Research and Development Business Data Model: AI-Ready Edition

The Research and Development Business Data Model: AI-Ready Edition is for CIOs, architects and transformation leads who need to know what information R&D work runs on before an AI agent touches it. It covers 16 subject areas, 112 entities, 829 key attributes and 440 relationships. Every entity and key attribute is classified for sensitivity, with the AI uses and data-quality requirements an agent depends on. It is the data-model part of Business Architecture for Research and Development: AI Transformation Blueprint, sold on its own.

What's Inside

The same model comes in three files: the full model in Word, a workbook to filter and extend, and a guide to the scales and the license.

• 16 subject areas and 112 entities, each with a definition, its typical system of record, sensitivity class, AI uses and data-quality requirements
• 829 key attributes with sensitivity and AI relevance, and 440 relationships with cardinality
• Under each entity, the capabilities that need it and the agents that read it, by name and ID
• The data and information items of the earlier edition of this model, each mapped to the entity that now holds it
• The Business Data Model (Word): every subject area and entity in full
• The workbook (Excel): subject areas, entities, attributes and relationships in sheets you can filter and extend
• The guide (Word): the scales the model refers to, the data foundation, and the license
• Free corrections, and new versions free for 180 days

Every file names your organization and carries your license ID.

The 16 Subject Areas

The 112 entities are grouped into 16 subject areas. The list gives what each holds and how many entities it has.

• Organization & Strategy (4 entities): The organizational frame R&D works inside: business units, R&D objectives, the technology strategy and the research themes that carry it.
• Portfolio & Investment (7 entities): The records that decide where R&D money and effort go: portfolios, programs, prioritization criteria, gate reviews, budgets, allocations and actual spend.
• Technology Intelligence & Roadmaps (6 entities): What R&D knows about technologies and the world around them: the technology taxonomy, signals, maturity assessments, competitors, external factors and the roadmaps built from them.
• Ideas & Concepts (6 entities): The front end of innovation: ideas, innovation challenges, idea evaluations, concepts, customer needs and feasibility assessments.
• Research & Experiments (8 entities): The scientific record: proposals, studies, experiments, protocols, observations, findings, peer reviews and publications.
• Research Data & Models (6 entities): Research data and the models built on it: datasets, provenance, corrections, access grants, scientific models and simulation runs.
• Research Ethics & Participants (5 entities): The records that protect people and the integrity of research: ethics reviews, research participants, consent, consent withdrawals and integrity concerns.
• Requirements & Design (8 entities): How needs become a design: stakeholder needs, requirements, trace links, system architecture, design items, engineering analyses, design risks and the engineering bill of materials.
• Design Release & Configuration (5 entities): The controlled state of a design: design reviews, baselines, releases, engineering changes and configuration items.
• Prototypes & Testing (8 entities): Evidence that a design works: prototypes, test plans, test runs, test results, failure reports, verification and validation records and reliability assessments.
• Projects & Resources (9 entities): How R&D work is planned and staffed: projects, business cases, deliverables, risks, dependencies, change requests, resources, assignments and capacity forecasts.
• Conformity & Product Lifecycle (9 entities): What a product must meet and how it lives after development: products, applicable requirements, conformity evidence, certification, design transfer, technical documents, field issues, obsolescence and retirement.
• Intellectual Property (9 entities): The organization's intellectual property: invention disclosures, inventors, IP assets, patent applications, patents, trade secrets, prior art, freedom-to-operate assessments and infringement alerts.
• Partnerships, Funding & Licensing (6 entities): Work with outside organizations: research partners, partnership agreements and their obligations, research funding awards, license agreements and royalty records.
• Laboratories, Knowledge & Expertise (9 entities): The physical and human base of R&D: laboratories, equipment, bookings, research materials, safety incidents, knowledge assets, lessons learned, technical competencies and expertise profiles.
• AI & Digital Labor Governance (7 entities): The records that make AI in R&D accountable: AI systems, agents, action logs, AI decision records, model validations, human reviews and the instruction limits that bound any agent working with laboratory or test equipment.

Who Uses It

Data owners and stewards, data architects, and AI and analytics engineers who need to know what an agent may read, how sensitive it is and how good it must be first; and business architects who link capabilities to data.

How It Fits With the Other Products

The capabilities named under each entity are defined in the Research and Development Capability Model: AI-Ready Edition. The agents are specified in Business Architecture for Research and Development: AI Transformation Blueprint, which contains this data model and adds the value streams, use cases, controls, an operating model and a roadmap. The Research and Development Value Streams are sold on their own too.