Gasgoo Munich- When discussing Chinese automakers' global push, the industry typically fixates on finished vehicles, production capacity, and traditional supply chains. But for Chu Huabin, general manager of Qindarui (China) Information Technology Co., Ltd., that's no longer sufficient in the age of intelligence. The real factor determining whether Chinese carmakers can sustain their global lead is a long-overlooked supply chain: data.
Speaking at the Automotive Industry Global Expansion Innovation Conference, co-hosted by the Chebaihui Institute and Hainan Qinglan Zhilian Technology Co., Chu noted a shift: Chinese firms are moving from exporting products to exporting entire systems, and from simply selling cars globally to operating globally. While parts and logistics powered the previous wave of globalization, the intelligent era demands the addition of data.
Global operations imply a split workflow: some tasks are executed locally, while others loop back to headquarters. Whether it's returning R&D data or integrating after-sales information, nothing works without cross-border data flows.
Data Becomes the Second Supply Chain
Chu defines this current wave of expansion as "systematic globalization," with the core pivot being from global sales to global operations. The old model—treating China as the sole base for all operations before exporting finished vehicles worldwide—is no longer viable. Instead, a portion of R&D, operations, and services must unfold locally, while the rest coordinates back home. He encapsulates this approach as "In China for Global."
In this global division of labor, data stands on equal footing with traditional goods. Whether it's the return of overseas R&D data or the accumulation of maintenance and after-sales records, the core issue revolves around data. Chu's assessment is stark: the product supply chain determines whether cars can go abroad, but the data supply chain determines whether Chinese companies can sustain their global leadership.
Image source: Doubao
Constraints on cross-border data are tightening rapidly. Europe implemented the GDPR in 2018 after passing it in 2016, while Southeast Asia and South America have been establishing their own data protection regimes. As data value climbs in the intelligent era, so does regulatory scrutiny over outflows. Whether data is leaving or entering, companies must establish robust cross-border management mechanisms.
The challenge becomes stark when a new energy vehicle (NEV) maker conducts autonomous driving road tests across multiple Southeast Asian cities. The environment is typically tropical—frequent, heavy downpours and roads shared with weaving buses and motorcycles. Add in the complexity of right-hand drive vehicles driving on the left, and a single day generates hundreds of gigabytes of street-view imagery and point cloud data, alongside algorithm decision logs.
This data is the core asset for refining autonomous driving models for foreign roads. R&D departments universally need to bring this information back home; only by transmitting it to China safely, promptly, and compliantly can they support model iteration.
The difficulties are twofold. First, transmission: moving massive volumes of data back to China requires dedicated international lines. Second, compliance: the footage contains pedestrians' faces, license plates, and other protected private information. Local laws often prohibit raw images from leaving the country, meaning irreversible blurring and license plate tokenization must happen on-site. Every step of transmission must align with both local and Chinese regulations; a single misstep can cross regulatory red lines.
Many companies, citing cumbersome processes and high penalty risks, opt to process data entirely locally. While this sidesteps compliance risks, it deals a significant blow to R&D iteration efficiency.
Turning Scattered Data Compliance into a Reusable Public Service
At an industry level, the reason cross-border data has become a universal burden is that most companies are redundantly solving the same set of problems. Collectors, dedicated line operators, law firms, and platforms must all be engaged separately. Every time a company enters a new country, the entire chain requires re-verification. Firms often have to maintain sizable in-house compliance teams, yet their experience and methods are difficult to replicate across different scenarios or borders.
The solution the industry needs is to transform one-off, fragmented data transmissions into a reusable service system. The first step is replicating the model: providing a unified entry point and a contract covering the entire chain to achieve end-to-end, one-stop processing. Once a scenario or country is successfully navigated, the established compliance methodology, transmission infrastructure, and service system can be horizontally copied, significantly reducing redundant investment.
Image source: Doubao
Next is compliance pre-positioning at the process level: data is pre-processed at local nodes. Faces and license plates undergo irreversible blurring and tokenization; point clouds are separated and time indexes are extracted. Only after passing a compliance gate is the data released, traveling via low-latency channels to China. Data usage, access periods, and invocation permissions are all encoded into digital contracts, ensuring controlled use and preventing the spread of raw data, with every access and transfer fully logged.
Third is service support at the regulatory level: specialized systems map out data regulations in major countries, outputting preliminary screenings, declaration material lists, and key risk points. By identifying the optimal transmission path for the business before handing it to professional teams for review, companies are spared the cost of researching foreign rules from scratch. Connecting these links ensures that the return of a single set of road test data requires lighter investment, is safer, and moves faster.
Looking further, cross-border data management cannot address just one link—be it transmission, compliance, or pre-processing—in isolation. It requires an end-to-end chain connecting collection, local pre-processing, compliance assessment, cross-border transmission, local processing, and finally the loop back to R&D and application. Synergy is needed across institutional standards, infrastructure, compliance channels, platform hubs, and ecosystem services; without any single link, the entire chain fails.
The key to scaling this model lies in standardizing every successful scenario: landing a use case creates a corresponding standard framework, set of tools, and chain of compliance evidence that the next company or market can reference.
The Hainan Going Global Industrial Technology City Data Cross-border Service Platform, currently launching with Qindarui as a digital partner, aims to convert the complex tasks shouldered by individual firms into platform-based, service-oriented public capabilities. In its initial phase, the platform has established eight types of trusted data sub-spaces for the automotive sector, identified eight industry data scenarios, and aggregated ten types of ecosystem partners.
Chinese automotive exports have moved beyond the stage of simple finished-vehicle trade and are entering an era of systemic competition. Factories and products can be rolled out quickly, but data capabilities require long-term cultivation. They connect overseas road testing, R&D iteration, and after-sales operations, all while navigating the complex web of global privacy compliance rules.
If companies only address transmission, or only compliance, or only pre-processing, they cannot form a complete closed loop. What is truly required is end-to-end data operational capability.
