You click off the spreadsheet and close the post-mortem. The conclusion is unsatisfactory, but at least it’s tidy and clear: the PIM we chose wasn’t the right fit. You go back to the ads, scan the market, and restart the customer cycle of looking for a more ‘suitable’ platform.
Although the above scenario (far more common than imagined) is of little comfort, at least it absolves the business of any part in the failure – after all, it’s the supplier’s tool that didn’t perform as desired. The big BUT is a reality check. Most underperforming PIMs haven’t failed because of weaknesses in the software. Rather, they’ve sunk because of what the platform was asked to support:
- Unstable product structure
- Poor-quality data
- Fragile integrations
- A governance vacuum
Until you address these fundamentally unsustainable foundations, the next tool will just inherit the same risk of failure.
Why the platform becomes the scapegoat
A PIM platform is where operational friction is felt. Business users habitually see slow screens, awkward workflows, missing attributes, and inconsistent outputs. In the absence of real insight, when teams are unable to trust output, they tend to blame what they choose from a drop-down menu and click on.
However, symptoms aren’t root causes. If people complain that “the PIM failed”, what they’re usually referring to are:
- Differing product definitions by team and category
- The chaos caused by legacy encoding of the product taxonomy
- A data migration which centralised inconsistencies instead of removing them
- An absence of end-to-end ownership of the ‘golden record’ (or ‘single source of truth’)
- Integrations which, although they worked in testing, degraded in operational use
- Lack of buy-in for PIM adoption, meaning the “Shadow Excel” workarounds never really went away
Even the best-in-class PIMs can’t compensate for this accumulation of obstacles. In fact, once you deploy your PIM, it makes these problems uncomfortably visible.
Failure mode 1: Data is migrated but not made usable in the process
This most common failure isn’t a technical one. It’s that the PIM was loaded with product data which wasn’t ready.
If you treat migration merely as a technical lift-and-load, business decisions will get made by default and hurriedly, under time pressure. What remains are several unanswered issues:
- Which records matter
- What to do with conflicts among sources
- How to treat incomplete product records
- which historical ‘mess’ shouldn’t be loaded
Ironically, the PIM becomes a governed home, only it’s inhabited by the ungovernable truth.Post go-live, confidence in the usability of the data stored in the new PIM collapses. Teams hold onto their own spreadsheets because they know the PIM is incomplete or contradictory. Once you arrive at that situation, you certainly don’t have a single source of truth. On the contrary, you’ve got an extra system and a new hiding place for errors.
Failure mode 2: The structure is designed for the wrong reality
To use a PIM to full effect, it needs to encode a model of how your products behave in reality regarding:
- Categories
- Attributes
- Inheritance
- Variants
- Bundles
- Relationships
If that model is lacking, everything downstream becomes more expensive.
From our experience, the two most common routes to a poorly-ideated structure are:
- importing the legacy hierarchy ‘as is’ (namely, the formalisation of years of ad hoc decisions)
- accepting the generic vendor default structure (fine, in theory but highly likely to be misaligned in practice)
The damage often becomes most apparent when you add a new channel. If a structure has been built implicitly for one particular web shop, it probably won’t survive the plethora of demands placed on it by marketplace templates, B2B requirements, regional variants, or compliance-driven attributes. So syndication has to become manual, variant management becomes easily breakable, and what were supposed to be exception workarounds turn into daily operations. Time-consuming, slow, costly, and very, very difficult to scale.
Failure mode 3: Integrations make the PIM unreliable
When dissatisfied business decision-makers describe their PIM system as ‘unreliable’, what they’re really complaining about is an integration landscape whose scope was too ‘light’ and which they tested too narrowly.
There’s a typical pattern. First, connectors work adequately for the initial scope, but then production exposes edge cases (such as variant logic, data volumes, intermittent sync failures, downstream rejection rules, or undocumented ERP customisations). In isolation, each issue is solvable. However, the BIG problem is that these live failures end up becoming more and more frequent, consuming all capacity – in effect, the programme never moves forward from a perpetual state of “keeping the lights on” to what you invested in it for – “creating value”.
The platform did what it was configured to do, but the operating environment into which it was dropped was a good deal more complex than the integration plan acknowledged.
Failure mode 4: Governance didn’t exist, so quality decayed
One of the primary purposes of a PIM is to act as a system of standards. Without any notion of data ownership, it becomes more like a system of storage (albeit, a very good system!)
Ownership roles must be explicit:
- Who enriches
- Who approve changes
- Who resolves conflicts
- Who’s responsible for maintaining taxonomy
One the implementation team has left, data quality starts to degrade without these ‘safeguarding’ roles in place. Your people revert to habits which just make their working day easier, primarily quick edits, using local files, and bypassing validation
The concept of ‘golden record’ becomes an abstraction to which lip service is paid, rather than an enforceable ground rule within the organisation.
This is the silent version of failure, a sleeping giant: the PIM system is live, but it isn’t where the day to day actually happens.
A quick health check: is it the tool, or the foundation?
Dig down and look for the following operational signals:
- Teams systematically turn to ‘Shadow Excel’ because the PIM is slower than workarounds
- Channel teams habitually fix ‘the final mile’ manually, and after syndication
- Errors have no single and accountable owner at source
- Taxonomy has overlapping categories and an inconsistent depth
- products can be marked “ready” even when missing essential attributes
- integrations break down when there are routine upstream changes
If a few of these sound familiar, be warned – replacing the software platform will not help until the foundation is stabilised – until you fix the fundamentals.
PIM rescue discussion
If your PIM is live but underused or underperforming, if your users haven’t adopted it and effectively write it off as “not fit for purpose” get in touch with us today at Start with Data and book your PIM rescue session.
We use our expertise and experience to get to the very bottom of the issues, isolating whether the failure sits in data condition, structure, integrations, governance or adoption. All this without going through the costs and time of going through another selection cycle (when the operational outcome is most probably going to be the same issues.)