PIM implementation projects basically go wrong for the following reasons:
- The product data is weak
- Data ownership is unclear
- Workflows are vague
- Too much data is pushed through too quickly
All factors that matter, because poor implementation locks these bad habits into more extensive practices. The checklist below is intended to provide practical advice for your projects so you can sequence the work appropriately, prevent common launch mistakes occurring, and give your PIM solution a much better chance of delivering the ROI you want from day one.
1. Define your business objective
Start with the problem, not the platform.
Establish clarity regarding what metrics the PIM is to improve. For instance:
- faster SKU onboarding
- better data quality
- marketplace or channel expansion
- less manual rework
- stronger governance
If your objectives are vague, the risk of project drift increases.
2. Audit your current data and processes
Before you start configuration, you should understand exactly what you’re dealing with. That involves mapping where your product data lives, who accesses and manipulates it, and how it moves around the organisation at present.
Be sure to review:
- ERP, spreadsheets, supplier files, legacy systems
- current approval and publishing workflows
- major data gaps, duplicates, and inconsistencies
- which system owns which attributes
…because this is where hidden complexity usually shows up.
3. Define your ‘single source of truth’
The PIM isn’t necessarily going to ‘own’ every data field. For instance, prices and stock belong better in ERP, but rich content, attributes, and channel outputs should be in PIM.
Create a clear map for data ownership so everyone in every area knows precisely where each type of information is maintained and who is accountable for its upkeep.
Without these steps, the danger is that integrations become confused/ing and user trust in the system drops quickly.
4. Design your future data model
This is a key step because it shapes everything else. You need to build a data model which reflects how your products, channels, and customers actually work (not how you’d like them to work!)
This includes:
- Product taxonomy and category structure
- Attribute sets organised by product type
- Controlled vocabularies for input (avoid free text input)
- Product variant and relationship logic
- Mandatory fields to be filled, plus validation rules
Keep the first version simple and practical. Overcomplicated models just slow teams down, put them off using the models, and lead to rework as an alternative to adopting the new system.
5. Build and implement a data governance framework before your PIM goes live
Without governance, a PIM very easily becomes a dumping ground for… everything. Therefore, you need to define who owns attributes, who approves changes, and how you handle exceptions.
Put in place the following:
- named data owners and stewards
- approval workflows
- role-based permissions
- escalation paths
- quality rules
Defer governance and you fall into ‘data debt’, with never-ending interest paid in reduced revenue.
6. Clean your product data before migration
Do not import poor data and make vague promises to deal with it later. You most likely won’t, and that’s one of the fastest ways to turn your PIM project into money down the drain.
So, before migrating from the legacy system(s):
- standardise values, units, and naming
- remove duplicates and obsolete records
- populate or flag mandatory fields
- prepare supplier templates and rules for data onboarding
A PIM is very good for enforcing structure but what it can’t do is magically create clarity from chaos on its own.
7. Pilot the system before you carry out the full migration
Avoid a ‘big-bang’ launch. Start with a representative slice of your product catalogue, such as a priority category, or data destined for a key channel.
Running this pilot will enable you to:
- Test the model with real products
- Validate import mappings
- Check workflows and permissions
- Prove channel outputs work correctly
Sort out any issues here, rather than far more costly and time-consuming remediation further down the line.
8. Configure your integrations and quality gates
Once the model is proven to work, configure your PIM around the operating model, not around users’ old habits. Set up integrations to ERP, eCommerce, DAM, marketplaces, or supplier feeds as needed.
At the same time, make sure you implement quality gateways like:
- Mandatory field checks
- Completeness scoring
- Validation rules
- Publish controls
Do this and you’ll prevent incomplete or non-conforming records getting through to live channels.
9. Train your users according to their roles
First and foremost, a PIM training programme should be practical and role-specific. eCommerce teams, Product managers, data stewards or approvers don’t all need the same view or the same instructions. In other words, avoid the ‘one size fits all’ approach.
Good user adoption usually depends on the following:
- hands-on training in a ‘safe’ (sandbox-type) environment
- simple guides to processes
- super-users or ‘PIM champions’ in each team (functional areas)
- clear and timely support during the first weeks after launch
A trained team not only maximises the effectiveness of the new PIM. It’s also far more likely to adopt the changed behaviours which demonstrate genuine buy-in for the new system. An untrained team? They will persist with old habits and find ways to work around the PIM. Cutting corners on training is a false economy.
10. Launch in phases and manage hyper care
It’s better to go live in controlled stages rather than attempting it all at once. Start with a channel, category, or supplier group where you can monitor results closely.
You can then run a short hyper care period with:
- A daily issue review
- Frequent channel checks
- Data quality monitoring
- Clear ownership for problem-solving
- A post-launch review to conclude hyper care
The PIM launch is not the end of the project, but the start of a governed operating rhythm.
What should a successful launch look like?
Positive signs are:
- Data moving through cleaner workflows
- Teams stop emailing versions around
- New SKUs reaching channels faster.
- Fewer product records failing validation.
- A much more trustworthy product catalogue
That’s the real point. “The proof of the pudding is in the eating.” You’ve demonstrably implemented a more reliable product data operation, part of your SOPs, not just a piece of software layered upon legacy practices.
Next steps
If you’re planning a PIM implementation project, start by laying the groundwork before you get dazzled by PIM vendor demos. Reach out to us today at Start with Data for a discovery call to discuss how we can help you build the right launch plan and put in place the steps you need so you can move from spreadsheet-led product data management on the hoof to a governed PIM operating model.