Chapter 2: Industrial Structural Transformation in the AI Era and the Strategic Value of Data Management

2.1 Industrial Structural Transformation

The structure of value creation has undergone significant change in recent years. A model in which value is continuously generated not only through performance and quality at the point of manufacture, but also through functional updates during operation and integration with external services, is becoming the norm. This shift is observed across many sectors, not just the automotive industry or manufacturing industry, and operations and services that leverage data and software are increasingly becoming the center of value competition.

2.2 Global Structural Change and the Reality of Platform Competition

In digital domains such as search, operating systems, social media, e-commerce, and cloud computing, platform-based businesses built on the accumulation of users and data have established competitive advantages. This is driven by a network effect: as more users join, more data accumulates, which enables service improvements, which in turn attracts even more users.

In the domain of industrial data collaboration that this document addresses, however, different dynamics prevail compared to consumer-facing platforms. Data exchanged between enterprises comes with prerequisites such as contractual terms, liability boundaries, confidentiality requirements, and regulatory compliance, meaning that centralized management or lock-in by a single entity is not necessarily rational. According to research by NEDO (New Energy and Industrial Technology Development Organization)*1, the digital-related market centered on applications and middleware for such industrial data is expected to continue expanding, with projections reaching approximately 1.2 trillion dollar globally by 2040.

2.3 The Structural Problem of "Data Exists But Cannot Be Used" and Data Access Capability

In value creation involving AI and other technologies, competitive advantage is determined less by what data a company possesses and more by whether it can access the necessary data under appropriate conditions — what can be called data access capability. Yet many enterprises have not made sufficient progress in leveraging external data. The root cause lies not in a lack of effort by individual companies, but in structural factors. The structural challenges that impede data collaboration are systematically documented in Design Philosophy. This document proceeds on the premise that these challenges exist.

The next chapter examines how Open Dataspaces differs from conventional data management frameworks and reviews the functional structure that users need to understand.

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Footnotes

*1 New Energy and Industrial Technology Development Organization (NEDO). (2025). Market Size Study of Data Spaces and Impact Modeling & Scenario Analysis Report. https://www.nedo.go.jp/content/800039315.pdf