When implementing software solutions, so many businesses see go-live as the finish line in a long-distance race against time. The project team celebrates and disbands, while the implementation partner moves onto their next appointment. The business breathes a sigh of relief and assumes the hard work is done and dusted.
…But at some stage, somewhere between hyper care and the first quarterly review, commercial momentum fades. Approvals start piling up. New product ranges arrive and then sit in spreadsheets because “someone else” is dealing with them. Product data quality starts to drift. The uncomfortable truth emerges – your new system is live, but the supposed added value is flatlining.
Our article explains just why this much-repeated scenario isn’t actually a failure of technology. It’s the inevitable consequence of implementing a tool without having established an operating model to run it. We’ll finish with a suggestion for what you can do to avoid it happening to you!
Why the post-go-live engine stall is a handover problem
During the implementation project, the programme has explicit urgency, adequate resourcing, and a deadline to respect. It’s after go-live when the work changes character. It reverts to ongoing, cross-functional operations. The problem is, when those project team members return to their day jobs without a clear “who owns what” model, the shiny new PIM becomes “everybody’s responsibility,” and we know what that usually means – NOBODY’S.
When the best-laid plans start to go awry, things tend to look like the following:
- quick fixes happen outside the PIM because “that new range MUST get to market fast!”
- ‘Temporary…just this time’ exceptions become normal practice
- The decline in data quality happens so gradually that no-one points it out or escalates it
- the PIM slowly turns into a rather costly repository, rather than the place where added value can be created
The missing component: An operating model
A PIM operating model doesn’t need to be overcomplicated. When all’s said and done, it answers three basic questions:
1. Who does what?
2. To what standard do they do it?
3. How do we know if it’s working?
If your PIM engine keeps stalling, it’s probably because it’s missing at least one of the following.
1) Ownership which is both explicit and enforced
“IT’s in charge of the tool” or “eCommerce is responsible for the PIM.” No, not explicit enough. Who you need are named owners for all data domains and decisions on:
- Who owns technical specs vs marketing content vs compliance fields
- Who approves schema changes and addition or elimination of attributes
- Who is accountable when data quality drops
Without this ownership framework, approvals queue up, launches are delayed, and teams route around the system because they have no confidence in it – money down the drain, to be frank!
2) Standards that users can actually apply
A lot of teams build a data model during implementation but they never actually codify it in a usable way. ‘Complete’ is a gut feeling, not a verifiable rule. Users make inconsistent judgement calls (they’re only human!) and quality becomes impossible to sustain across channels.
Quality thresholds must be practical: That means areas like:
- Minimum publishable sets (by category and channel),
- Universally adopted naming conventions and units measurements
- Validation rules which block bad data at ingestion
3) Supplier onboarding isn’t an ad hoc project
Post-go-live decline is often marked by quality of intake. New suppliers and new ranges arrive in various formats, and there’s no repeatable onboarding process in place. What do internal teams do? Yes, you’ve guessed it – they revert to manual reformatting and spreadsheet triage. Time-to-market slowly gets longer and the PIM performance record starts off compromised.
4) A change loop for channels and catalogue evolution
Channels change requirements. Categories expand. Attributes evolve. If you don’t have procedures for updating mappings, schema and governance, your PIM turns into a ‘frozen model’ within months. Worse, your teams start bypassing it because it simply can’t keep up with operational cadence.
5) Training whose knowledge transfer survives staff turnover
If “this is how we use the PIM” resides only in the heads of the original project team, you lose that knowledge when they leave (or indeed, when they are on leave). The risk is then that new joiners will learn bad habits (or, from their perspective, the fastest route to delivery), which usually involves copious spreadsheets and endless email threads. The result? A bypassed, passive PIM.
6) Metrics that make drift easily visible
If your main KPI is ‘uptime,’ you’re not going to identify decline until it rears its head as a commercial incident. Operational metrics for your PIM should constantly be tracking:
- Data completeness and validation failure rates
- Time-to-market by category
- Supplier intake performance
Adoption signals (such as in-system edits as opposed to exports and rework)
Confused by PIM Vendors?
With 100s of PIM software vendors worldwide, choosing the right PIM solution can be a daunting & confusing task.
Use our guide to assess PIM solutions against the right capabilities to make an objective and informed choice.
Why ‘more features’ doesn’t re-ignite momentum
Once the performance level of a PIM is perceived as problematic, teams across the organisation often respond by requesting enhancements to the software. However, more feature work will never compensate for a lack of ownership, weak and poorly-enforced standards, or uncontrolled supplier data intake. All more features do is end up adding complexity to a system nobody is consistently using anyway!
Competitive momentum will only return if the PIM solution actually solves things and becomes the path of least organisational resistance:
- Correct data in
- Trusted outputs out
- Clear roles
- A predictable operational rhythm
Tell-tale signs why you’re experiencing post-go-live product data management drift
Your operations are highly likely to be drifting into the doldrums if:
- Products can be pushed as ‘ready’ even though they’re often missing certain essentials
- The approvals queue is getting longer because there aren’t enough (or any) formally assigned approvers
- Teams are habitually patching channel data downstream, after syndication
- Suppliers submit inconsistent file formats and internal teams have to fix them manually as a matter of course
- Data quality reporting is patchy and largely retrospective, rather than being embedded in operations
Book a discussion about your PIM operations
If your PIM is live but momentum is fading, get in touch with us today at Start with Data to book a PIM operations discussion. We’ll review your operating model layer (ownership, standards, supplier intake, change control, training, and performance metrics) so we can identify what’s causing drift. Make your PIM system a competitive capability again, not a mere project artefact!