PIM Data Migration

PIM Data Migration Services: What We Do for You

PIM data migration services move your product data from spreadsheets, an ERP or a legacy PIM into a new product information management system, mapped to its data model, cleaned and validated before go-live. We lead that work on the client side: we audit the sources, write the mapping, define the cleansing and remodeling rules, check every validation run and plan the cutover with a rollback. Your integrator runs the imports and the connectors.

Data migration is one part of a PIM project, and often the part that decides the launch date. If you need the full build, from requirements to adoption, see our PIM implementation service. This page is about moving the data, and about making sure it arrives in the new PIM ready to use, not ready to clean.

What We Migrate From, and To

Every migration has its own starting point. The ones we see most often:

• Spreadsheets to PIM: category files, supplier templates and the business rules hidden in formulas and macros.
• ERP to PIM: identifiers, logistics and master data, with a clear line between what the ERP keeps and what the PIM owns.
• Legacy PIM to new PIM: including moves to Akeneo, where years of workarounds should be retired, not copied.
• Several catalogs into one: duplicated catalogs per country, brand or distributor, consolidated into one model.
• Assets and links: images and documents from a DAM or shared drives, linked to the right products and variants.

Our PIM Migration Approach, Step by Step

1. Source Audit and Data Profiling

We list every system that holds product data, including legacy systems that are supposed to be retired but are still used, and profile the source data that is really inside. The output is a short data profile per source (completeness, duplicates, identifiers, value lists) and the list of decisions the business must take before mapping starts.

2. Target Data Model and Mapping

A migration is the cheapest moment to fix the data model. We check the target families, attributes, variants and identifiers against how customers shop, remodel where needed, then map every source field to its target with a transformation rule, a default value and a business owner. On a European DIY retailer's catalog, 1,324 flat families copied from a supplier classification were restructured into a two-level family model: moving the old structure as it was would have moved the problem too.

3. Cleansing and Transformation Rules

Data cleansing rules are written once and rerun on every extract, as scripts or import profiles, never fixed by hand in the last week. Duplicates get a rule for which source wins, units and value lists are standardized, and obsolete products are retired instead of migrated. Migration tools and automation make loads repeatable; data quality still comes from the rules. When a supplier portal was still being deployed at a European DIY retailer, supplier Excel files were checked with simple scripts (GTIN key, mandatory fields): a pragmatic stopgap beats waiting for the perfect tool.

4. Validation Runs

We load real products early and more than once, category by category. Each run is reconciled against the sources: record counts, completeness, identifiers, variants and asset links, so data integrity is checked, not assumed. Errors are fixed in the rules, not in the target, and the run is repeated until the results stop moving and business owners validate their categories.

5. Cutover, Rollback and Reconciliation

We plan the switch with your integrator: a phased migration by category or market, or a single cutover, depending on the risk. Then the freeze on source systems, the final load order, go and no-go gates, and a rollback plan tested in a dress rehearsal, so a failed gate means a delay, not data loss or a broken storefront. After cutover, a reconciliation report confirms that what went live matches what was signed off. We stay through the first weeks, while teams find the last gaps and the data governance routines take over.

Case Study: Consolidating Duplicated Catalogs Into One Model

A veterinary distribution platform ran a distributor-centric catalog, so the same product existed several times, once per distributor, and every change had to be made in each copy. A faster copy of that catalog into a new system would have carried the duplication with it.

The answer was a model change: one central "umbrella" catalog replaced the duplicated distributor catalogs, with a projected 50%+ cut in catalog maintenance. The lesson: duplication is a model problem, not a people problem, and a migration is the right moment to fix it.

Working With Your Integrator

The integrator configures the PIM, builds the import connectors and API integrations with your ERP and channels, and runs the loads. We work on your side across the migration process: the mapping, the cleansing rules, the review of each validation run and the cutover decisions. That split keeps one question from falling between two teams: is the data right?

If no integrator is chosen yet, we can help write the migration scope into the brief, so data mapping, cleansing and validation runs are priced from the start. Our guide on how to brief a PIM vendor shares the method.

Further Reading on PIM Migration

Still at the planning stage? These guides cover the method in detail:

• PIM data migration: process, risks and how to prepare
• PIM data migration checklist, from audit to go-live
• How long a PIM migration takes, phase by phase

Tell us where your product data lives today and where it needs to go. In one call, we will tell you what to check first and how we would scope the migration. You can also read how we work as an independent PIM consultant, or start with a PIM audit if the project is already off track.