When businesses select and buy a PIM, it frequently leads to one entirely preventable way that the system appears to underperform. These organisations treat the software decision as the starting line in the race to commercial success but then discover mid-way through implementation that their product data has no stable shape. The platform does go live, but the degree of user adoption remains sluggish, because every step in the workflow triggers discussions (even arguments) about “what’s correct” regarding categories, attributes, and sources.
This ostensibly counter-intuitive situation isn’t a problem connected to software tooling. It’s a problem with sequencing in the implementation process. If the business hasn’t clearly defined (for everyone) what ‘good’ means for product data in relation to channels and operating model, the new PIM will only industrialise this confusion.
At base, a PIM is not meant to be a data cleansing tool. It should be used as a management layer which enforces structure, routes work and efficiently distributes what you give it. If you load it with bad data, that data won’t get fixed inside it. On the contrary, it just multiplies.
The ‘rework trap’: what a ‘software first’ strategy really buys you
If you choose a PIM without having stabilised your product data, it obliges the implementation team to build the chosen model around whatever is currently easiest to extract rather than what the business actually needs in order to syndicate its product information to channels. That’s a fateful decision because it ends up causing a whole heap of avoidable rework further down the road:
- A schema built on guesswork. Attributes, variants, and category logic are encoded before the business has actually agreed definitions. Consequently, six months later, you discover to your dismay that the schema structure doesn’t support how you sell.
- Mappings become easily compromised. If your upstream sources aren’t consistent, integrations get engineered to manage exceptions, and once you do get round to standardising your product data, those mappings will need to be redesigned anyway.
- Workflows become persistent organisational politics issues. Areas like approval steps or ownership rules have been designed to compensate for the missing standards (thus: “someone has to check this manually”). The result? Automated disagreements on ‘the single source of truth.’
- Training principles are built on shifting sand. Business users end up being trained on a model that doesn’t reflect their reality, so naturally revert to spreadsheets to get work done and basically make life easier for themselves (as they mistakenly see it).
All the above is why the wrong sequencing for a PIM integration project always ends up inflating costs and extending time – and there’s no noticeably improved outcomes to boot. The business hasn’t bought the wrong tool. It’s just bought that tool at the wrong moment.
When you really must fix your data before selecting
To be clear, we’re not saying it’s a case of having to cleanse every SKU to be ‘ready.’ However, you do need enough clarity for the PIM configuration to be anchored to facts, as opposed to wishful thinking. Therefore, if you experience two or more of the following issues, fix your data first!
1) Your taxonomy is contested or outdated
If teams still feel the need to debate where exactly products belong, or your category tree reflects a convenience built for internal operations rather than how customers browse, jumping into selecting a PIM runs a significant risk of locking in a structure your people then have to spend years working around.
Tell-tale signs: persistent use of ‘temporary’ categories, endless reclassifications, filters which don’t match customer search language.
2) You can’t define a small set of non-negotiable attributes
If you can’t name the minimum attribute set needed to list your products credibly across your highest-priority channels, you’re not in a position to design validation, enrichment, or ownership. The PIM thus becomes a repository for partially-complete records.
Tell-tale signs: the concept of ‘complete’ data means different things to different teams, so practically all launches depend on manual spot checks.
3) The same concept exists in multiple fields
Colour vs Colour name vs Primary colour vs Color
Size vs Pack size
Weight vs shipping weight.
All these are examples of duplicate semantics. Small as they are, these still create endless reconciliation work, apart from destroying any trust users might have in the principle of ‘Single Source of Truth.’
Tell-tale signs: Reports never match; Teams argue about which field is “the real, genuine, definitive version.”
4) Supplier data is your main input and arrives in many formats
When suppliers send inconsistent templates and naming conventions (and they surely will, as you know!), the PIM won’t magically solve that problem by itself. You need to establish standards, templates, and onboarding rules before you choose tooling. Otherwise, you’re simply baking supplier data inconsistencies into your model.
Tell-tale signs: Excessive need for manual rekeying, lots of ‘copy-paste enrichment,’ and a chronic problem with missing specs or digital assets like imagery.
5) You don’t know where the authoritative truth lives
If your ERP, eCommerce platform, DAM, and spreadsheets don’t tell the same story, and nobody can say which version wins for which attribute, again, you’re not selecting a PIM but choosing a place where unresolved conflicts are housed.
Tell-tale signs: “The latest” depends on who you ask; Various teams keep local files “just in case.”
6) You haven’t measured completeness and consistency
If you can’t state (even roughly) what percentage of core products meet a sellable threshold, you can’t calculate the workload for enrichment or be in a position to choose a tool which matches it.
Tell-tale signs: The PIM implementation plans are solely based on SKU counts, not on the effort required for getting product data in shape.
What ‘data first’ really means (without needing to boil the ocean)
“Let’s fix the data” isn’t a six-monthly ‘Spring-clean’ of every single record. Cleansing needs to establish a sustainably stable standard for publishing product content:
- A documented taxonomy for priority categories, aligned to each channel’s stipulations.
- A golden attribute list (up to 15–20 fields in many cases) with definitions, formats, and allowed values.
- Normalisation rules for units, naming, and key enumerations (such as colour values).
- Source-of-truth decisions by attribute: Specifically, where each field comes from and who owns it.
- Supplier specifications and templates for inbound data, plus a defined onboarding process.
- A ‘readiness toll booth’ so that incomplete products cannot flood the new system from day one.
You can do this in a staging environment or structured catalogue file, and the whole point is to remove ambiguity before it becomes configured.
It’s commercial logic: “Data first” reduces costs and accelerates time to value
Data clarity strips a lot of risky variables out of implementation. If you can confirm validated structures and standards, customisation is reduced, there’ll be far less rework, and the time between go-live and ‘actually being used’ should close.
Most importantly, you avoid the trap of trying to use software to force an organisational square into a round hole. Software tools are good at enforcing standards. What they can’t do is invent those standards.
Fix the truth-shaped gap, then decide on the tool
It’s essential to get your product data into a fit state before choosing a PIM when your product truth still requires negotiation:
- Contested taxonomy
- Undefined attribute listing and normalisation
- Unclear data sources
- Inconsistent supplier inputs
- Completeness as a moveable feast
Not dealing with the above simply hard- codes uncertainty into the operating model, guarantees rework and, most harmful, impacts your CX and bottom line.
Book a discovery call
If you want to avoid the traps so many others have fallen into, and select a PIM based on your commercial reality rather than demos, contact us today at Start with Data to book a discovery call. We’ll scrutinise your circumstances and quickly identify whether your data standard is sufficiently stable to embark on a well-informed PIM selection process, or whether pivoting to a “data first” strategy is the wisest, cheapest, and fastest next step.