Multi-Organization Data Exchange

When the Thread Crosses Legal Boundaries

Within a single organization, data sharing is a technical and organizational challenge. Across organizations, it becomes a legal, commercial, and strategic challenge as well. The questions shift from "how do we connect these tools?" to "what can we share without giving away competitive advantage?" and "who owns the data generated from our collaboration?"

These are not edge cases. They are the default condition for any complex system developed by multiple companies — which is nearly every aerospace, defense, automotive, energy, and infrastructure program of significant scale.

IP Protection

Every organization contributing to a digital thread has intellectual property it must protect. A supplier's manufacturing process parameters are trade secrets. A prime contractor's system architecture reflects decades of competitive differentiation. A customer's operational requirements may be classified or commercially sensitive.

The tension. The digital thread's value comes from connecting information across boundaries. IP protection's value comes from restricting information flow. These goals are in direct conflict, and the resolution is never a simple binary of "share everything" or "share nothing."

Practical approaches. Define data sharing at the level of abstraction appropriate to the relationship. A supplier does not need to share its proprietary manufacturing process with the prime contractor. But the prime does need to know the resulting material properties, geometric tolerances, and quality metrics. The thread can carry the output of the supplier's process without exposing the process itself.

Technical enablers. Role-based access control at the data element level, not just the document level. Encrypted data exchange with defined recipients. Data use agreements that specify permitted uses (analysis, manufacturing, maintenance) and prohibit reverse engineering. Audit trails that demonstrate compliance with sharing restrictions.

Pitfalls. Over-restriction kills the thread's value. If every data exchange requires weeks of legal review, engineers will bypass the thread and share data informally — via email, USB drives, and phone calls — destroying traceability. The goal is to make compliant sharing easier than non-compliant sharing.

Data Sovereignty

When two organizations collaborate, they inevitably generate data that neither could have produced alone. A prime contractor provides requirements; a supplier runs simulations against those requirements. Who owns the simulation results? The supplier did the work, but the prime's requirements shaped the output.

The tension. Each organization wants to own as much data as possible (to enable future reuse, competitive advantage, and freedom of action) while the collaboration requires sharing. Joint ownership sounds fair but creates practical problems: who controls access? Who can share with third parties? Who is liable for errors?

Practical approaches. Define ownership at the data type level in the contract, before the work begins. Common frameworks include:

• Background IP: Each party owns what they brought to the collaboration. A supplier's pre-existing analysis tools remain the supplier's property.
• Foreground IP: Data generated during the collaboration is owned according to contractual terms — often by the party that performed the work, with a license granted to the other party for the program's purposes.
• Joint IP: Data that genuinely required both parties' contributions. Requires explicit governance: who manages it, who can license it, who is responsible for quality.

Pitfalls. Vague data ownership clauses lead to disputes when the data becomes valuable — which is exactly when you need the thread to work. Define ownership clearly upfront, even when it feels premature.

Federated Models

The alternative to a centralized data repository (where one organization controls the master) is a federated architecture: each organization maintains its own data and models, and the thread connects them at defined interface points.

How it works. Each organization publishes a defined set of interface data — performance specifications, interface definitions, compliance evidence, quality metrics — in an agreed format. The thread connects these published interfaces without requiring access to each organization's internal models.

Think of it as a set of contracts: Organization A publishes that Component X has a mass of 12.3 kg, a power consumption of 45 W, and meets thermal requirement T-003. Organization B consumes that interface data for system-level integration. Neither organization needs to see the other's internal design details.

Advantages. IP protection is inherent — internal models stay internal. Each organization uses its preferred tools and methods. The thread scales because interface data is smaller and more stable than full model data.

Challenges. Interface definitions must be precise and maintained. If Organization A changes its internal model in a way that affects the published interface data but fails to update the publication, the thread breaks silently. Federated architectures require discipline in maintaining the published interfaces — which is fundamentally a governance problem, not a technology problem.

Contract-Based Sharing

In practice, multi-organization data exchange is governed by contracts. The digital thread must operate within the contractual framework, which defines what data is shared, in what format, with what rights, and on what schedule.

Data rights clauses. Government contracts often specify data rights categories: unlimited rights, government purpose rights, limited rights, restricted rights. Commercial contracts define similar categories with different terminology. The digital thread must enforce these categories — preventing unauthorized access to restricted data while enabling authorized access to shared data.

Format requirements. Contracts increasingly specify data delivery in machine-readable formats rather than PDFs. STEP AP242 for product data. OSLC for lifecycle data. Custom XML or JSON schemas for program-specific data. These format requirements enable the thread to consume supplier data directly rather than transcribing from documents.

Timing. Contracts define when data is delivered — at milestones, on demand, or continuously. Moving from milestone-based delivery to continuous data exchange requires both technical infrastructure (secure APIs, real-time feeds) and contractual innovation (data sharing agreements that allow continuous access rather than discrete deliveries).

Standards That Enable Exchange

Three standards deserve specific attention for multi-organization data exchange:

OSLC (Open Services for Lifecycle Collaboration). A set of specifications for linking data across lifecycle tools using web standards (REST, RDF, linked data). OSLC enables tool-to-tool integration without requiring a common data repository. Each tool publishes and consumes linked data according to OSLC specifications. This is particularly valuable for cross-organization integration because it does not require organizations to adopt the same tools.

STEP AP242 (ISO 10303-242). The international standard for 3D product data exchange, covering geometry, product manufacturing information (PMI), and design intent. AP242 enables exchange of rich product data between CAD systems — critical for supply chains where different organizations use different CAD tools.

LOTAR (Long-Term Archiving and Retrieval). Standards for archiving engineering data in formats that remain readable decades later. For long-lifecycle systems (aircraft, nuclear facilities, infrastructure), the data must outlive the tools that created it. LOTAR defines archiving practices for 3D models, simulation data, and associated metadata.

These standards reduce the governance burden by providing common ground. When two organizations agree to exchange data via STEP AP242, they have a shared understanding of data structure, semantics, and quality expectations — without needing to negotiate every detail bilaterally.

Assessment