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Key Rationale

1. Unification of Fragmented Practices​

Currently, product descriptions exist in islands of specification:

  • Data Products rely on DPDS (deployment) and DPROD (semantic).
  • AI artifacts use partial standards such as ONNX, PMML, or documentation frameworks like Model Cards.
  • Physical products are covered by GoodRelations, ETIM, or PLM ontologies.
  • Software services use heterogeneous API catalogs and ad hoc descriptors.

The Base Product Specification (BPS) provides a unifying kernel to which these disparate approaches can align.
It eliminates redundancy by abstracting common elements — identity, ownership, lifecycle, obligations — into a shared ontology.

2. Consistency of Governance​

Fragmentation leads to inconsistent governance:

  • Contracts and Service-Level Objectives (SLOs) are attached differently across domains.
  • Provenance and lineage are tracked with varying fidelity.
  • Compliance controls (e.g., GDPR, EU AI Act, ISO 42001) must be implemented separately.

BPS provides a common governance model that enables uniform handling of:

  • Obligations and policies.
  • Accountability and ownership.
  • Provenance and evaluation metrics.

This strengthens trustworthiness and regulatory alignment.

3. Reduction of Integration Costs​

Enterprises often maintain multiple catalogs for Data, AI, APIs, and services.
This creates duplication in:

  • Entitlement systems.
  • Discovery interfaces.
  • Lifecycle management workflows.

By adopting BPS as a shared foundation, integration costs are reduced.
Enterprises can operate one unified marketplace instead of parallel silos.

4. Future-Proofing​

New product categories are constantly emerging:

  • Digital twins in manufacturing and smart cities.
  • Simulation products in financial risk and climate modeling.
  • Autonomous agents in AI-driven enterprises.

Without a base specification, each new category must reinvent product semantics.
With BPS, new categories are introduced as extensions, preserving compatibility while enabling innovation.

5. Interoperability Across Standards​

BPS ensures alignment with established standards:

  • W3C vocabularies: DCAT for datasets, PROV-O for provenance.
  • ISO/IEC ontologies: top-level frameworks for semantic consistency.
  • Commercial ontologies: GoodRelations for product offers.
  • AI governance frameworks: NIST AI RMF, ISO/IEC 22989, EU AI Act.

By serving as a neutral kernel, BPS enables mapping and interoperability across multiple standards families.

6. Strategic Value Creation​

The rationale for BPS extends beyond operational efficiency:

  • It enables marketplaces where Data and AI Products coexist.
  • It enhances discoverability and reuse across enterprise and ecosystem boundaries.
  • It supports innovation by lowering the barrier to introducing new product classes.
  • It strengthens accountability by ensuring ownership, obligations, and evaluations are consistently attached.

7. Conclusion​

The rationale for BPS rests on three pillars:

  1. Unification of fragmented domain practices.
  2. Consistency in governance, compliance, and lifecycle management.
  3. Future-readiness for emerging product types and regulatory frameworks.

By adopting BPS, enterprises and standards bodies secure a sustainable, interoperable foundation for productization in the digital age.