PIM migration means moving your product data into a new system. That can mean two different things: setting up a PIM for the first time by importing data from spreadsheets or an ERP, or replacing one PIM platform with another. Both require the same planning discipline. But they're not identical in scope, and the second is usually harder.
This guide covers both PIM data migration scenarios: what's involved, what tends to go wrong, and how to approach the process so the new system actually works the way it should from day one.
What Is PIM Migration?
PIM migration is the process of transferring product data from a source system into a new Product Information Management platform. PIM data migration covers the source, the target, and everything in between: the data itself, its structure, its relationships, and the systems connected to it. The source can be Excel, an ERP, a legacy PIM, a collection of spreadsheets across multiple teams, or some combination of all of these.
Before any data moves, you need a target data model: a defined structure of product types, attributes, classifications, and relationships. Without it, you are loading data into an undefined space, and the result is usually a mess that is harder to fix than the original. A PIM migration is, at its core, a data architecture decision. The quality of the target structure determines how useful the system is after go-live.
When a PIM Migration Makes Sense
From spreadsheets or an ERP to PIM. The most common signal is scale. A manufacturer managing 2,000 SKUs in Excel might be functional. At 8,000 SKUs across multiple product lines, with channel-specific descriptions and localized content, the spreadsheet approach starts breaking. Data errors multiply, updating product specs across channels becomes manual work, and collaboration between teams degrades into email threads and version conflicts.
ERP systems store product data too, but they're built for transactions, not content. They don't handle multi-language descriptions well, have no concept of marketing copy, and rarely support the attribute depth that distributors or e-commerce channels require. When product data requests from sales, marketing, and e-commerce are bouncing between departments and arriving inconsistently, that's the ERP gap. Migrating to a PIM centralizes that data in one place and connects it to every channel from a single source of truth.
From one PIM to another. A PIM system migration usually happens for one of a few reasons: the current system can't scale to catalog size or user count, integration with ERP or e-commerce platforms is fragile or expensive to maintain, the vendor is raising prices or restricting features behind a higher tier, or the system's data model is too rigid to accommodate new product categories. Vendor lock-in is a real factor here. Some PIM vendors make it technically difficult to export data cleanly, which is worth evaluating before you commit to any platform.
What PIM Data Migration Actually Involves
The data transfer is the visible part. The less obvious work is what determines whether the migration succeeds. A full PIM data migration covers:
- Data model design: defining entities, attributes, attribute groups, product classifications, and taxonomies in the target system before any data arrives
- Attribute mapping: translating field names, data types, and value formats from source to target, handling mismatches where they exist
- Data cleaning: removing duplicates, standardizing formats, correcting errors, filling gaps in required fields
- Relationship mapping: linking products to categories, variants, assets, and related items
- Asset migration: images, documents, and technical files need to move alongside product records and stay associated correctly
- Integration reconnection: once the new PIM is live, every connected system (ERP, e-commerce platforms, marketplaces, print catalogs) needs to be reconnected and validated
In a PIM-to-PIM migration, there's an additional layer: structural translation. Different PIM systems use different data models. An attribute that exists as a multi-select list in one system may need to be rebuilt as a classification-based attribute in another. Taxonomy hierarchies rarely transfer directly. The time required to map these differences is consistently underestimated.
Pre-Migration Planning: The Phase That Decides Everything
Cut corners here and you will spend months correcting data that should never have been imported in the first place.
Audit your current data. Before setting up anything, document what you have: how many products and SKUs, how many data sources, where the quality issues are (duplicates, missing values, inconsistent formats, mixed data types in the same field). A manufacturer with 15,000 industrial components spread across three country-specific spreadsheets will need to resolve those structural differences before import, not after.
Design the target data model. Define product classifications (what types of products exist and what attributes each requires), attribute data types (string, integer, float, boolean, dropdown, multi-select, range, date), attribute groups for logical organization, and category hierarchies. This work takes time and should involve the people who will use the system daily: product managers, marketing, sales operations.
Some PIM systems let you export sample import feeds, which show the exact column structure and naming conventions required. AtroPIM goes further: it can convert export feeds into import feed templates, so you can reverse-engineer the required format from an existing export. That's a useful shortcut during setup.
Decide what to migrate and what to archive. Not all historical data deserves to move. Discontinued products, duplicate records, and outdated content add noise and slow down the initial setup. Agree on a cutoff.
Identify data owners. Assign responsibility for each data domain: who is accountable for product specs, who owns marketing copy, who manages assets. Migrations without defined ownership produce data quality problems the moment they're live, because no one knows whose job it is to fix them.
Data Preparation for PIM Migration
Clean data before migrating it. Migrating dirty product data into a new system doesn't fix it; it just moves the problem. Gartner research puts the average annual cost of poor data quality at $12.9 million per organization, and a PIM migration that imports unresolved quality issues compounds that cost rather than reducing it.
Standardize data types. PIM systems enforce strict data typing. Excel doesn't. Fields that contain a mix of text and numbers, dates formatted differently by different contributors, or measurement values combined with units in a single cell all need to be resolved before import. Dimensions stored as "10x20x5 cm" in a single field need to be split into separate numeric fields for length, width, height, and a unit field. Price ranges stored as "100-200" need separate minimum and maximum numeric fields.
Normalize values. Inconsistent capitalization, spelling variants, and unit mismatches ("Blue", "blue", "BLUE"; "kg" vs "KG" vs "kilograms") create classification errors and break filters. Standardize before import, not after.
Map Excel columns or legacy PIM fields to target attributes. Build a mapping document. For each source field: what is the target attribute name, what is the data type, is it required, and what transformation is needed. This document becomes the reference for everyone working on the migration.
For large-scale transformations involving complex restructuring, ETL tools like Talend, Apache NiFi, or Microsoft Power Query (built into Excel) can automate much of the data reshaping work before import.
Organize data by entity type. Separate import files for products, categories, attributes, assets, relationships, and variants. Mixing entity types in a single file creates import errors and makes troubleshooting harder. For product catalogs with multiple classifications (electronics, apparel, furniture), prepare separate files per classification: each has different required attributes, and a single combined file adds unnecessary complexity.
PIM Migration Execution
A phased import sequence reduces errors and makes failures easier to isolate. For most PIM data migrations, this order works:
- Dictionaries and reference data (measurement units, currencies, languages, tax categories)
- Category hierarchies and attribute sets
- Core product records
- Product-category relationships
- Assets (images, documents, technical files) with product links
- Variants and pricing
- Additional metadata
Always run a test import first. Select 15-20 representative products covering different product types. Review the results in the PIM interface before importing the full dataset. Error logs from the test will surface field mapping errors, data type mismatches, missing required fields, and broken asset links. Fixing these on 20 records takes minutes; fixing them after importing 15,000 records takes considerably longer.
For asset imports, linking via URL is cleaner than uploading files manually: include image URLs directly in the import feed and let the PIM fetch and associate them automatically. This works well when assets are hosted on a CDN or accessible via a public URL.
For PIM-to-PIM migrations specifically, keep the old system operational while validating the new one. If the old system feeds live e-commerce channels, a cutover without a fallback period is high risk.
PIM-to-PIM Migration: What's Different
Switching platforms adds complexity that a first-time migration doesn't have.
Data model translation is usually the hardest part. Every PIM has its own internal product data model. Attribute types, classification structures, and relationship logic don't map directly between platforms. What was a flat attribute list in the old system may need to be rebuilt as a hierarchical classification tree in the new one. Taxonomy hierarchies rarely transfer directly. The time required to map these differences is consistently underestimated.
Integrations need to be rebuilt, not just reconnected. If your current PIM feeds an e-commerce platform via a custom connector, that connector is almost certainly platform-specific. Budget for rebuilding integrations, not just reconfiguring them.
Export your data from the old system before giving notice. Some PIM vendors restrict export capabilities for customers in churn. Pull a full data export before starting any transition process. Confirm the export is complete and usable before proceeding.
In projects we implemented for manufacturers switching from legacy PIM systems, the most common problem was discovering mid-migration that the old system's export format was incomplete: assets were missing from exports, attribute values were truncated, or relational data (product-category links, variant structures) was not included. Getting that data out retrospectively, while already mid-migration, is expensive and disruptive.
In terms of timeline, a first-time PIM migration for a mid-size catalog of 5,000-15,000 SKUs typically runs 6-12 weeks when data preparation is done properly. A PIM-to-PIM migration at comparable scale takes longer: data model translation and integration rebuilding routinely add 4-8 weeks on top of that, depending on how structurally different the two platforms are.
Post-Migration Validation
Before going live, run through each of these checks systematically:
- Data completeness: confirm the expected number of product records imported, all required attributes are populated, and no records were silently skipped
- Data accuracy: spot-check products across different classifications to confirm attributes are mapped to the right fields
- Search and filtering: if attribute data types were configured correctly, filters and faceted search should return accurate results
- Multi-channel distribution: confirm data flows correctly to connected e-commerce platforms, ERP, and any marketplace feeds
- Asset associations: confirm images and documents are linked to the right products
Document every error that surfaces. Correct the source data. Re-import only the failed records if the system supports incremental updates.
Common PIM Migration Failures
Migrating before the data model is finalized is the most expensive mistake. If the attribute structure changes after products have been imported, bulk corrections are slow and error-prone. The data model needs to be complete before a single product record moves.
Skipping data cleaning is the second most common problem. A new PIM loaded with dirty data is only marginally better than the system it replaced, and significantly more expensive. The cleaning work is unavoidable; doing it before migration is always faster than doing it after.
Relationship data catches teams off guard regularly. Products connect to categories, variants, assets, related products, and accessories. Each is a separate data operation. Teams focused on product records often reach the relationship migration step without having planned for it.
A PIM-to-PIM cutover without a rollback plan is high risk. If the new system reveals a critical data issue post-launch, you need the old system accessible while you fix it. Keep it running for at least 30 days after go-live.
For large catalogs, file size is a practical constraint. Imports of 50,000+ rows are slow and harder to recover from when errors occur. Split large imports into batches of 10,000-50,000 rows and use CSV format over XLSX for better performance.
Choosing a PIM That Supports a Clean Migration
Some PIM systems are significantly easier to migrate into than others. How well a platform handles PIM data migration is a legitimate evaluation criterion. Worth checking before you commit:
- Flexible data model: the system should accommodate your attribute types, classification structures, and product relationships without custom development
- Import/export capabilities: full round-trip import/export for all data types, including ranges, multi-select fields, and relational data
- REST API coverage: well-documented and covering the full data model, so automated migrations and ongoing integrations are straightforward
- Deployment options: on-premise deployment gives full control over data and infrastructure, which matters during migration from a security and compliance perspective
AtroPIM is built on the AtroCore data platform, which makes it highly configurable: the data model is entirely flexible, attribute types include ranges and multi-select, and the REST API is auto-generated per instance to the OpenAPI standard. Import feeds can be configured for every entity type.
The open-source model also eliminates the vendor lock-in risk. There's no proprietary export format, no migration fee, and no architectural barrier to pulling your data out if circumstances change.
After the PIM Migration
Going live is not the end of the project. The first 90 days after go-live are when data governance either takes hold or quietly falls apart.
Assign ownership for each data domain: who is responsible for product specs, who maintains marketing copy, who manages assets. Without named owners, data quality degrades by default. Create entry standards covering required fields, accepted formats, and naming conventions. Set up approval workflows for product data changes so updates go through a review step before reaching connected channels.
Schedule a data quality audit at 30 and 90 days post-migration. Issues that slipped through validation surface quickly once real teams start using the system daily.
A PIM with strong governance produces accurate, channel-ready product data consistently. Without it, a well-executed PIM data migration degrades back into the same quality problems the migration was supposed to solve.