Industrial Big Data: The Core Key to Intelligent Manufacturing
Big data has now been widely acknowledged across the industry as a key technical enabler for industrial upgrading. Within the technical roadmap of Made in China 2025, industrial big data is positioned as a critical breakthrough area. Over the next decade, data-centric intelligent systems will serve as the core driving force behind intelligent manufacturing and the Industrial Internet.
The importance of industrial big data is widely recognized, yet fundamentally speaking, big data is a means rather than an end — the same holds true for artificial intelligence. It would be a grave misconception for enterprises to blindly embrace the Industrial Internet, industrial big data and AI technologies simply because these concepts are trending.
Industry’s Evolution from Basic Data to Big Data
Manufacturing enterprises have operated smoothly for hundreds of years prior to the emergence of new-generation information technologies. We must clearly recognize that information technology acts more like a catalyst. Enterprises should first define clear business objectives to improve existing production processes, industrial products and management methodologies.
At its core, big data underpins manufacturing business transformation to boost quality and efficiency, enabling an intelligent manufacturing system built upon automation and informatization. Only after establishing intelligent manufacturing can enterprises develop platforms, build industrial ecosystems, achieve more efficient collaboration across industrial chains, and unlock multiplicative growth for the Industrial Internet.
There are three typical application scenarios for industrial big data, which also represent the core goals of the Industrial Internet: intelligent equipment, service-oriented manufacturing and cross-industry integration.
The first layer lies at the equipment level: enhancing the reliability of individual machines, identifying equipment faults, and optimizing equipment operation.
The second layer focuses on production lines, workshops and factories to elevate operational efficiency, covering energy consumption optimization, supply chain management, quality control and more.
The third layer breaks factory boundaries to realize cross-industry interconnection.
Industrial big data does not emerge out of nowhere. Traditional industrial informatization has long generated massive volumes of data spanning R&D, production and after-sales service. The shift from scattered industrial data to integrated industrial big data largely relies on the fusion with data from automation domains, namely the integration of industrialization and informatization. In the Industrial Internet era, we also need to incorporate data from upstream and downstream industrial chains as well as cross-industry sectors.
How Industrial Big Data Acts as the Core Driver of Intelligent Manufacturing and the Industrial Internet
Industrial big data is characterized by multi-modality, high throughput and strong relevance. Over 130 distinct types of data have been identified in industrial scenarios, featuring diverse data modalities and intricate structural relationships.
High throughput means data is generated continuously at high collection frequencies with massive transmission volumes.
Strong relevance indicates that industrial data is backed by solid physical mechanisms; correlations exist at the mechanical and disciplinary level instead of merely between data fields.
Simply deploying deep learning or reinforcement learning algorithms cannot deliver effective industrial big data analytics. Practitioners must master mechanism models and quantitative domain knowledge of research objects, which poses major challenges for further technological advancement. Industrial big data applications are fundamentally built on identifying statistical correlations between data inputs and outputs to compensate for ambiguous or incomplete mechanism models.
Business-Led, Data-Driven Industrial Development
Intelligent manufacturing is increasingly powered by data. From intelligent manufacturing to Industrial Internet platforms, the core logic lies in leveraging data and models to optimize the allocation efficiency of manufacturing resources.
The Industrial Internet is not equivalent to intelligent manufacturing. The key distinction lies in whether data and business operations break factory boundaries. Currently, excessive attention is paid to platform capabilities, whereas the true essence of the Industrial Internet is industrial ecosystems. Resource optimization evolves from descriptive and diagnostic analysis to predictive analysis and decision-making, with its scope expanding from single equipment and production lines to industrial chains and broader industrial ecosystems.
The success of the Industrial Internet hinges on how ecosystems build business systems and realize cross-industry collaboration, and its development direction is ultimately determined by business demands. We have always opposed the mindset of carrying a hammer and hunting for nails to strike — a common flaw among many big data and AI vendors today.
Instead, enterprises must delve deep into specific industrial sectors to develop targeted, reliable tools tailored to actual business demands. Business and problem orientation, rather than technology orientation, is the fundamental engine of industrial progress. To sort out business logic and data assets, conduct data assessment and deliver tangible business outcomes, three core elements must work in synergy: people, application scenarios and algorithms.
Source: Information and Software Service Network
