Pharmaceutical Business Information Model

Pharmaceutical manufacturers manage data from molecule through commercial sale — product, regulatory, clinical, and patient-safety records that don't map cleanly onto a generic industry template. This business information model organizes those data entities into foundational, transactional, and informational layers, with the relationships between them documented, delivered as an editable Word document.

Pharmaceutical product data has a hierarchy that a flat "product" entity destroys. The molecule is distinct from the brand or formulation, which is distinct again from the specific packaged SKU — and the same molecule routinely appears under multiple brand, generic, and international names, which matters because adverse events have to be aggregated at the molecule level, not the brand level, to meet pharmacovigilance obligations. "Customer" is genuinely three entities, not one: the prescriber who writes it, the patient who takes it, and the payer who reimburses it, each with a different data relationship to the product. Commercial data adds an entity with no equivalent in most industries — the chargeback, where a wholesaler buys at list price, resells to a hospital or pharmacy at a contracted price, and the manufacturer reimburses the difference. Batch genealogy then has to trace raw material lots through manufacturing to a finished batch, and forward again to any adverse event or recall tied to it.

Contents

• Foundational entities — molecules, products/brands, SKUs, regulatory approvals, prescribers, payers
• Transactional entities — batch and lot records, samples, rebates and chargebacks, adverse events
• Informational entities — pharmacovigilance signals, market access/formulary status, batch traceability
• Entity relationships mapped across the model, delivered as an editable Word document

Applications

• Foundation for pharmacovigilance and quality system data architecture
• Reference for molecule, product, and SKU master data hierarchies
• Feeds integration planning across manufacturing, commercial, and safety systems
• Framework for modeling chargeback and rebate data flows

Best Suited For

A fit for data architects, business architects, and IT leaders at pharmaceutical manufacturers and technology partners working on commercial, quality, or safety system initiatives.