7 data import issues in PIM systems and ways to prevent them the right way
Moving data from spreadsheets, legacy systems, and scattered databases into a PIM is one of the key stages of a Product Information Management implementation. It is during migration that missing information, duplicates, inconsistent units, incorrect product relationships, and other data quality issues often come to light for the first time.
A well-prepared import helps organize data before the PIM goes live and reduces the number of corrections needed after migration. In this article, we look at the 7 most common pitfalls when importing data into a PIM and show practical ways to identify them and reduce their impact on the project.
What most often causes problems when importing data into a PIM?
The biggest problems tend to arise when an organization starts migration before product structures, data sources, responsibilities, and quality rules have been properly defined. The import itself is only the technical stage of the process. Its success depends largely on knowing which data is correct, how it should be mapped, and what structure it should have in the target PIM.
| Risk area | Typical migration problem |
|---|---|
| data quality | missing information, duplicates, inconsistent formats |
| product model | incorrect variants, relationships, and hierarchies |
| integrations | inconsistent mappings between systems |
| governance | no clear data owners or approval rules |
| scale | performance issues and large data volumes |
| assets | missing links between products, images, and documents |
1. Importing incomplete and inconsistent data into a PIM
Challenge:
Before implementing a product information management tool, organizations often manage product data across multiple sources – Excel files, ERP systems, CRM platforms, or even email threads. This fragmented approach results in inconsistent, incomplete, and duplicated information. Over time, companies may discover that crucial product data is missing, such as:
- Product images, technical specifications, or compliance certifications.
- Localized content for different regions or languages.
Additionally, data entry inconsistencies arise due to the lack of standardized formatting, such as:
- Different product name structures (e.g., 4GB RAM Laptop vs. Laptop, 4 GB RAM).
- Inconsistent units of measurement (e.g., cm vs. inches).
- Duplicate SKUs with slight variations in descriptions.
Impact:
- Products can’t go live on eCommerce platforms due to incomplete data.
- Manual work required post-migration to fill in missing information, slowing down time-to-market.
What should you check before importing data?
✔ Identify missing data early through a comprehensive data audit.
✔ Assign tasks to relevant teams (e.g., marketing for descriptions, legal for certifications) to fill in missing details.
✔ Create data governance rules to enforce consistency in naming conventions, units of measurement, and data entry.
✔ Use PIM’s built-in validation tools to flag errors automatically.
Migration itself is only one stage of a broader process. Before it begins, the data needs to be analyzed, cleaned, mapped, and validated. If you want to go through the entire preparation process step by step, see our checklist for preparing data for PIM.
2. Incorrect product, variant, and relationship model
Challenge:
Many organizations lack well-defined product hierarchies, leading to difficulties when structuring data within a PIM. Issues include:
- Poorly defined relationships between parent and child products (e.g., product bundles, size/color variants).
- Inconsistent categorization, with products assigned to multiple or incorrect categories.
- Manually entered attributes, causing discrepancies in format, terminology, and unit measurement across different systems.
Impact:
- Incorrect product relationships disrupt the online store’s functionality, affecting search filters, product variants, and cross-selling.
- Time-consuming and resource-intensive rework if hierarchies need correction post-migration.
- Non-standardized product attributes create challenges when syncing with ERP, eCommerce, and marketplace platforms.
What should you check before importing data?
✔ Define product models early – identify how products, variants, bundles, and accessories are related to ensure consistent and logical structuring.
✔ Involve cross-functional teams (portfolio, marketing, sales, IT) to align on product hierarchy and attribute rules (data mapping workshops).
✔ Implement a predefined attribute dictionary (taxonomy) in PIM to keep names, units, and formats the same across all products.
| Element | What needs to be defined before migration |
|---|---|
| base product | which data is shared |
| variant | which attributes differentiate the versions |
| bundle | which components it consists of |
| accessory | which products it is related to |
| category | which taxonomy is used for classification |
Learn why PIM matters for better product management – read more now.
3. Lack of clear mapping between PIM and source systems
Challenge:
Many organizations need different systems such as ERP, OMS, and eCommerce platforms to function together as they all rely on product data. Integrating the PIM with these systems is complex because:
- Each system may have different data models and API requirements.
- Legacy systems might not support modern integration protocols.
Impact:
- Data synchronization issues between PIM and other systems, leading to outdated or inconsistent product information.
- Increased IT workload and potential for costly development efforts.
What should you check before importing data?
✔ Identify all systems that will connect to the PIM; map out system dependencies and data flow diagrams with your technology partner.
✔ Use integration platforms (middleware solutions) to bridge gaps between legacy systems and the PIM.
✔ Ensure the PIM supports flexible APIs for real-time data exchange. The API-first approach is key.
| Data type | Example source |
|---|---|
| product data | PIM |
| operational data | ERP |
| assets and documents | DAM |
| inventory data | ERP / WMS |
| customer data | CRM |
Before importing, it is worth defining the source system for each type of information and only then designing the mappings.
4. Resistance to change and lack of stakeholder alignment
Challenge:
Implementing a PIM isn’t just a technical project. It requires a cultural shift in how teams manage product data. Common barriers:
- Resistance from teams accustomed to using spreadsheets or legacy systems.
- Misalignment between departments on data ownership and workflows.
Impact:
- Partial or incomplete adoption of the PIM, with some teams continuing to rely on old, inefficient processes.
- Delays in the migration process due to conflicting priorities.
What should you check before importing data?
✔ Communicate the benefits of PIM clearly to all stakeholders (change management).
✔ Involve key users from marketing, sales, compliance, and supply chain in the migration process.
✔ Provide comprehensive training to ensure teams are comfortable using the new system within the organization.
5. Migrating large data volumes without performance testing
Challenge:
Large organizations may have thousands (or even millions) of SKUs, with complex attributes, digital assets, and translations in multiple languages. Challenges include:
- Managing massive data sets without performance degradation.
- Migrating large volumes of digital assets (e.g., images, videos) alongside structured data.
Impact:
- Long migration times, increasing the risk of errors during data transfer.
- Performance bottlenecks if the PIM isn’t optimized for large-scale data.
What should you check before importing data?
✔ Migrate data in stages, starting with high-priority products.
✔ Archive outdated or inactive products that don’t need to be migrated in order to minimize migration scope.
✔ Test the PIM’s performance with large data sets before going live.
Top tip – Cover all cases in testing. Complex products can be tricky. That’s why all product variations and data types must be checked. Avoid choosing only the first few well-structured entries to prevent migration issues later.
6. Lack of data governance rules after migration
Challenge:
Organizations struggle to maintain long-term data quality post-migration without clear data governance policies. Top challenges:
- Unclear data ownership: Who is responsible for maintaining specific product attributes?
- No standardized processes for data updates, allowing errors to accumulate over time.
Impact:
- Data degradation, with errors creeping back into the system.
- Uncertainty and confusion among teams about who can create, edit, or approve product data.
What should you check before importing data?
✔ Define clear roles and responsibilities for data ownership.
✔ Set up automated approval processes within the PIM to enforce data quality control.
✔ Schedule routine audits to maintain data integrity over time.
7. Loss of links between products and digital assets
Challenge:
Businesses often overlook the difficulty of managing digital assets during migration. Issues include:
- Inconsistent file naming conventions and poor metadata tagging.
- Digital assets stored in multiple locations without clear links to product records.
Impact:
- Broken image links or missing assets post-migration, affecting product presentation on eCommerce sites.
- Difficulty managing localized content versions (e.g., different images for different regions).
What should you check before importing data?
✔ Integrate the PIM with a Digital Asset Management (DAM) system to manage assets efficiently.
Discover how PIM and DAM compare in our brief blog post—just 3 minutes to read!
✔ Standardize metadata to maintain seamless linking between assets and product records.
✔ Run validation scripts to ensure all assets are properly linked and meet quality standards before migration.
| Pitfall | Risk | What to do before migration |
|---|---|---|
| incomplete data | publication issues, manual corrections | audit and clean the data |
| incorrect product model | incorrect variants and relationships | design the structure |
| unclear data sources | conflicts between systems | define the system of record |
| no clear data owners | inconsistent decisions | define roles and responsibilities |
| large volumes without testing | performance issues | run a pilot migration |
| no governance | declining data quality after go-live | define workflows and audits |
| unlinked assets | missing images and documents | map PIM–DAM relationships |
Most import-related problems can be identified before the actual migration begins. The more decisions about data structure, sources, and quality are made in advance, the fewer corrections will be needed once the PIM is live.
Importing product data to a PIM system? Takeaways for a successful migration
1. Audit your data sources – identify where the correct information is stored.
2. Clean and standardize the data – remove duplicates and standardise formats.
3. Design the product model – define variants, relationships, categories, and attributes.
4. Define source systems – establish the responsibilities of PIM, ERP, DAM, and other systems.
5. Prepare mappings and validation rules – define how the data should be mapped to the target model.
6. Run a pilot migration – test a representative part of the catalog.
7. Establish post-migration governance – define roles, workflows, quality rules, and audits.
Key takeaway from Tandemite projects
Most data migration problems arise when the team starts importing before making clear decisions about the product model, data sources, and ownership. A well-prepared migration therefore begins with organizing the information architecture and only then moves on to the actual data import into the PIM.
Migrating to a PIM is a good opportunity to clean up data at the source and establish rules that will continue to apply after the system goes live. Auditing, modeling, mapping, and testing help reduce the number of issues that arise during the actual import.
If you are preparing a data migration to a PIM, contact us. We can help you review your data sources, product model, mappings, and migration plan before the import begins.
FAQ
What are the most common errors when importing data into a PIM?
The most common issues include missing data, duplicates, inconsistent units and naming, incorrect product relationships, faulty mappings, and broken links to assets and documents.
Should data be cleaned before importing it into a PIM?
Yes. Auditing and cleaning the data before migration helps identify duplicates, missing fields, conflicting values, and inconsistent formats before the data is moved into the target model.
How should you test a PIM data import before the full migration?
It is worth running a pilot migration on a representative part of the catalogue. The test set should include different product types, variants, relationships, languages, and linked documents and assets.
How do you determine which system should provide the data during migration?
Before importing, define the source system for each type of information. PIM may be responsible for product data, ERP for operational data, DAM for assets, and other systems for information related to their specific processes.
What should you do with duplicates before migrating to a PIM?
Duplicates should be identified and resolved before the actual import. You need to determine which record is the source record, which values should be retained, and how the system should handle similar conflicts during future imports.
How do you migrate images and documents together with product data?
The key is to preserve clear links between assets and the correct products, variants, languages, and markets. If the files are stored in a DAM, the migration should also include mapping the relationships between PIM and DAM.
What should you check after completing a PIM migration?
After migration, verify data completeness, product relationships, mappings, asset accuracy, validation results, and integration performance. The next step is to activate governance rules that will help maintain data quality after the system goes live.
