On July 31, the National Development and Reform Commission (NDRC) disclosed that AI-related industries in China maintained high growth of over 30% in the first half of the year.
By the end of June, national intelligent computing power had reached 2.8 times that of the same period last year, with high-quality datasets exceeding 120,000.
As computing power scales and large models advance, the AI industry is accelerating.
However, shifting from macro-level industry trends to internal enterprise realities raises a practical question: With abundant computing power and mature models, why do many enterprise AI applications remain stuck in demonstrations, pilots, and localized validations?
The answer usually lies not in the models themselves, but in the underlying data systems.
Enterprises often lack not another model API, but a data infrastructure capable of continuously providing high-quality, traceable, and reusable data.
1. Over 30% AI Growth Signals a Shift in Competitive Focus
High-speed AI growth signals a transition from technical capability building to large-scale application.
Early on, computing resources and model availability were core prerequisites. Now, as infrastructure improves and model services standardize, the barrier to accessing AI capabilities is lowering.
The intelligence gap between enterprises will no longer depend solely on having models, but on whether they possess clearly structured, consistently defined data, can continuously fix quality issues, trace data origins, and securely provide data to business and AI systems.
In short, as computing power and models become public capabilities, the key competitive variable is shifting from the model side to the data side. Computing power solves "can we calculate," models solve "how to calculate," and data governance solves "what to calculate with, whether the basis is reliable, and if results can be trusted."
Before entering production, enterprises must systematically reconstruct their data infrastructure.
2. The True Bottleneck Is Data Usability, Not Volume
Most enterprises have abundant data across various systems, but "having data" and "using data" are entirely different.
Data formed across different departments and systems over time often results in isolated structures and metrics. These issues surface when attempting cross-system analysis or AI applications:
- Inconsistent Standards: The same "customer" might be identified by phone in sales, company name in finance, and contract entity in projects. Without unified data elements and metrics, models face conflicting expressions rather than clear business facts.
- Uncontrollable Quality: Nulls, duplicates, and anomalies are common. In AI scenarios, these are amplified. Models don't inherently understand anomalies, leading to plausible but untrustworthy outputs.
Opaque Data Pipelines: Without unified metadata and lineage tracking, troubleshooting requires checking systems and SQLs individually, making change assessments difficult.
Data Trapped in Systems: Even with data warehouses, data often remains as tables or files. Without standardized services, it cannot be easily reused by AI applications.
3. Data Platforms Are Enterprise Data Production Systems
Data platforms become bottlenecks because they connect the entire data lifecycle.
A sustainable system requires four continuous stages: Data Ingestion → Data Governance → Data Assetization → Data Servicing. Missing any stage disrupts applications.
A data platform should not just be centralized storage or a collection of tools; it is an enterprise-level data production system that continuously ingests heterogeneous data, translates business definitions into standards, embeds quality rules, manages governance, and packages results into standard services.
Bridging the "last mile" means ensuring data has clear definitions, controllable quality, traceable origins, clear permissions, and stable calls before reaching models.
4. Achieving the Transition from "Data Aggregation" to "Data Usability"
These challenges point to a lack of data governance capabilities. qData Data Platform is designed to bridge this gap by integrating connections, standards, development, quality, assets, and services into a unified architecture:
- Data Can Enter: qData uses standardized connectors to ingest diverse sources (relational, domestic, big data, messaging, files) via single-table, full-database, SQL, file, or real-time sync, managed through scheduling and monitoring to form sustainable data channels.
- Data Is Regulated: Through a "Standard Data Elements—Logical Models—Standard Documents" framework, qData unifies definitions. This translates scattered business definitions into executable governance rules, shifting governance from post-hoc correction to the design phase.
- Data Is Governed: qData supports single and cross-table validation rules across accuracy, completeness, consistency, uniqueness, and validity. It provides quality scores and problem lists, enabling automated cleaning (e.g., null filling, format unification) and manual correction workflows, embedding continuous governance into pipelines.
- Data Is Clarified: Through unified metadata management and asset maps, qData integrates tables, files, and APIs with structures, quality scores, and lineage. Field-level lineage helps assess the impact of upstream changes, upgrading asset management to understanding and tracking data.
- Data Is Utilized: qData publishes tables and queries as API services with authentication, authorization, and monitoring. This isolates applications from underlying source changes and decouples them from databases, making the platform a unified entry point for data capability output.
Conclusion: The Second Half of AI Competition Is About Data Engineering
The 30%+ AI growth means the industry is entering a new phase, but purchasing computing power and models is only the starting point for enterprises.
Converting scattered, heterogeneous data into unified, trusted, and reusable assets determines whether models accurately understand business and whether AI enters production.
Data platforms are not separate projects but critical infrastructure connecting business systems, data, and AI. qData helps enterprises transition from data dispersion to aggregation, from aggregation to trust, and from trust to usability.
Future intelligence gaps will depend on continuously providing real, accurate, and governable data to models.
Computing power determines speed, models determine reach, but data governance determines whether AI ultimately enters real business.
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