A product data quality assessment should take three to six weeks and end with three things. A scored picture of one part of your catalogue. A named mechanism behind each failure. A costed, sequenced remediation plan you can take to a budget holder. If it ends with a dashboard and no plan, it was an expensive way to confirm what everyone already suspected. This is the method we run, written out in full so you can run it yourself.
Search for data quality dimensions and you get six words: completeness, uniqueness, timeliness, validity, accuracy, consistency. That list is real, it has a proper source, and it was written for customer and transaction records rather than for catalogues. Applied to a product record without translation it produces scores that look precise and change nothing. This page does the translation.
Product data enrichment is the work of turning a supplier’s raw record into something you can actually sell from. A part number, a trade price and forty words of manufacturer prose go in. A classified, attributed, described and illustrated product record comes out. That is the whole job. What varies enormously is how much of it a given catalogue needs, which is why nobody will quote you a price over the phone.
Most product data business cases die in the same meeting. They are written as a data project, so the finance director reads a cost with a vague benefit attached. The ones that get funded read as a change to three or four lines in the profit and loss. A measurement plan is bolted on. This article gives you that structure. The value lines that survive scrutiny, the ones that do not, the inputs to gather, and the eight objections you will get.
We sell outsourced enrichment, so treat what follows with the scepticism it deserves. We also turn work away roughly once a quarter. The honest answer to in-house vs outsourced data enrichment is that neither model wins outright. It depends on how specifiable your work is, how steady the volume is, and whether the knowledge involved is worth keeping inside the building. Here is the comparison we actually use when a Head of Product Data asks us which way to go.
Ask three suppliers for a product data enrichment cost and you will get three numbers that cannot be compared. One quotes per SKU. One quotes a day rate. One quotes a fixed price for a batch of ten thousand lines. None of them state what is inside the price, so the cheapest quote is usually the one with the smallest scope hidden in it. This article gives you the cost model instead of a headline rate, because the model is the thing you can budget against.
Most AI visibility tools give you a score out of a hundred and no way to act on it. The useful version of this exercise is different. You define a fixed panel of buying questions, run it across the assistants your buyers use, and record the whole answer each time. Then you read the citations rather than the score. This is the method we use, written out in full so you can run it yourself.
Ask an assistant which product to buy and it does not consult one ranked list. It rewrites your question into several queries. It pulls from a live search index and a merchant feed at once, then writes an answer from both. The products it names and the sources it links are chosen by two different mechanisms. Understanding how AI assistants choose products matters, because most catalogue owners are working on the wrong one.
Almost everything written about LLM optimisation is about blog posts. Write clearly, use headings, answer the question early. Fine advice, and useless if your problem is 400,000 SKUs where the torque rating lives in the middle of a paragraph. Product catalogues fail machine reading for structural reasons, not stylistic ones. This is what those reasons are, and what you change in the data model to fix them.
Ask an AI shopping assistant for a cordless impact driver under two hundred pounds with a brushless motor, and it will give you five products. Your competitor is in that list. You are not. Nothing is broken on your site. Nobody has penalised you. The assistant simply could not establish that your product met the conditions, so it moved on. Here are the six reasons that happens, and what each one looks like inside a real catalogue.
Most AI readiness assessments are workshops. Someone scores your organisation out of five on culture, skills and governance, then hands over a heat map. It tells you nothing about whether an assistant can answer a question about your products. This diagnostic does. It runs against your own catalogue. It takes five working days of one person’s time. It ends with a number you can put in front of a board.
Most articles about the digital product passport explain the policy. The policy is the easy part. A digital product passport is a structured data record. It ties to a product through an identifier and a data carrier. It holds fields you almost certainly do not have, at a level of identity your PIM was probably not built for. That is the part that costs money, and it is the part nobody prices.
From 18 February 2027, an electric vehicle battery cannot be placed on the EU market without a battery passport. The same goes for an e-bike battery, and for any industrial battery above 2 kWh. That date sits in Regulation (EU) 2023/1542, and the European Commission’s Digital Product Passport Registry has been live since 20 July 2026. This is the first digital product passport that will actually bite. For almost every distributor and manufacturer we speak to, it is a data problem long before it is a compliance problem.
ESPR is the Ecodesign for Sustainable Products Regulation, and it is the law that creates the Digital Product Passport. It entered into force on 18 July 2024 and it applies to almost every physical product sold in the EU. There are plenty of legal summaries of it. There are almost none that answer the question a product data team actually has: which attributes, at what granularity, held in which system. That is what this piece covers.
Most DPP readiness assessments are questionnaires. They ask whether you have a sustainability strategy, score you out of five, and produce a chart. That tells you nothing you can act on. The only useful test is run against your actual product records, field by field. It takes an afternoon. Below are the twelve checks we run on a client catalogue. Each one names the data it tests and what a fail looks like.
Most catalogues we open have four identifiers in one column. A GTIN is the GS1 number that identifies a trade item, and EAN and UPC are older names for two of its formats. An MPN is the manufacturer’s own part number. An ASIN belongs to Amazon and identifies a listing, not a product. Here is what each one is, who issues it, and where it belongs in your data model.
A supplier sends you a BMEcat file. It is 300MB of XML, half the descriptions are in German, and nobody can tell you whether it is version 1.2 or version 2005. BMEcat is the XML standard for exchanging product catalogues between suppliers, distributors and procurement systems, published by BME e.V. in Germany. If you buy from continental European manufacturers, you will meet it, and the English-language explanations of it are thin.
A GTIN identifies your product. It does not describe it. We watch suppliers discover that difference every year, usually the week a retail customer accepts the barcode and then rejects the listing. GS1 standards are excellent at the job they were built for. Selling the product is not that job.
A UNSPSC code tells a procurement system what kind of thing was bought. It tells a customer nothing at all about the product. That distinction is where most UNSPSC work quietly goes wrong. We map UNSPSC for distributors and manufacturers most years, usually alongside ETIM or a merchandising tree.
ECLASS, ETIM and UNSPSC get shortlisted against each other in the same meeting, and they should not be. They do three different jobs. ECLASS describes what a product is made of and how it performs, across every industry there is. ETIM does the same job in more depth for technical products in a narrower set of sectors. UNSPSC puts a code on a line of spend and stops there. When distributors ask us which one to adopt, the answer is usually two of them, for two different reasons.