Data Architecture — Designing a Data Model for the Digital Product Passport: A Step‑by‑Step Walkthrough

The EU’s Digital Product Passport (DPP) is moving from policy into implementation — and the quality of your data model will make or break compliance, interoperability, and product velocity. For European tech leaders and policy stakeholders, this guide translates legal intent into a concrete, scalable data architecture that satisfies regulators while creating real business value.

Below, you’ll find a pragmatic, step‑by‑step approach to model design — from identifiers and lifecycle events to access control, verifiability, and systems integration across GovTech and LegalTech ecosystems.

TL;DR — What you will get right if you follow this guide

• A standards‑aligned data model that maps to ESPR requirements and industry vocabularies.
• Clear separation of public vs restricted data — designed for privacy, IP protection, and market surveillance.
• Interoperability by default — using GS1 Digital Link, EPCIS 2.0, JSON‑LD, and W3C Verifiable Credentials.
• A practical rollout plan — start with a Minimal Viable Passport and iterate per product category.
• Governance you can operate — roles, stewardship, and controls that auditors and regulators can trust.

What is the Digital Product Passport — in plain terms

A Digital Product Passport is structured product information that travels with the product across its lifecycle — design, manufacturing, distribution, use, repair, and end‑of‑life — to support circularity, transparency, and compliance across the EU. It is accessed via a data carrier such as a QR code and exposes both public information for consumers and controlled information for professionals and authorities.

In simple terms: it’s a durable, scannable “file” about your product — what it is, what it contains, how it was made, how to repair it, how to recycle it, and whether it meets EU rules.

Design principles for a DPP‑ready data model

• Interoperability first — align to open standards and shared vocabularies to prevent vendor lock‑in.
• Verifiable and tamper‑evident — support digital signatures and credential formats to prove authenticity.
• Least‑privilege transparency — publish what’s needed publicly while safeguarding sensitive or personal data.
• Lifecycle completeness — model events, not just master data, to cover circularity and compliance needs.
• Operationally realistic — keep payloads lightweight for scanning performance and offline access.
• Governance by design — embed stewardship, quality rules, lineage, and audit into the model.

Step‑by‑step walkthrough

Step 1 — Define scope, stakeholders, and data audiences

Identify required product categories and implementation timelines, then map who needs to see what.

• Consumers — product claims, safety instructions, care/repair, environmental info.
• Professionals (repairers, recyclers) — parts, schematics, disassembly, hazard notes.
• Authorities — conformity data, certificates, traceability events, recalls.
• Partners (suppliers, logistics) — materials, batches, lifecycle events.
• Internal teams — master data, BOM, quality, EHS.

Create a RACI per attribute group to make ownership explicit.

Step 2 — Choose identifiers and URIs

Consistency starts with identity:

• Product master — GTIN, brand owner, model/variant codes.
• Serialized item — SGTIN/serial or equivalent per category; batch/lot where applicable.
• Facilities — GLN for sites; ISO country codes; internal plant IDs.
• Digital Link URI — the QR‑encoded entry point that routes to the right passport view.
• Optional decentralized identity — DIDs for issuers/holders if you adopt verifiable credentials.

Rule of thumb: one canonical identifier per entity, with stable cross‑references and versioning.

Step 3 — Define canonical entities and attributes

Create a small, durable set of entities that cover 80–90% of scenarios:

• Product, ProductVariant, Item (serialized), Batch
• Material, Component, Substance
• EconomicOperator (manufacturer, importer, distributor, repairer, recycler)
• Facility (site), Equipment
• Certification, Declaration, Assessment
• Document (attachments), Image, Instruction
• LifecycleEvent (EPCIS), Shipment, Return, Repair
• EnvironmentalFootprint, Energy, Durability, Repairability
• AccessPolicy, Credential, Consent
• DataSource, DataContract, DataLineage

Keep attribute names human‑readable and map them to standard vocabularies via JSON‑LD contexts.

Step 4 — Model lifecycle events using EPCIS 2.0

DPP is not just static facts — it’s what happened and where. Use event modeling to represent:

• Commissioning, transformation, aggregation, shipping/receiving, disaggregation
• Business step, disposition, readPoint (where), bizLocation (who), time, source/destination
• Link events to items, batches, and certificates to form provenance chains

This powers recall traceability, warranty, and circularity analytics.

Step 5 — Capture compliance and sustainability data

Create structured sub‑documents for:

• Conformity and certificates — CE marking basis, harmonized standards, validity periods
• Restricted substances and material declarations — with thresholds and references
• Durability and repairability indices — with test protocols and scoring methods
• Energy performance and environmental footprint — method, system boundary, version, verifier
• Safety and instructions — multilingual, accessible formats, versioned

Model both the value and the methodology — auditors will check how you obtained the number.

Step 6 — Design access tiers and policy rules

Separate the passport into tiers:

• Public — safe for consumers and marketing claims
• Restricted — professional access (after role or credential check)
• Confidential — authority access or NDAs, never public

Use role‑ and attribute‑based policies: role = “authorized repairer” AND device.category = “electronics” AND brand = “X”. Link policies to attributes at the field level, not just the document.

Step 7 — Data quality, lineage, and governance

Treat DPP as an MDM program with external exposures:

• Golden sources and data contracts per attribute domain
• Quality rules — required fields per category, allowed value sets, freshness SLAs
• Lineage — where data came from, when, and under which contract
• Stewardship — named owners, escalation, and change control

Step 8 — Storage and persistence patterns

Use polyglot storage to balance scale and flexibility:

• Relational for master data and governance
• Document store for passport payloads and localized content
• Graph for relationships (items ↔ components ↔ suppliers)
• Object storage for documents and media
• Event store for EPCIS events, partitioned by time and GTIN

Keep public payloads small — retrieve restricted details on demand behind policy checks.

Step 9 — APIs and exchange formats

Prioritize JSON‑LD for semantics, and design stable, versioned APIs:

• Read operations — per GTIN, per serial, per Digital Link URI
• Event ingestion — EPCIS JSON events with schema validation
• Credential endpoints — issue, verify, revoke W3C Verifiable Credentials
• Webhooks — recall notices, certificate expiries, non‑conformance alerts

Use idempotency keys and ETags to support caching and offline behavior.

Step 10 — Security, privacy, and legal alignment

Operationalize EU Data Compliance & Strategy:

• Data minimization — publish only what each audience requires
• GDPR — personal data avoidance; if unavoidable, define legal basis, retention, and DSAR handling
• Trade secrets — field‑level policies and non‑extractable rendering where possible
• Integrity — digital signatures, tamper‑evident logs, clock synchronization
• Auditability — immutable event logs and verifiable credential proofs

Step 11 — Data carrier and user experience

QR scanning must be fast, resilient, and predictable:

• Digital Link routing with locale negotiation and device‑appropriate views
• Offline snapshots for critical safety information
• Graceful degradation and long‑lived redirects if product URLs change
• Accessibility — WCAG compliance, simple labels, clear disclosures

Step 12 — Migration and incremental rollout

Start with a Minimal Viable Passport for a single category, then expand:

• Phase 1 — product identity, public attributes, basic certificates
• Phase 2 — BOM summary, repairability, recyclability guidance
• Phase 3 — lifecycle events, professional data, authority interfaces
• Phase 4 — verifiable credentials, partner integrations, analytics

Reference schema — practical examples

Product master (JSON‑LD)

{
“@context”: [
“https://schema.org/”,
{ “gs1”: “https://gs1.org/voc/” }
],
“@type”: “Product”,
“gtin”: “04012345123458”,
“name”: “Model X — Smart Washer 700”,
“brand”: “Acme”,
“model”: “SW-700”,
“category”: “household-appliance”,
“manufacturer”: {
“@type”: “Organization”,
“name”: “Acme Appliances GmbH”,
“leiCode”: “529900T8BM49AURSDO55”
},
“digitalLink”: “https://id.acme.example/p/04012345123458”,
“dpp”: {
“version”: “1.0.0”,
“status”: “active”,
“publicAttributes”: [
“safetyInstructions”,
“repairabilityScore”,
“energyLabel”
],
“restrictedAttributes”: [
“disassemblySteps”,
“partsCatalog”
]
}
}

Serialized item and lifecycle link

{
“itemId”: “04012345123458.1234567890”,
“scheme”: “sgtin”,
“batch”: “B24-10-001”,
“manufactureDate”: “2025-09-22”,
“facilityGLN”: “1234567890123”,
“events”: [
“urn:epcis:event:3f4a…”,
“urn:epcis:event:7c21…”
]
}

EPCIS 2.0 event (transformation example)

{
“type”: “TransformationEvent”,
“eventTime”: “2025-09-23T10:11:12Z”,
“inputEPCList”: [“urn:epc:id:sgtin:4012345.012345.1234567890”],
“outputEPCList”: [“urn:epc:id:sgtin:4012345.012345.2233445566”],
“bizStep”: “urn:epcglobal:cbv:bizstep:assembly”,
“disposition”: “urn:epcglobal:cbv:disp:in_progress”,
“readPoint”: { “id”: “urn:epc:id:sgln:1234567.00001.0” },
“bizLocation”: { “id”: “urn:epc:id:sgln:1234567.00001.0” },
“ilmd”: {
“acme:processVersion”: “2.7.1”,
“acme:operatorId”: “EMP-9982”
}
}

Certificate as a Verifiable Credential

{
“@context”: [
“https://www.w3.org/2018/credentials/v1”,
“https://example.eu/credentials/conformity/v1”
],
“type”: [“VerifiableCredential”, “ConformityCertificate”],
“issuer”: “did:example:nb-12345”,
“issuanceDate”: “2025-10-01T00:00:00Z”,
“credentialSubject”: {
“gtin”: “04012345123458”,
“category”: “household-appliance”,
“standards”: [
{ “ref”: “EN 50564”, “version”: “2011” }
],
“validUntil”: “2028-10-01”
},
“proof”: {
“type”: “Ed25519Signature2020”,
“created”: “2025-10-01T00:00:00Z”,
“verificationMethod”: “did:example:nb-12345#keys-1”,
“proofPurpose”: “assertionMethod”,
“jws”: “eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9…”
}
}

Access policy — field‑level control (conceptual)

{
“resource”: “dpp:04012345123458”,
“rules”: [
{
“effect”: “allow”,
“fields”: [“public.*”],
“condition”: “audience == ‘public'”
},
{
“effect”: “allow”,
“fields”: [“restricted.disassemblySteps”, “restricted.partsCatalog”],
“condition”: “role in [‘authorized_repairer’, ‘manufacturer’]”
},
{
“effect”: “deny”,
“fields”: [“confidential.*”],
“condition”: “audience != ‘authority'”
}
]
}

Public vs restricted vs confidential — quick mapping

 

Centralized vs federated vs decentralized — which pattern fits

• Centralized — one platform stores and serves all passport data. Simple to operate, faster rollout, but higher concentration risk and potential vendor lock‑in.
• Federated — each operator hosts its data; a resolver composes a passport view. Better data sovereignty and scalability; requires strong discovery, policy, and caching.
• Decentralized credentials — authoritative facts are issued as verifiable credentials and verified at read time. Strong authenticity and portability; more complex key and revocation management.

Most enterprises start centralized for speed, then evolve to federated with credentials for high‑assurance facts.

KPIs and controls that matter

• Coverage — % of SKUs with DPP, % of serialized items linked
• Freshness — median passport update latency after product change
• Data quality — required field completeness, schema conformance rate
• Access — P95 QR resolve time, authorization error rate, offline fallback rate
• Assurance — % of credentials verified, audit findings closed on time
• Circularity — repair rate uplift, components reused, EoL capture rate

Common pitfalls — and how to avoid them

• One giant document — split into composable resources with field‑level policies.
• Ignoring standards — map early to GS1 Digital Link and EPCIS to avoid rework.
• Over‑publishing — leakage of IP or personal data; apply tiering and minimization.
• No lineage — auditors will ask “who said this, when, and under which method?”
• Heavy payloads — slow scans and timeouts; keep public views lightweight with just‑in‑time expansion.

Implementation roadmap — 120‑day starter plan

1) Weeks 1–3 — scoping and discovery: categories, stakeholders, attributes, risks. 2) Weeks 4–6 — data model v1, JSON‑LD context, identifier strategy, policies. 3) Weeks 7–9 — API and resolver services; basic Digital Link and QR routing. 4) Weeks 10–12 — MDM onboarding, content localization, certificate ingestion. 5) Weeks 13–16 — pilot launch on one product line; monitoring and KPI baseline. 6) Weeks 17–18 — hardening: access controls, signatures, audit logging. 7) Weeks 19–20 — rollout to second category; add lifecycle events and pro data.

FAQ — quick answers for decision‑makers

• Do we need to serialize every item? Not always — some categories work at batch/lot level, but serialization improves traceability and repairs.
• Is a blockchain required? No — verifiable credentials and signed events provide authenticity without on‑chain storage.
• Can we reuse product content systems? Yes — integrate via data contracts; treat DPP as a governed product data exposure.
• How do we handle recalls? Model authority‑visible lifecycle events and push recall notices via webhooks and public flags.
• What about GDPR? Design to avoid personal data in DPP; where unavoidable, apply legal basis, retention, and DSAR processes.

Summary — make compliance your product advantage

A robust DPP data model is a strategic asset — not just a regulatory chore. Center it on interoperable identifiers, lifecycle events, verifiable claims, and audience‑appropriate access. Start small with a Minimal Viable Passport, standardize your entities and APIs, and build governance that stands up to regulatory and market scrutiny.

Get these foundations right and you’ll ship faster, satisfy EU expectations, and unlock real circularity value — all while aligning GovTech and LegalTech integration with your broader Data Compliance & Strategy.