Implementing a Product Information Management (PIM) system offers huge benefits: structure, scalability and control. But you’ll only enjoy these advantages if the project is planned and delivered with the right strategy.
Too many businesses spend heavily on software and assume success will follow. PIM isn’t plug and play. Managing product data is complex, and a successful implementation depends as much on people and process as it does on technology. When done right, a PIM project can be a cultural and operational transformation, not just a technical one.
This article looks at ten common mistakes we see in modern PIM implementations and how to avoid them. Each one is drawn from real-world experience helping organisations turn product data into a strategic business asset instead of an ongoing frustration.
1. Starting with Technology, Not Strategy
The biggest mistake is choosing a PIM before defining what success looks like. A PIM will not fix poor data or unclear processes on its own.
Without a clear vision, you risk automating existing problems instead of solving them.
Pitfall avoided:
Start with strategy. Define your commercial goals, governance framework and measurable KPIs before selecting any platform. Treat technology as an enabler, not the starting point.
2. Underestimating the Complexity of Product Data
Legacy data is often messy, inconsistent and scattered across systems. Many teams assume migration will be straightforward. It rarely is.
Pitfall avoided:
Before the project begins:
- Audit your legacy data thoroughly.
- Identify quality issues such as missing attributes, duplicates and conflicting taxonomy.
- Plan time for cleansing, standardisation and enrichment.
Do not assume you can “fix it later.” Poor-quality data carried into your new system will only multiply your problems.
3. Ignoring Governance and Ownership
Without defined ownership, even the best PIM will degrade over time. If no one is accountable for maintaining product information, quality and trust quickly erode.
Pitfall avoided:
Assign clear roles for data creation, approval and review.
Build governance into your workflows so every product update is traceable and controlled.
4. Overcomplicating the Data Model
Trying to include every possible product detail from day one is a recipe for confusion. Complex, rigid data models are hard to maintain and slow to scale.
Pitfall avoided:
Start lean. Focus on your most important product attributes and expand later.
AI tools can help identify redundant fields and recommend smarter, more efficient structures as your catalogue grows.
5. Neglecting Supplier Onboarding
Even the most advanced PIM is only as strong as the data entering it. Suppliers are often the weakest link in the data chain. If onboarding is unstructured, you’ll waste time fixing errors instead of enriching products.
Pitfall avoided:
Use an onboarding platform such as SKU Launch to automate validation, mapping and data checks. Structured onboarding ensures cleaner inputs, less manual rework and faster time to market.
6. Overlooking Change Management
PIM projects fail more often from human resistance than from bad configuration. If teams don’t understand the value of the new system, they’ll keep using spreadsheets.
Pitfall avoided:
- Involve users early in the process.
- Communicate changes clearly and often.
- Make training practical and focused on real tasks.
Show people how automation and governance make their jobs easier, not harder. Successful adoption always starts with buy-in.
7. Failing to Measure Success
Go-live is not the finish line. Without measurement, you won’t know if your PIM is actually working.
Pitfall avoided:
Define measurable KPIs before launch, such as:
- Time-to-market
- Product data completeness
- Reduction in manual updates and related costs
Review performance regularly to track ROI and identify areas for improvement.
8. Treating AI as a Gimmick
AI can transform product data management, but it must be used wisely. Overreliance without oversight can lead to incorrect, biased or non-compliant data.
Pitfall avoided:
Decide where AI adds the most value. Use it to speed up enrichment, validation and attribute mapping but always keep human approval in place for high-impact changes. AI should enhance governance, not replace it.
9. Rushing Migration and Testing
Tight deadlines tempt teams to skip proper migration checks and user testing. This almost always leads to problems after go-live.
Pitfall avoided:
Treat migration as a data improvement project, not just a transfer task. Test early and with real data. Use validation dashboards to detect quality issues before they reach your live environment.
10. Treating Go-Live as the Finish Line
A PIM project does not end at launch. Without continuous monitoring, adoption will drop and quality will decline.
Pitfall avoided:
Set up ongoing governance and feedback loops, such as:
- Regular data audits
- User feedback sessions
- Periodic governance reviews
Plan a short “hypercare” phase after launch to fine-tune the system and keep engagement high.
Final Thoughts
You can’t avoid every challenge, but you can avoid the biggest ones. With the right strategy, governance and people-focused approach, your PIM project can deliver real business value and a single, trusted source of product truth.
Key takeaways:
- Start with organisational goals and governance, not tools.
- Involve your people early and plan for adoption.
- Use AI and automation with oversight, not blind trust.
Partnering for Success with Start with Data
If you’re planning a PIM project, or need to recover one that has gone off course, now is the time to act. Talk to our team today to build a clear roadmap that connects your product data, processes and people and turns your PIM into a real engine for growth.