Software Opportunities After the Data Industry Scale Breaks Through 6 Trillion: Rebuilding the Technical Foundation for Data Element Circulation

2026-08-28 · Software Development · Big Data Analysis

According to public statistics, the scale of China's data industry exceeded 6 trillion yuan in 2025, with annual growth maintained in the double-digit range. This figure alone is not surprising; what is truly noteworthy is the structural change: the share of data trading, data services, and data infrastructure within it surpassed traditional hardware and storage investment for the first time. In other words, the focus of data element marketization is shifting from "storing data" to "making data flow." For the software industry, this is a foundational opportunity no less significant than the mobile internet.

From Asset to Element: Circulation Forces a Rebuild of the Technical Foundation

Over the past decade, when enterprises talked about data, the endpoint was almost always "data assetization"—building warehouses, deploying BI, producing reports; in essence, treating data as an internal asset to be accumulated. The upgraded concept of "data elements" has a completely different meaning: if data is to enter market circulation like land and labor, it necessarily involves four things—rights confirmation, pricing, trading, and compliance—none of which can be solved by traditional databases and BI tools.

This is a key cognitive watershed. As an asset, data only needs to solve "how to view it"; as an element, data must solve "how to safely give it out, how to price it fairly, and how not to break the law during circulation." The latter involves an entire new software technology stack—data governance platforms, privacy computing engines, trusted data spaces, and data API gateways. These barely existed in past enterprise procurement lists, and are now becoming the next-generation infrastructure after databases and middleware.

The Technical Foundation of Circulation: Four Underestimated Links

Data circulation sounds like a policy proposition, but in engineering terms it is actually supported by four specific technical links.

The first isdata governance, which is the prerequisite for all circulation. Without unified data standards, clear lineage relationships, and quality grading, data will lose its semantics once it leaves the original system, and downstream can only see a pile of uninterpretable fields. The reason many data exchanges initially had "transactions but no circulation" lies precisely in the fact that source-level governance was not done solidly. Governance is not a one-time project; it requires metadata collection and quality monitoring mechanisms embedded in the data production process.

The second isprivacy computing, which is the key to compliant circulation. The three technical routes of federated learning, secure multi-party computation, and trusted execution environments actually solve one core contradiction: data needs to be computed jointly, but the raw data cannot be handed over. Cross-institutional data collaboration in heavily regulated industries such as healthcare and finance can almost only take this path. It is worth noting that the performance overhead of privacy computing has long been high, and the slow convergence of federated learning on non-IID data remains the biggest obstacle to engineering implementation.

The third istrusted data space, which solves the problem of "controllable circulation." Through a series of mechanisms such as authorization, usage metering, and sandbox execution, data providers constrain "who can use the data, how many times it can be used, and that it is destroyed after computation." This is essentially moving the cloud-native idea of tenant isolation into cross-organizational data exchange scenarios.

The fourth isAPI-based delivery of data, which is a turning point at the business model level. In the past, selling data meant selling a copy, and control was lost upon delivery; now the more mainstream approach is to encapsulate data into interfaces—priced by number of calls, authorized at field granularity, and traceable in real time. This "data as a service" model turns data from a one-time commodity into a measurable, subscribable software service, and upgrades the path to data monetization from "selling raw data" to "selling data capabilities."

From Selling Data to Selling Capabilities: A New Delivery Paradigm

The most direct impact of this round of data industry growth on software development is the emergence of a new delivery paradigm: data integration and data capability encapsulation. Take Shenzhen's local manufacturing industry as an example. For a medium-sized management system development project, what used to be delivered was functional modules; now more and more clients will ask one additional question—"Can the data accumulated by this system in turn provide data interfaces to my upstream and downstream partners?"

The pain points in manufacturing are very specific: quality inspection data, equipment operation data, and inventory data have long been scattered across separate MES, ERP, and IoT platforms, isolated from one another. Connecting and standardizing these data pipelines, and then outputting them externally via data APIs, is itself a complete software development project, and its repurchase rate is far higher than one-time delivery. Once data capability becomes a service, customers have a reason to keep paying.

There is an easily overlooked judgment here: the software demand truly unleashed by data element marketization is not in the visible scenario of the "exchange," but in the invisible long tail of thousands of enterprises' "internal data organization + external data services." Every enterprise preparing to empower others with data needs a round of data governance, a privacy computing solution, and a data API gateway. The thickness of this long tail is the real incremental growth for the software industry behind the 6 trillion scale.

Trend Judgment: Data Middleware Will Enter an Explosive Window

If we take a longer view, the maturation of the data element market will most likely follow a development trajectory similar to cloud native. When cloud native emerged, the first to break out were "middle layer" technologies such as containers, orchestration, and service meshes, rather than a specific upper-layer application. Similarly, the middle layer of the data element market—data governance engines, privacy computing frameworks, trusted data spaces, and data API gateways—is entering a concentrated explosive window.

For enterprises, this trend implies a clear choice: the earlier internal data governance and API-ization capabilities are built solidly, the more proactive a position they can occupy in the process of data circulation. Technology itself is not the goal; using technology to transform data assets into circulable and monetizable business value is.

Industry Insight: Govern First, Then Circulate

For most enterprises, the rational path is "internal first, external second": first solidify the foundation of internal data governance, then consider compliance and technical solutions for external data circulation, rather than jumping directly into the trading link. Forced circulation when data quality is substandard will only bring compliance risks and downstream trust collapse.

Xiangming Technology has repeatedly verified this in the process of serving local Shenzhen enterprises—projects that solidly complete data governance before discussing data capabilities have significantly higher later data API reuse rates and success rates in external cooperation. For enterprise digital transformation, in the era of data elements, the most expensive thing is not the data itself, but the layer of engineering capability that makes data trustworthy and circulable.

This article was written by the industry research team of Xiangming Technology. Internal links for reference:Xiangming Technology Official Website Provides software development, big data analysis, and enterprise digital transformation solutions.

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