Despite being a strategically wise investment, a new Product Information Management (PIM) platform can also expose some uncomfortable truths about the current state of your data management. Every duplicate SKU, every missing attribute, every “temporary” spreadsheet workaround is suddenly under the glare of the PIM spotlight. Don’t worry – it’s just the PIM system doing its job!
We’ve produced this article to show how you can turn your PIM implementation into a once-in-a-decade opportunity to fix your product data quality properly, not just move existing data issues into shinier software.
Product data quality – The real success factor
High-quality product data underpins everything you expect from your new PIM solution:
- Normalisation of supplier data
- Omnichannel consistency
- Automated processes
- AI-powered content enrichment
- Faster launches
Substandard data does precisely the opposite. It slows your teams down, degrades hard-earned customer trust, and turns the go-live (something that could be celebrated) into a tiresome and seemingly never-ending clean-up project.
In 2026, those businesses extracting real value from PIM treat their data quality as a strategically-driven discipline, not a technical ‘make-do-and-mend’ sticking plaster exercise. That shift in mindset is what marks the difference between a PIM solution which enables growth and one that, little by little, becomes shelfware, no more than a shiny new repository to keep that damaged data.
So, how do we go about addressing this thorny issue?
Phase 1: pre-implementation — define your “golden record”
Before migration from a legacy system begins, you must establish absolute clarity regarding what “quality” looks like.
(a) Audit and profile your data
Start by analysing all source systems: ERPs, legacy PIMs, spreadsheets, DAMs. Comb through them for:
- Missing mandatory attributes
- Conflicting values between systems
- Duplicate products and SKUs
- Inconsistent units, formats, and naming
- Broken or outdated asset links
This gives you a factual baseline, not one based on hope.
(b) Define standards and structure early
Your product taxonomy and data modelling are the enablers of quality standards. You need to establish baseline norms for:
- Product types and variants
- Attribute sets per product type
- Mandatory vs optional fields
- Controlled vocabularies and picklists
- Units of measure and formatting rules
This blueprint for your “golden record” becomes the target which every migrated product must attain.
Phase 2: implementation: Clean, migrate, and enforce
The migration phase is where data quality projects either gain momentum or start falling apart.
Clean before you migrate
Never treat PIM as a cleansing tool of last resort. Before data enters the new system, Remove duplicates, normalise values, and align records to your new taxonomy. Migrating poor-quality data simply makes it harder to sort out later.
Use phased migration
Do a test run. Start with one complex, high-value category.
- Load it
- Review it
- Test it on downstream channels
- Refine the rules
Then you’re ready to scale. Applying this load, check, refine loop enables you to catch these structural issues early.
Configure validation rules as guardrails
This is where modern PIM platforms truly stand out for their versatility. During implementation, you can feed in configurations for:
- Mandatory attribute rules
- Conditional logic (for instance, screen size required for TVs)
- Format validation (GTIN length, numeric ranges)
- Use of controlled vocabularies instead of free text
These baseline rules prevent bad data from entering the system, thus reducing potential reliance on team heroics and persistent manual checks.
Phase 3: governance: Protecting quality after go-live
Data quality protocols fall apart most rapidly if you haven’t clarified data ownership.
Assign clear roles
Successful teams define:
- Data owners, who are accountable for the accuracy of given data sets
- Data stewards who take responsibility for governance and standards
- Approvers who are in charge of validating product data before syndication
This embeds the principle of accountability into daily work, rather than having to rely on quarterly clean-ups.
Build quality into workflows
Approval workflows, quality gates, and automated notifications are what ensure that every product has to fulfil the same quality standards before reaching customers. Consider it “a second pair of eyes” by design, not by chance.
Integrate your digital assets properly
Attributes without images, manuals, or videos are incomplete. Link assets directly to product records, retire outdated files, and define channel-specific asset requirements. Wherever you can, integrate PIM and DAM so data and visuals stay in sync. In fact, the large majority of modern PIMs already have a DAM as a built-in feature.
Phase 4: monitoring quality as an organic KPI
The quality of your product data is never “done and dusted.”
Track the right metrics
At this stage in the evolution of quality management, teams using best practice monitor 4 metrics in particular:
- Completeness: required fields populated
- Accuracy: alignment with real-world product values
- Consistency: uniform data across channels
- Timeliness: speed of updates across systems
User-friendly dashboards available with modern PIMs make the issue of quality visible. Visibility is what drives behaviour, because what you can’t see, you won’t rectify!
AI has its role, but humans are the ultimate gatekeepers
Inbuilt AI tools are able to perform tasks like:
- Flagging inconsistencies
- Suggesting enrichment options
- Identifying gaps in data fields at scale
Used mindfully, it will reduce manual effort dramatically. However, used with blind faith, as a panacea for your organisational sins, it’ll simply spread mistakes faster. The winning model is human oversight (not granular, by any means), rather than humans being left out of the quality loop.
PIM solutions from vendors such as Akeneo and Pimberly now include native data quality scoring, validation, and enrichment features. However, these tools only work if you have governance and standards already in place.
The bottom line
Ensuring data quality during PIM implementation is not about perfection on day one. It’s about establishing standards, enforcing rules, and creating habits that keep data trustworthy as your catalogue, channels, and markets grow.
Treat implementation as a reset moment. Clean what you can, govern what you must, automate where it makes sense — and make quality part of how the business works, not just how systems are configured.If your PIM programme is exposing duplicate SKUs, missing attributes, and inconsistent formats, it’s high time you set a “golden record” to refuse entrance at the door to bad data. Contact us today at Start with Data and we’ll review your current-state data, define the standards that matter for go-live, and put validation rules and governance in place so quality improves during implementation — not months after.