Chapter 5: The Process for Evaluating ODS Adoption

Chapter 5　The Process for Evaluating ODS Protocols Adoption

This chapter organizes the evaluation process by which user enterprises determine whether to enter dataspace-related businesses. The process proceeds in four steps: "(1) Identifying the organization's own challenges and use cases," "(2) Selecting an entry pattern," "(3) Validating feasibility," and "(4) Making the entry decision."

5.1 Step 1: Identifying the Organization's Own Challenges and Use Cases

It is important that entry evaluation begins not from "responding to Open Dataspaces as a technology" but from "solving the organization's own business challenges".

In the world of data management, the principle known as "Garbage-in, Garbage-out" illustrates that collecting large volumes of data without a clear purpose results in nothing more than mounting running costs, with ROI steadily worsening. Breaking free from the illusion that "something good will happen if we just collect data," and instead collecting only the data that is necessary — working backward from the purpose of solving a specific problem — will be the key to project success.

The questions shown in table 7 are examples of how to articulate specific challenges as a starting point for this discussion.

Table 7 Questions to Identify the Organization's Own Challenges

Question (Example)	Focus Area
What specific business or operational challenge are we trying to solve by integrating data in the first place?	Threats and opportunities facing the current business model; operations that are locally optimized at the individual team or company level but not optimized at the organization or supply chain level; data silos
What is currently blocking us in connecting with external data?	Coordination costs for individual API integrations, opacity of data quality, difficulty in aligning terms of use
What would change if we could provide our data externally?	New revenue streams, strengthened partnerships, compliance with industry standards
What data is needed for AI and BI/DI initiatives but currently inaccessible?	Upstream supply chain data, external statistical and market data, real-time environmental data, etc.
How much does our current individual integration work cost in time and effort?	Quantitative estimation from a "Save Money" perspective

5.2 Step 2: Selecting an Entry Pattern (Organized Along Four Axes)

A users entry strategy can be organized as a combination of the following four axes. These axes are not independent — their combination gives concrete form to the entry approach.

(1) The User's Participation Role

Choose from: data provider, data consumer, or a combination of both. Even within the same function, which role the enterprise takes on changes how value is articulated and where responsibility boundaries lie.

(2) Addressing the Priority Pain Point: Which Challenge Will Open Dataspaces Solve?

Identify which of the following represents the organization's greatest pain: 'Where to Get' problem (DAD), 'What to Mean' problem (OSI), or 'Who and How to Use' problem (IUC).

(3) Implementation Scope: The Range of Functions the Organization Will Handle Internally

Distributed (the organization implements and operates protocols in-house) or federated (using managed services). In the initial stages, the federated model tends to lower entry costs.

(4) Scope of Responsibility (SLA): How Much Will the Organization Take On?

Determine whether the organization will design its own data provision scope, terms of use, and SLAs, or whether it will operate within the framework of a managed service.

5.3 Step 3: Validating Feasibility

In addition to a PoC (Proof of Concept) that confirms technical operability, a PoV (Proof of Value) that validates "whether this makes sense as a business" is indispensable. Table 8 presents the steps for feasibility validation.

Table 8 Steps for Feasibility Validation

Type of Validation	What Is Confirmed	Primary Output
PoC. Whether data integration is technically achievable in a minimum configuration (MVP).	Confirmed by building a PoC environment using ODS SDKs or managed services.	Connectivity validation report
PoV	ROI through cost reduction and value creation effects. Lead time improvements. Usefulness of external data for AI applications.	Value hypothesis memo, business case
Operational Feasibility	Whether the operational burden and organizational requirements of the minimum configuration can be understood.	Operational flow

5.4 Step 4: Making the Entry Decision

Whether the organization can form its own hypothesis with a sense of the numbers for the following questions serves as one benchmark for deciding whether to proceed to full-scale entry.

• Relative to current individual integration costs, how much cost reduction (Save Money) can be expected after Open Dataspaces entry?

• To what extent will new revenue and competitive advantage (Make Money) emerge from the use of external data?

• Is the initial investment required for entry (human resources, technology investment, external service costs) acceptable within the business plan?

• Is there competitive risk if other organizations enter the Open Dataspaces busines?

The next chapter organizes the implementation planning process following the decision to enter.