What is GDSN and How Does It Work – A Practical Guide

Start today by mapping your product data to the GDSN standard and enrolling in a data pool. This move reduces errors across the supply chain and creates a single source of truth that travels with your products throughout industries. If you already use salsify, you can link your catalog to the GDSN pool to sync records efficiently.

The GDSN framework has the largest components you will manage: the data pool, the product classification, and the attribute sets. Each component acts as a validation layer so your team keeps records organized and ready for audits. The standard itself defines data elements you have entered, so you can map them once and reuse them across partners.

Through the process, data quality checks run automatically, and updates you enter are validated before publication. Publish synchronization across trading partners happens in near real-time, which keeps product information accurate and reduces recalls. Use your data governance plan to document who updates which fields and how often.

To seize opportunities, keep data organized with consistent naming, unit conventions, and attribute values. This approach helps retailers, manufacturers, and distributors stay aligned across peaks of demand. It also makes onboarding new suppliers faster and strengthens compliance during audits.

Practical steps you can take this quarter: assign a GDSN owner, schedule quarterly data health checks, and set up automatic validation rules in salsify or your chosen data pool. Track metrics such as data completeness, accuracy, and time from entry to publication to quantify value and justify investments. As you grow, the GDSN process scales with you, keeping your catalog consistent as your product lines enter new markets.

GDSN in Practice: Foundation, Data Flows, and Real-World Gains

Begin with entered data in your GDSN pool and enhance your interface to manage manufacturers and suppliers.

Underneath the surface, the basic foundation rests on a standard data model that includes product identifiers, attributes, and traceability fields, including beverage SKUs, so partners across networks see consistent information.

Data moves from data entered by manufacturers and suppliers into the pool, then direct distribution through networks to partner interfaces where they access the shared records, and the system validates entries to keep your datasets aligned.

Real-world gains include faster onboarding, improved traceability, and streamlined transport planning; the pool provides end-to-end visibility for partners, and suppliers and manufacturers report fewer duplicate efforts across networks.

Define core data elements and mandatory attributes in GDSN records

Create a single data dictionary that defines core data elements and marks all mandatory fields for every GDSN record. This dictionary should be owned by your organisation’s data governance team and aligned with GS1 standards to ensure accurate data flows across the GDSN network from suppliers to retailers.

Core data elements span several groups: identification and description (GTIN, product name, brand, and a common description), measurements and units (net content, unit of measure, weight, and dimensions), packaging (pack level, packaging type, and hierarchy), and supplier/organisation data (manufacturer, supplier, GLN, data pool reference). Within each group, particular fields belong to the core set and repeatable across items, with common definitions and consistent values. This advanced framework supports enhancing data quality and enables faster onboarding across your digital network.

The mandatory attributes at minimum include GTIN, a clear description, brand or preferred name, unit of measure, and the data source or manufacturer. These items must be non-empty and validated against approved formats; if a field is not applicable, mark it as not required rather than leaving a placeholder, preventing inconsistencies in downstream systems.

Implement data quality controls, including value validations (numerical ranges for net weight, decimal precision for net content), controlled vocabularies for UOM and packaging types, and cross-field checks (net content versus quantity, dimensions versus packaging level). Automated checks reduce inconsistencies and help achieve enhanced accuracy, which offers a reliable foundation for automatic publishing in the digital ecosystem.

Focus on sustainability and customer satisfaction by keeping fields in the core set up to date and accurate. Accurate core data reduces waste in the supply chain, strengthens the trust of trading partners, and supports a smoother onboarding experience for new suppliers to the network.

Practical steps: map your categories to a common data model, decide which fields are mandatory for each category, build validation rules, appoint data owners in your organisation, and enforce across all trading partners. This approach speeds learning cycles, helping standardisation and reducing errors across the network.

Case example: in a case of packaging redesign, update net content, packaging code, and related measures in the core data set. The result belongs to a consistent data baseline, with fewer incidences of mismatched attributes and an improved level of satisfaction among retailers and consumers, reinforcing the value of a unified data backbone for your organisation.

Explain the GDSN data flow: from data pool to global registry and trading partners

Validate core product data in your pool first, then synchronize with the global registry to ensure alignment with trading partners. This approach improving data quality across the network and reducing fulfillment delays.

In practice, a provider enters key attributes into their pool: numbers such as GTIN or UPC, brand, description, packaging, dimensions, weight, origin, and target markets. The pool enforces validation rules, flags missing fields, and stores revision history. When implemented, these checks catch errors before data leaves the provider’s environment, improving trust with partners and consumers alike.

From the pool, data is pushed to the global registry through direct connections using apimios, or via a managed integration layer provided by a platform like salsify. The registry validates the submission, enriches records with global attributes, and propagates updates to the network of trading partners. With thousands of retailers and distributors subscribing to feeds, partners receive timely changes and maintain consistent product descriptions, images, and attributes.

Real-world changes–such as a packaging update or a nutrition declaration revision–flow as a new version in the pool, pass validation, travel through the global registry, and appear on partner catalogs. This synchronized flow improves consumer trust, enhances distribution accuracy, and reduces mismatches at the shelf where shoppers rely on accurate details.

Best practices to optimize this flow include establishing a single source of truth in the pool, implementing additional automated validation checks, and scheduling regular synchronization with the registry. Monitor numbers and error rates, maintain direct connections where feasible, and aim for enhanced data quality across the partner network to boost satisfaction for providers and their customers.

Onboard suppliers: step-by-step process to join a data pool and publish GTINs

Provide suppliers with a concise onboarding guide and fixed data schema to ensure consistency across GTINs and barcodes. This governance connects them to the pool, aligns data within the pool, and supports distribution globally through retailers and distributors.

1. Establish governance and standards
   • Assign owner(s) for data quality and a published approval workflow.
   • Define required fields (GTIN, brand, product name, description, size, unit, packaging, barcode, country, currency) and acceptable formats.
   • Set validation rules and an alternative identifier policy to handle exceptions.
2. Invite suppliers and provide onboarding materials
   • Share an organized checklist illustrating what data to submit and the timeline.
   • Explain how to submit data via form or API, and how they can learn about the pool’s governance and standards.
3. Collect and normalize data
   • Ask them to provide numbers that match field definitions; use a single data model to avoid duplicates.
   • Support various item types and packaging units; map fields to GS1 standards.
4. Validate GTINs and product data
   • Run a barcode check against the issuer database; verify check digits and numbers.
   • Detect duplicates within the pool and resolve conflicts by the governance team.
5. Publish to the pool
   • Approve records and publish them so distribution partners can see data within the pool.
   • Publishers can see where a SKU connects to shelf‑ready data in the system.
6. Train suppliers and teams
   • Provide short, practical training modules focused on data quality, field definitions, and error handling.
   • Offer quick-reference guides and an FAQ to reduce back-and-forth communication.
7. Establish ongoing communication
   • Set regular status updates, data-cleaning windows, and escalation paths.
   • Offer support channels so they can learn and resolve issues fast.
8. Connect to sides of the ecosystem
   • Ensure the pool connects with retailers, distributors, and marketplaces to enable globally distributed data.
   • Maintain a single source of truth that feeds shelf data and catalog feeds.
9. Monitor, measure, and improve
   • Track metrics: data completeness, publish latency, error rate, and approval time.
   • Take action on feedback from businesses to refine the process and fields as needed.
10. 规模化和可持续化
    • Expand coverage to new regions and product categories while keeping organized governance.
    • Update standards as needed and communicate changes to all sides of the network.

Quality checks and common data-quality fixes: mapping, clean-up, and validation rules

Begin with a firm mapping plan and automated validation to drive data quality across all data packets exchanged between manufacturers and distributors. Build a current view of each product record, so teams in marketing and supply chain communicate from a single source of truth.

Set a canonical data model and crosswalks to enable a single view of products across systems and channels. Map core attributes such as GTIN, brand, packaging hierarchy, units per package, and country of origin. Include contextual fields like regional codes and language variants, and designate owners for each mapping step to avoid inconsistencies that surface when feeds come from multiple sectors. This approach reduces the challenge of reconciling feeds from diverse sources.

Clean-up actions: trim whitespace, normalize case, standardize abbreviations, convert units to a common system, and remove duplicates. Apply de-duplication rules on product identifiers and supplier IDs to prevent conflicting records from propagating into the GDSN packet you share with partners.

Validation rules should be concrete and repeatable: require mandated fields (GTIN, manufacturer ID, packaging type), enforce length constraints, verify allowed value sets (currency, unit of measure, country codes), and run a check-digit test on GTINs. Add cross-field checks such as matching brand owner with manufacturer, and ensuring packaging level increases consistently along the hierarchy.

Address inconsistencies by surfacing them in a clear packet view for correction. Examples include mismatched manufacturer identifiers, inconsistent unit codes (e.g., KG vs. kg), missing or invalid country codes, and mismatched pack quantities across packaging levels. Use guided remediation steps and keep an audit trail to support communication with stakeholders.

Apply sector-specific rules for fields that matter most in particular contexts. For food and beverage, confirm allergen language and allergen codes; for cosmetics, ensure batch/lot traceability fields align with regulatory needs; for electronics, verify model numbers and revision codes. This tailoring enables marketers to maintain a consistent data story while preserving data integrity across distributors and retailers.

Distribute ownership and automate workflows: assign data stewards, create ticketing for fixes, and log changes. During data intake, chewing through raw feeds should stop at validation gates; if a field violates a rule, reject the packet with a helpful error message and a recommended fix. If a field like mahrukh appears, map it to the closest standard attribute or drop it, and document the decision for future reference.

Measure impact with concrete metrics: percentage completion of mandatory fields, rate of validation passes on first submission, time to resolve flagged records, and the share of records that require rework. Use these numbers to drive improvements, keep distributors and manufacturers aligned, and maintain a high view of data readiness for current campaigns and product launches. Take targeted actions once rules are in place to minimize rework.

Practical steps to implement now: run a pilot in two sectors, publish a shared mapping dictionary, and schedule weekly checks with clear escalation paths. Produce example corrected records to illustrate fixes and provide supporting guidance for data contributors. This approach reduces back-and-forth, improves data quality, and speeds time-to-market for marketing and sales teams across the world.

Track tangible gains: metrics for visibility, stock levels, and replenishment cycles

Set up an up-to-date dashboard that tracks three core metrics: visibility, stock levels, and replenishment cycles. Use this single view to drive decisions every day and promote accountability across partners.

Visibility metrics pull data from the industry-wide data exchange to show on-hand inventory by location, in-transit stock, and current stock movements. Such visibility helps you pinpoint bottlenecks, prevent stock-outs, and act quickly when delays arise.

Absolute stock levels by SKU and location reveal high-risk gaps. Track inventory by region, warehouse, and store, and flag items with low service levels or excessive carrying costs. This supports balancing promotions and replenishment decisions, improving overall inventory discipline.

Replenishment cycle metrics measure lead time, order cycle time, forecast accuracy, reorder-point compliance, fill rate, and days of supply. Use these signals to trim cycle time and ensure replenishment happens before shortages occur.

Flywheel approach: each improvement in data quality, governance, and model calibration accelerates the next improvement. By learning from current context, you can optimize the exchange workflow, transport logistics, and inventory flow, driving a steady rise in service levels.

Practical steps to implement: map every SKU to a standard unit, validate data against industry-wide standards, and enforce up-to-date attributes for ingredients and packaging. Establish an ongoing data-quality program with automated checks and periodic audits. Align partnerships on the governance framework so that every supplier contributes reliable data to the shared model.

Metrics to report and targets: service level ≥ 98%, stock-out rate below 2%, days of inventory in the 21–42 range, and forecast bias within ±5%. Create simple visualizations that highlight deviations and trigger alerts when a SKU breaches the threshold. Here is a practical example: a weekly cadence that compares actuals to forecast and flags data quality gaps for correction, with escalation to governance when issues persist.