It’s common to find two organisations who’ve invested in practically the same PIM platform, collaborated with the same implementation partner, and followed the same implementation steps and methodology. But strangely enough, they end up experiencing very different outcomes. The first goes live quickly, and is soon publishing high-quality, consistent product content across all channels on a daily basis. Conversely, the second business is still trapped in precisely the rework load it thought it was shedding: supplier imports fail, exceptions keep piling up, and the PIM users soon start returning to spreadsheets because the PIM “just doesn’t feel right.”
This gap between aspiration, expectation, and reality isn’t usually explained away by lack of software capability, but by what the software is being asked to ingest. That’s where you need to be on the spot with your product Data readiness because it’s going to determine whether your shiny new PIM is to become the operational accelerator you were promised, or not a lot more than a glorified (and expensive) filing cabinet.
The hidden variable in the PIM business case
The majority of PIM business cases tend to model the downstream benefits: Things like faster launches, fewer manual updates, greater (and better consistency, and higher conversion rate. Unfortunately, what they under-model are the upstream costs involved in making product data fit enough for the system designed to govern and distribute it.
For most businesses, pre-PIM product data management is usually fragmented by design:
- Identifiers and commercial fields in ERP
- Attributes in category spreadsheets
- Imagery and copy in marketing folders
- Compliance documentation in shared drives
- supplier feeds in a range of inconsistent structures
A PIM isn’t magically going to make all this complexity disappear. On the contrary, it’s going to expose it. If your PIM project discovers structural gaps mid-build, you find yourself paying implementation rates for what is essentially basic catalogue triage. Timelines get extended, overall confidence in the project’s outcomes drops, and it’s the platform which gets the blame for creating problems which have nothing to do with it.
What does “data readiness” mean in PIM terms?
To clarify, ‘readiness’ doesn’t mean perfection. It’s simply a clear and testable understanding of whether the data you have is able to support the operating model you’re trying to implement.
In practical terms, this readiness comes down to five key areas.
1) Product structure you can operate
Before configuration, all your teams need a shared definition of how products behave:
- The logic behind product vs variant
- Relationships between bundles/kits and components
- Rules regarding attribute inheritance
- Product relationships (like accessories, compatibles, or replacements)
If this definition is a moveable feast, the PIM itself becomes a negotiated patchwork with every workaround adopted turning into a long-term drag on operational efficiency.
2) Identifiers you can trust
If the same SKU has multiple identifiers across systems (or its identifiers aren’t stable), it’s not a question of having ‘messy’ data but one of a problem caused by reconciliation.
Readiness is grounded in certainty. It means you can state, unambiguously:
- The join key across systems
- Where duplicates exist and why
- Which record represents the unique, agreed upon and, thus, sellable truth
3) Completeness aligned to channel reality
The ROI on a PIM solution has much to do with the fact it can publish at scale. Nevertheless, this scaling only works if you can define ‘sellable’ per category and per channel:
- Specs
- Dimensions
- Materials
- Compliance fields,
- Imagery rules
- Marketplace templates
- Retailer-specific requirements
If you’re unable to define what constitutes a minimum publishable set, you’re basically configuring your new PIM platform to automate uncertainty.
4) Governance which matches the cost of enrichment
Weak adoption of a new system very often reflects a data governance mismatch: either it’s absent (as in “nobody owns it”) or over-stringent (“everything needs three approvals”).
This dimension of readiness is characterised by:
- Explicit ownership (create, enrich, approve)
- Designed and embedded rules on exceptions (what action is triggered when data is missing)
- rules are enforceable at entry, not argued about after publishing and underperforming
5) A migration plan grounded in what exists
Data migration projects usually fail when they’re treated merely as a technical lift-and-shift. Readiness should mean that you’ve already mapped:
- Sources for each field
- Transformations needed (units, formats, normalisation)
- Rules concerning conflict and discrepancy (what wins when sources disagree)
- a prioritisation of SKUs (which products matter first)
Where unreadiness turns into cost
Unreadiness typically manifests in three places on your commercial body, each with its own predictable commercial symptoms.
Migration becomes a cleansing loop. If inconsistencies, duplicates, or structural gaps surface mid-implementation, “migration” becomes ongoing manual correction. This is generally the single biggest driver of delays and scope creep.
Supplier onboarding turns into a permanent bottleneck. When your supplier feeds arrive in inconsistent formats (and they DO, let’s face it!), the onboarding process turns into one-off mini-projects for mapping logic and resolving exceptions. The new PIM cannot reduce effort if the quality of your intake is uncontrolled.
Go-live quality triggers user resistance. If data outputs are worthy of people’s trust, new workflows are configured and accepted. However, if incomplete pages get published after go-live (likewise, missing imagery, or conflicting specs) people hang onto parallel spreadsheets “just for backup.” So, although your PIM is alive and operational, it’s being bypassed constantly.
A readiness test you can run this week
To unearth the truth, you need to work with a revealing sample.
- Pick 50 SKUs at random across a few categories (and not just your best-behaved lines).
- Define the minimum publishable set for one target channel.
- Try to map each SKU using current sources.
- Track:
- missing required fields
- manual investigation occurrences
- identifier mismatches across more than one source
- duplicates, discrepancies, and contradictions
If a meaningful share requires investigation rather than extraction, you don’t have a PIM configuration problem. You have a readiness gap — and forcing the build won’t remove it; it will just relocate the work into the most expensive phase.
The mismatch that decides outcomes
Those two businesses with the identical PIM but different outcomes happen because the failed PIM system was purchased as a cure for data disorder, but the organisation hasn’t made the required structural decisions which allow data to behave like a true asset.
When all’s said and done, a PIM will accelerate what already exists:
- if the structure, ownership, and minimum standards of your product data are clear, it accelerates publishing and reuse
- if the above are contested and not resolved, it will accelerate what you’ve given it to work with – exceptions, rework, and bypass behaviour
Next: A data readiness assessment
If you’re planning a PIM implementation, or your PIM is live but underused, reach out to us today at Start with Data and we’ll run a data readiness assessment for you to quantify the gap between your current catalogue and what your channels and operating model require.