Chapter 4: ODS Participation Structure

4.1 Choosing a Participation Model: Distributed vs. Federated

ODS-RAM offers two implementation patterns: the "Distributed Service Model" (in which the organization directly implements and operates ODS Protocols) and the "Federated Service Model" (in which the organization participates via a managed service provider). Table 4 presents an overview of the Distributed, Federated, and Hybrid models, the last of which combines elements of both. For users, the federated model tends to lower the barriers to entry in the initial stages. It should be noted that even in the federated model, the data provider retains responsibility for data and ontology, and exercises usage control.

Table 4 Participation Models in ODS-RAM

Participation Model	Overview	Cases Suited to User Enterprises
Distributed	Operate ODS Middleware in-house and connect directly	When the enterprise has technical capability and operational resources, and wishes to retain full control over software and its operations
Federated	Participate using infrastructure provided by a managed service provider	When seeking early entry while keeping initial costs low, or when technical resources are limited
Hybrid	Core functions are operated in-house; supplementary functions leverage external services	When expanding the scope of participation in a phased manner

4.2 The Position of Users in Open Dataspaces

Open Dataspaces is not a centralized platform but a distributed architectural paradigm in which multiple parties, centered on domain owners, each fulfill distinct roles and responsibilities. When evaluating entry and adoption, it is important to understand one's own position not from the perspective of "which entity to become", but rather "which functions and responsibilities to take on". (Table 5)

Table 5 Types of Users in Open Dataspaces

Participation Role	Primary Responsibilities	Examples of Entry as a User
Data Provider (Domain Owner)	Provide internally held data and its meaning (ontology) as a Product to external parties, and manage terms of use internally	Conditional external provision of product data, sensor data, and purchasing data
Data Consumer	Acquire external data under defined conditions and apply it to operations, analytics, and AI	Acquisition of supply chain data, integration with external APIs
Industry Service User	Adopt ODP-implemented applications and managed services as a user	Use of Open-Dataspaces-compatible BI tools, DI tools, AI services, and inventory management services
Fundamental Service User (Federated Model)	Participate by utilizing foundational functions (Identity and Trust, Metadata Exchange, etc.) provided by a managed service provider	Connect to Open Dataspaces using an external provider's ODP implementation

4.3 Phased Entry Maturity

Participation in Open Dataspaces does not require uniform compliance with all requirements from the outset; phased adoption in line with the organization's own maturity is both realistic and effective. Table 6 presents the levels of phased adoption.

Table 6 levels of Phased Adoption

Level	Status	Typical Activities
Lv0: Pre-Consideration	Not yet participating in Open Dataspaces; in information-gathering phase	At the stage of reading this document; beginning to articulate challenges and use cases
Lv1: Awareness	Understanding concepts of Open Dataspaces and benefits; beginning internal discussion	Evaluating whether to enter at the management and business planning level
Lv2: PoC Participation	Participating in small-scale proof of concept using managed services	Piloting one to two distributed data management use cases
Lv3: Full Participation	Full operation of internal data provision and external data utilization	Distributed data management with multiple partners in continuous operation
Lv4: Expansion and Rollout	Horizontal expansion across multiple Open Dataspaces and industry profiles	Leveraging Open Dataspaces as a competitive business advantage

4.4 Use Case-Driven Adoption Strategy

The key to successful participation in Open Dataspaces is to advance not from "abstract discussion of the framework" but in alignment with actual use cases. To progress participation in a phased manner, it is practical and effective to start by establishing the minimum necessary functions and governance structures within the use cases in which the organization has a direct stake.

The following questions are useful as a starting point for selecting use cases:

• Is the organization primarily a "data provider," a "data consumer," or both?

• What is the current level of cost and effort spent on individual API integrations and CSV-based data exchange? (Save Money perspective)

• How would data obtained through Open Dataspaces contribute to the organization's AI initiatives and BI/DI capabilities? (Make Money perspective)

The next chapter systematizes the entry evaluation process for organizing answers to these questions.