Supplier onboarding should be predictable: ask for and receive the data, validate it, publish it. Simple. However, in the real commercial world, it’s often a slow and frustrating grind. Teams chase down suppliers for missing attributes, inconsistent formats, and images which don’t meet minimum requirements. Weeks pass. Launch dates are missed. Sales opportunities go by the wayside. Everyone blames “supplier delays.”
However, most of the time, the root cause isn’t supplier speed. It’s broken product structure.
When your product data model, templates, and onboarding workflow are unclear, ambiguous, or inconsistent, suppliers can’t be expected to know what the threshold for “good” looks like. They guess. They improvise. They send what they have and, every time, your teams clean it up. And so it goes… the onboarding cycle stretches from days into weeks or even months.
The hidden cost of long onboarding cycles
Every extra day spent onboarding is a day products aren’t on sale. At scale, that delay compounds across hundreds or thousands of SKUs:
- Lost sales from delayed launches
- Slower category expansion and assortment growth
- Reduced agility when consumer trends shift
- Increased manual effort in merchandising and data teams
- Frustrated suppliers stuck in “onboarding limbo” with no clear feedback
Long onboarding cycles have generally been treated as inevitable. But they aren’t. They’re a symptom of structural issues in how product data is defined, communicated, and governed.
The core issue: suppliers don’t know what “good” looks like
Suppliers don’t want to provide bad data. Give them a helping hand. “Good” is not a universal standard, but your standard. If you don’t make it explicit, what quality really means becomes no more than guesswork on their part.
Here’s what these remedies look like in practice.
1) Vague or inconsistent templates
If templates vary by category without clear logic—or if attributes aren’t defined—suppliers interpret them differently.
“Colour” becomes “Blue,” “BLU,” “Navy,” or “#0000FF”.
“Material” becomes “Cotton”, “100% Cotton”, or “Cotton Blend”.
If that ambiguity is multiplied across dozens (or hundreds) of fields, of course you get inconsistency. That breaks filters, complicates search, and forces manual normalisation.
2) No single source of truth for requirements
Many businesses are still operating with outdated PDFs, email threads, or legacy spreadsheets. Consequently, suppliers receive conflicting versions and don’t know which is actually correct. Without an authoritative specification, onboarding becomes subjective: whoever shouts loudest or replies fastest wins.
3) No examples of high-quality data
Suppliers rarely get to see what “excellent” looks like in your context. That means elements like:
- How titles should be structured
- How attributes should be formatted
- How images should be named
- What level of detail is expected
Without examples, they naturally default to their own internal standards, and you’ll be lucky if they happen to match yours.
4) No validation and weak feedback loops
If suppliers only discover errors after submitting a full dataset, onboarding turns into a game of spreadsheet ping-pong:
- Supplier submits a file
- Merchant rejects missing/incorrect fields
- Email thread explaining why “10 cm” isn’t the same as “0.1 m” in your system
- Supplier resubmits, and then…something else is missing
Rinse and repeat until both sides are exhausted and the product goes live anyway, with compromised content.
5) Overly complex or irrelevant requirements
Sometimes the problem isn’t too little structure – it’s too much. If a business uses bloated templates full of irrelevant attributes, it simply overwhelms suppliers and often leads to incomplete submissions. When everything is mandatory, nothing appears to be prioritised.
The knock-on effect inside your business
When suppliers aren’t able to see what “good” data looks like, there’s a ripple effect across teams.
- Merchandising spends time cleansing data instead of building category strategy.
- eCommerce waits for complete data before pages can be built, delaying launches.
- PIM / data governance can’t enforce standards because the standards are unclear or inconsistently applied.
- Customer experience suffers when inconsistent data makes it through: poor filtering, missing specs, confusing content, avoidable returns.
A broken product structure doesn’t just slow down the onboarding process – it ends up undermining your entire product data ecosystem.
What “broken structure” really means
When we talk of “Broken structure” we’re not referring to one single failure. Usually, it’s a combination of three gaps in the structure:
Taxonomy turmoil
When categories are poorly-defined, suppliers can’t reliably classify products. A “running shoe” ends up under “Footwear” for one supplier and “Sporting Goods” for another. Without clear data-mapping, your catalogue becomes inconsistent, your internal teams have to spend time correcting classification rather than scaling onboarding.
Attribute ambiguity
If attribute requirements aren’t clear and fail to be enforced at the point of entry, products may arrive with critical specs missing. And that creates issues downstream:
- weak filters on Product Landing Pages
- customer questions due to uncertainty or/and confusion
- Constant need for rework before publishing
Validation voids
If there’s an absence of automated validation checks (for elements like format, allowed values, mandatory fields, or image specs), it has to be humans who become the ‘validation layer.’ Rather than using their full range of skills for enhancing product information, your teams have to function as “data janitors”: Manually cleaning and reformatting supplier spreadsheets line by line.
What ‘good quality’ looks like: the foundations of fast onboarding
Getting your onboarding right isn’t about putting ever greater effort into chasing suppliers. It’s about building the structure on a foundation of clarity so that suppliers can get it right too – at the first time of asking.
1) A coherent product data model
Your taxonomy, attributes, and relationships should be logical and consistent, as well as being aligned with how customers shop and how channels consume data. And these factors aren’t static, so it isn’t a case of ‘fix it once and we’re done.’
2) Supplier-friendly templates with unambiguous definitions
For every attribute that matters, suppliers need:
- A clear definition (what it means)
- Allowed values or formats (including units of measure)
- Examples of correct submissions
- A clear rule for mandatory vs optional fields
This is how you move from “send product details” to a genuinely useful definition of “ready.”
3) A single, authoritative source of truth
Suppliers should always know where the current requirements should live – that means one place, one version. Whether it’s a portal, a structured hub, or system-driven documentation, the objective is straightforward: remove ambiguity and eliminate the risk of version drift.
4) Validation with immediate feedback
Suppliers should know instantly if something is missing or incorrectly formatted, certainly before it hits your internal teams. This will shorten onboarding cycles by eliminating practically all that avoidable back-and-forth with them.
5) Category-specific “gold standard” examples
Demonstrate what your “high-quality” looks like, per category: The fundamentals, like
- Titles
- Imagery
- Key attributes
- Formatting
Don’t just assume suppliers will infer your expectations. Take nothing for granted!
6) A repeatable onboarding workflow
Clear steps. Clear responsibilities. Clear SLAs. Clear visibility. Suppliers should never be left in a ‘status void.’
When structure is strong, onboarding cycles compress from weeks to days, not because suppliers magically got faster overnight, but because the system stopped forcing them to use guesswork.
Final words: Auditing your structure
If supplier onboarding is dragging down your business performance (as well as your people’s morale), start with a structure audit. Contact us today at Start with Data to request one. We have a wealth of experience reviewing product data management: Taxonomy, attribute requirements, templates, validation rules, and workflow handoffs. We can then identify where suppliers are being forced to guess, and where your teams are wasting time on carrying out avoidable clean-ups. You’ll get a prioritised set of fixes that reduce cycle time without a risky redesign.