There are a multitude of businesses, both B2C and, especially, B2B, who rely on industry standards for a substantial part of their product data. Having said that, standards like ETIM, ECLASS, GS1, UNSPSC, ISO 8000, and other sector templates can stabilise operations but also may create new friction. The key difference is how you apply them.
Our article shows where standards genuinely improve onboarding, compliance, and data quality, and where they damage customer experience, slow change, and increase the need for rework. Our information will help you to be able to diagnose the typical failure modes and put a practical ‘map, not mirror’ approach in place.
The core failure: treating a standard as your product model
The data failure usually isn’t down to the standard itself. It’s a question of governance and modelling. Business teams tend to adopt a standard and assume it will replace decisions about taxonomy, attribute definitions, variant logic, and channel rules.
It won’t, and the operational consequence is predictable: supplier files still arrive with inconsistencies, teams still have backs and forths about meaning, and eCommerce teams can’t get their products live without going through manual fixes.
The impact on the business is equally predictable: slower onboarding, poorer discoverability, higher returns rates, channel rejections due to non-compliance, and endless delays in meeting regulatory demands (including sustainability reporting and Digital Product Passport-style requirements).
When standards help (and why distributors benefit)
Standards help most when they are used as an input to a data governance framework, and interchange, but not as part of the front-end structure.
1. Interoperability and syndication
If you trade with multiple suppliers and multiple customers, you need a shared baseline for identifiers, units, and attribute meaning. GS1 identifiers (GTIN), standard units of measure, and standardised attribute names reduce the ‘lost in translation’ problem, mapping as they do across ERP, PIM, WMS, marketplace feeds, and customer procurement portals.
2. Regulatory compliance and risk control
Sector mandates (for example labelling, safety, chemical compliance) and broader rules like UK GDPR for connected products, oblige businesses to capture and retain specific data points with traceability built in. Standards also provide a defensible minimum set of required attributes, permitted values, and documentation expectations. Overall, these factors reduce the risk of selling non-compliant products, failing audits, being forced to delist, or suffering product recalls.
3. Data quality via validation
A given standard can be used to drive validation rules: mandatory fields, valid ranges, controlled vocabularies, and unit constraints. This is where ISO 8000-type quality principles have significance: a value is only ‘present’ if it is correct, consistent, and usable. Catching avoidable errors at ingestion prevents downstream rework.
4. Faster supplier onboarding
Distributors feel the benefits of this immediately. When supplier templates align to a standard (plus your own rules), ingestion can move from weeks of chasing down missing information to repeatable loading, automated mapping, and approval gates. Faster onboarding increases sellable range and reduces backlog
When standards hurt (and why purchasers notice the pain)
Standards harm businesses when they try to force them into roles they weren’t designed to fulfil.
1. You use a technical classification as your eCommerce taxonomy
Standards like ECLASS and UNSPSC are built for classification and procurement systems, not for human browsing. When you mirror that hierarchy on your website, buyers can’t find products quickly. Filters may feel counter-intuitive because they’re reflecting engineering logic, not how the modern buyer searches and compares. In terms of operations, this leads to increased volume of support calls and more abandoned carts – lost orders and, more seriously in the longer term, fewer repeat purchases.
2. You import the entire attribute library without selection
The aim of standards is to cover an industry, but your catalogue clearly isn’t as far-reaching. If you ingest every possible attribute, you’re only creating bloated item records, inconsistent completion, and confusing enrichment queues. The risk is that teams spend time populating fields without any channel value. while missing fields that do have value.
3. You lock your change process to committee pace
Standards evolve relatively slowly, but your commercial needs don’t. That means if you need to rework your standard mapping every time a new attribute or product type needs it, you create a bottleneck – a rod for your own back. Associated costs show up in delayed campaigns, slower range expansion, and missing customer requirements (with the knock-on of higher returns, more customer service complaints, and the aforementioned ‘desertion. Of your brand during the customer journey.
4. You assume that standards automatically solve governance
A standard lists product attributes. What it doesn’t do is assign ownership, define approval workflows, or enforce consistency. Without the requisite ownership and rules, the concept of ‘standardised’ ends up becoming a range of inconsistent interpretations.
Use case 1: when the seller helps the purchaser (good use)
To exemplify these factors, let’s take two examples – one of good practice, one not.
A UK industrial distributor sells fasteners and MRO (maintenance, Repair and Operations) parts to large manufacturers. Customers demand procurement-ready data: consistent units, clear dimensions, compliant safety documentation, and reliable identifiers.
The distributor sets a two-layer model in the PIM:
- Master record (standardised): ECLASS/ETIM classifications, GS1 identifiers where applicable, controlled units, and required technical attributes.
- Channel layer (customer-centric): customer-facing categories, simplified filter sets, and customer-specific views (for example: “fits machine family X”, “approved for plant Y”, “pack size for line-side use”).
Supplier onboarding uses a template aligned to the master record, with validation rules (mandatory dimensions, unit constraints, controlled vocabularies) and an approval gate before publishing. Purchasers benefit because the data lands in their procurement tools cleanly and the distributor’s site remains easy to search. Rework drops. Order accuracy improves.
Use case 2: when the seller hinders the purchaser (bad use)
A distributor adopts UNSPSC, a UN-backed open, global, multi-sectoral standard used to classify goods and services. It mirrors the information as website navigation. The hierarchy becomes deep and jargon-heavy. Filters expose procurement codes instead of buyer language. Variant logic is forced into the standard structure, so related items end up being split across categories.
Purchasers can’t locate compatible equivalents quickly, so they either phone the branch impatiently or go elsewhere. Internally, eCommerce can’t add practical attributes (such as suitable for washdown areas, or quick-fit) because these aren’t in the standard’s attribute library. Use of this standard then becomes an excuse for poor findability and slow improvements. Operational effort increases, and revenue declines.
The best corrective approach: stabilise, standardise, enforce
Stabilise: Closely define your internal taxonomy, attribute definitions, and variant rules based on how you sell and how customers actually buy. Set data owners and approval gates.
Standardise: Select the parts of the standard that add value (classification, units, identifiers, regulated attributes). Create mapping rules from supplier inputs to your master record and then maintain a translation layer from your model to customer and channel formats.
Enforce: apply a validation layer at ingestion (supplier templates, portals, automated checks), enforce completion thresholds for publish, and monitor for exceptions. Ultimately, external standards will only work if you embed them in operating practices.
Next steps
If you wrestling with a particularly challenging product category (with complex variants, a heavy compliance load, or inconsistent supplier feeds), Start with Data can create an example schema to prove the best-practices model:
Ø Category-specific attributes
Ø Definitions
Ø Controlled vocabularies
Ø Validation rules
Ø Approval gates
That artefact can become the template from which you scale across adjacent categories. Review here how SKULaunch supports mapping, validation, and publish gates. If standards are causing you as many headaches as they solve, reach out to us today to arrange our example schema. That’s the first step to implementing a translation and enforcement layer so that you can meet necessary standards without having to sacrifice buyer experience and lose sales and credibility.