If you’re a stakeholder with hands on the purse strings, you’ll be sure that when you sign that contract for your new PIM, you have a pretty clear set of figures in mind: Licence fees, a watertight quote for the implementation project, and probably a buffer amount for training. Fast forward six months however, and the CFO is asking awkward questions about why Phase 1 has burned through contingency costs while the new system is still in a sandbox.
When Initial PIM costs spiral beyond what you’d budgeted for, blame is usually apportioned to the vendor or the implementation partner. But in practice, these overruns almost always turn out to be more a question of underlying structural problems. PIM projects will inevitably go over budget if you only discover the real complexity of product data once the meter’s running.
PIM Budgets: Set more on assumptions than on evidence
Early-stage budget projections are built using information which the business believes to be true:
- SKU counts
- Source systems
- A taxonomy
- Three languages needed
- A connector to the ERP
It’s rarely the case that requirements are ridiculously over-optimistic in scope. They’re just not complete.
The underlying issue is one of timing. If the genuine state of your data ecosystem is only fully understood once mapping and migration start, the project has to shift focus from configuration (setting up the tool – what you expected) to remediation (dealing with what the tool is unable to ingest cleanly). Remediation is where budgets put on weight, and it’s a factor rarely priced realistically when preparing the original statement of work.
The moment budgets start to bloat
Every PIM which goes over budget has its ‘data discovery moment.’ It tends to arrive when real extracts from the product catalogue, alongside the multi-formatted supplier files, hit the target model. Teams learn, to their alarm, that:
- Product identifiers don’t reconcile across systems
- Variants behave inconsistently by category
- Attribute names have different meanings depending on which team you ask
- Mandatory fields exist only inside certain people’s heads
- Supplier data arrives in incompatible formats and units
These aren’t exactly ‘exotic’ problems, but there is a fundamental problem here – they’re treated as unpleasant surprises during implementation when they should have been the subjects of a pre-project fact-finding mission.
Late discoveries which break the budget
1) Transformation gap
The team realises ERP or legacy data isn’t merely “mapped” to the PIM. It needs to be restructured. Of course, this triggers extra work like:
- Transformation logic
- Middleware, scripts
- Repeated validation
- Testing and multiple re-runs of migration
Whereas the original PIM plan assumed there’d be a pipeline, the reality is now looking more like a series of workshops.
2) ‘Ghost’ attributes
Unsurprisingly, when vendors run a demo, they run ‘clean’ products possessing the fields you wish you had. Your business reality almost certainly requires more – for instance:
- Compliance evidence
- Safety documentation
- Sustainability claims
- Compatibility rules
- Dimensions at pack level
- Market-specific variants
The hindrance is that many of these elements aren’t consistently stored in a single location. So, what happens when this issue is discovered mid-build? Businesses are forced to either:
(a) Delay sourcing/enriching them
or
(b) Go live with compromised outputs and leave the rework until later
Both options have their costs.
3) Conflicts in logic among functions
Marketing, trading, logistics and compliance teams often hold mutually exclusive definitions of things like the following:
- “product set”
- “variant”
- “bundle,”
- (even “size”)
A PIM, however, forces you to adopt a single model, so if you are having to reconcile these conflicts during the actual implementation, you end up paying to redesign the data model, re-map integrations, and re-train users, all while that meter keeps ticking over.
Why overruns compound so quickly
Late-unearthed complexities don’t just mean more tasks – they rewire data management dependencies:
- Migration rework delays configuration
- Configuration delays integration testing
- Integration delays UAT and training
- Any delay in go-live will extend paid external partner time as well as occupying more internal working hours
- In the meantime, you’re still paying the overhead for licences and programme
Eventually, by the time the system is technically live, the cost base has risen and the start date for increasing value has been pushed and pushed back.
The business case doesn’t fail because the platform is inherently weak; it falls down because what you budgeted for isn’t what you ended up needing to do.
What to do before you ask for a quote
In essence, it’s hard (and timely) evidence of your ‘hard’ product data reality which will protect your PIM budget – not feature comparisons.
There’s a practical pre-quote move:
Stress test a slice of your hardest reality.
Take a representative set of complex products (not your cleanest category – that’s cheating!). Attempt to map these products to your intended end-state in a spreadsheet:
- Required attributes
- Sources
- Transformations
- Channel rules
- Data ownership
If you can’t do that on paper, we’re back to the usual suspect – guesswork. Your aim is not absolute perfection. Your aim is to bring that all-important discovery phase forward, because only then can you price and plan the real work before contracts lock in the cost.
Next step: Discovery call
If you want to avoid your implementation turning into ‘price tag shock’, reach out to us today at Start with Data to book a short discovery call. We’ll surface the data entanglement that standard scoping misses – areas like transformation gaps, missing attributes, model conflicts, and integration dependencies. Knowledge is power, which will help your budgeting to reflect commercial reality, not misplaced optimism.