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Product Data Done Right: From Chaos to Clarity

April 2026 8 min read
You have thousands — perhaps hundreds of thousands — of product SKUs. They live across multiple systems, each with its own category labels, and none of them align. When a buyer searches your catalogue, they can’t find what they need. When leadership asks ‘how much are we spending on bearings?’, the answer takes a week of manual reconciliation.
This is a product classification problem, and it’s one of the most common — and solvable — challenges in product data management. The solution lies in adopting a standardised classification scheme and applying it consistently across your catalogue.

“Classification tells you what type of product something is. Identification tells you exactly which product it is. A good product data strategy needs both — but classification is the foundation that makes everything else work.”

Classification vs. identification: a critical distinction

Product classification and product identification serve fundamentally different purposes, and conflating the two is one of the most common mistakes in master data governance. Classification organises products into hierarchical groups — from broad segments down through families, classes, and commodities — enabling spend analysis, search, and procurement workflows. Identification, by contrast, is non-hierarchical: it pinpoints a specific product through a manufacturer's brand name and part number combination, linked to defined property values and units of measurement.

How classification levels work — a real example

How “Roller Bearings” sits within the UNSPSC classification hierarchy, moving from broad to specific:
Level 1
Manufacturing Components
31000000
Level 2
Bearings & Gears
31170000
Level 3
Bearings
31171500
Level 4
Roller Bearings
31171505
Each level narrows the product group — you choose the depth that fits your needs.

Anatomy of the major classification schemes

There are several established classification schemes, each designed for different purposes. The three you’ll encounter most often in industrial and procurement contexts are UNSPSC (widely used for spend analysis and procurement), ECLASS (strong in engineering and manufacturing, with rich product properties), and the WCO Harmonized System (required for international trade and customs). Your choice depends on what you need classification to do for your business: enable catalogue search, support spend analytics, comply with trade regulations, or facilitate data exchange with suppliers and customers.

Your choice depends on what you need classification to do for your business. Many organisations map products to multiple schemes simultaneously — UNSPSC for procurement analytics and the Harmonized System for customs compliance, for instance. The key is having a single, well-governed source of product data from which different scheme mappings are derived.

Common pitfalls and the right approach

The problem

Mapping too deep, too early

Teams jump straight to Level 4 across all categories, spending months on classification when Level 2 or 3 would answer their questions.

The approach

Start with the question

Define the business questions first, then choose the level of depth that answers them.

The problem

Ignoring version changes

Schemes update annually. New codes appear, old ones are deprecated. A code without a version reference can mean different things to different recipients.

The approach

Version everything

Always include the scheme version when storing and transmitting codes. Agree on versions with trading partners upfront.

The problem

Classification without identification

Two "spherical roller bearings" from different manufacturers with different specs look identical in the system.

The approach

Pair with product properties

Classification says what kind of product; identification says which product exactly. You need both layers.

Deciding the right level of mapping

1

Multi-scheme support. Your solution should handle UNSPSC, ECLASS, ETIM, and the Harmonized System natively — not require a separate project for each.

2

Version management. Your platform should track versions, manage migrations, and flag deprecated codes automatically.

3

Scalability. A solution that works for 5,000 SKUs but buckles at 100,000 isn't ready for production.

4

Standards-based architecture. Look for ISO 8000 compliance — a recognised framework that ensures classified data is interoperable and auditable.

5

Property-level enrichment. Can the solution attach standardised technical properties (dimensions, materials, tolerances) to each classified product?

6

Multi-language capability. If you operate internationally, classification needs to work across languages with semantically equivalent terminology.