous driving road tests in multiple cities in Southeast Asia vividly exposes this challenge. The local environment is typical of tropical testing conditions, with frequent and intense showers, buses and motorcycles crossing roads at any moment, and right-hand drive scenarios. This generates hundreds of GBs of street view imagery and point cloud data daily, along with algorithm decision logs.
This data constitutes the core asset for optimizing autonomous driving models for overseas road conditions. Corporate R&D departments generally require backflow, and only safe, timely, and compliant transmission back to China can support model iteration.
The difficulties lie at both ends. One end is transmission: large volumes of data returning to China require international dedicated lines. The other end is compliance: images contain pedestrian faces, license plates, and protected personal privacy information. Original footage cannot leave the country under local laws; irreversible masking and tokenization of license plates must be completed locally first. Every step of data transmission must comply with regulations in various countries and domestically, with even minor errors potentially triggering regulatory red lines.
Many companies report cumbersome processes and high penalty risks, opting instead to process data locally. While this avoids compliance risks, it significantly reduces the efficiency of R&D iteration.
Transforming Scattered Data Compliance into Reusable Public Services
From an industry perspective, cross-border data has become a universal burden because most enterprises are repeatedly solving the same set of problems independently. Data collectors, dedicated line operators, law firms, and platforms need to be coordinated separately. Each time a company enters a new country, the entire chain must be re-verified. Companies often have to maintain sizable data compliance teams, but experience and methods are difficult to reuse across scenarios and countries.
The solution required by the industry is to transform one-off, scattered data transmissions into a reusable service system. First is mode-level reuse: providing a unified entry point and a single contract covering the entire chain to achieve end-to-end one-stop completion. Once a scenario and a country are validated, the accumulated compliance methodology, transmission infrastructure, and service system can be replicated horizontally, significantly reducing redundant investment.
Second is process-level compliance pre-positioning: Data undergoes preprocessing at local nodes first. Faces and license plates receive irreversible blurring and tokenization. Point clouds are stripped, and time indices are extracted. Each piece of data passes through a compliance gate check before being released. Processed data enters China via low-latency channels. Data usage, access duration, and call permissions are all written into digital contracts, ensuring controlled use, non-proliferation of raw data, and complete auditing trails for every access and transfer.
Third is rule-level service support: Specialized systems organize data regulations from major countries worldwide, outputting preliminary legal screening, application material lists, and key risk points. Suitable transmission paths for business adaptation are determined first, then handed over to professional teams for assessment, reducing the cost for enterprises studying each country's rules from scratch. Connecting these links allows single road test data backflow to achieve lighter investment, safer data, and faster circulation.
Going further, cross-border data cannot just solve transmission, compliance, or preprocessing alone. It requires an end-to-end chain connecting collection, local preprocessing, compliance assessment, cross-border transmission, local processing, and backflow to R&D and application businesses. Synergy must be formed across institutional standards, infrastructure, compliance channels, platform hubs, and ecosystem services. If any link is missing, the entire chain fails.
For each scenario implemented, a corresponding standard framework, tools, and compliance evidence chain are deposited for reference by the next enterprise and market. This is the key to sustaining the scalability of this model.
Chinese automotive global expansion has moved beyond the stage of simple complete vehicle trade and is entering systematic competition. Factories and products can be deployed quickly, but data capabilities require long-term construction. They connect overseas road testing, R&D iteration, and after-sales maintenance, while also involving complex privacy compliance rules across various nations.
If enterprises only solve transmission, or only compliance, or only preprocessing, they cannot form a complete closed loop. What is truly needed is end-to-end integrated data operation capability.