How to migrate your PIM: a practical guide

Moving to a new PIM is a big project. Your product data has built up over years, and your storefronts and marketplaces depend on it every day.

That is why reducing risk is usually the part teams worry about most. Much of that risk can be planned for upfront.

This guide starts with what needs to move and how to choose a migration approach. It then covers the scenarios worth checking for early and how to clean data as it moves. The last sections focus on reducing risk, with a checklist to work from.

What needs to move when you migrate a PIM?

Product records are the most visible part. A full migration usually also includes:

• The attribute model. Every field and the values it allows.
• The taxonomy. Your categories and which attributes apply to each one.
• Product relationships. Variants and bundles, and the links between related products.
• Digital assets. Images and documents, along with which product or variant each one belongs to.
• Channel mappings. Which field goes to which storefront or marketplace, and in what format.
• Workflows and permissions. Who can edit what, and who approves changes.
• History. Version history and audit records, where you need to keep them.

Listing these out at the start gives you a clear scope to plan against.

What is the best approach to PIM migration?

There are three common approaches. The right one depends on the size of your catalog and how much resource your team has to run two systems at the same time.

Phased migration moves one part of the catalog at a time, for example one brand or one product category. It keeps risk low, since any issue stays within the part being moved. For a while, some products live in the old system and some in the new one, so you'll need to agree which system owns each product during each period. That way, it'll be clear where the source of truth is.

Parallel run keeps both systems running side by side with the same updates going into each. Once the new system's output matches the old one, you switch over. It carries the least risk, since the old system stays in use and you can compare the two directly before switching. It also takes a lot more time and effort, because on top of your current system, teams have to start working on an entirely new one while making sure every update lands in both.

Big bang migration moves everything on a set date. It is the fastest option, and it works best when your data is already clean and your channel mappings are well understood. It's also the approach with the most risk, since the whole catalog switches over at once and any issue reaches every product at the same time.

Phased migration is a common choice for larger catalogs. Split by brand or by category, whichever is easier to separate in your current data.

What scenarios should you plan for in a PIM migration?

A few scenarios are worth checking for early, so the plan accounts for them from the start.

Your product hierarchy may be structured differently in the new system

PIMs can organise products in different ways. Your current system might group products four levels deep, for example brand, collection, style and colour, while the new one is set up around a parent product with variants underneath.

In many cases the new system can hold the structure you already have, and it just needs to be defined correctly during setup. Where the two do differ, decide which level your product pages are built on. That tells you where content should sit in the new system.

One or a few brands may be set up differently from the rest

If you manage several brands, one or a few of them may be organised differently. One might group sizes under each style while the others group colours under each style. A brand that joined through an acquisition may still carry its original structure.

This may have worked in your current system because each brand's setup was built there over time. A migration mapping is usually written once for the whole catalog, so a brand with a different structure needs its own rules. Listing these brands early lets the mapping handle each one correctly.

Your storefront may expect data in a specific shape

Most PIMs send data to storefronts and marketplaces through an integration, and some still use file exports. Either way, the receiving platform sets the rules. Some platforms create products at the parent level, so they need a parent record even when content is written for each variant. Platforms that take files may only accept a specific format, such as XML.

Check these requirements with your ecommerce team early, since the new PIM has to send data in exactly the shape your storefront expects.

There may be some stray values that need cleaning up

Over the years, fields like material and colour can collect stray values, especially when data comes from many suppliers. Stainless, SS, S/S and stainless steel might all describe the same material.

A migration is a good time to agree one standard value for each and map the stray values to it. Filters and search on your storefront then work from consistent values.

How do you clean product data during a PIM migration?

The best approach is to clean as you migrate. Start by agreeing your data standards: the values each field allows and what a complete product looks like. Then records get cleaned as they move.

That way, when the migration is complete, there are no loose ends to tidy up and no extra work waiting afterwards. And you get a fresh, happy start.

Before planning the cleanup, audit the source data for duplicate products and missing or stray values. That shows how much cleanup the migration needs before any records move.

What to handle as records move:

• Deduplicate. Merge products that exist more than once, for example the same item listed under two SKUs.
• Standardise values. Map the different spellings of a value to the one standard value you agreed.
• Fill in missing information. Some products will arrive with gaps. If your new PIM can enrich product data, those gaps can be filled in the new system from sources like supplier documents and product images.
• Check records against the new system's rules. Validate required fields and allowed values as records load. Anything that does not fit appears in a report your team can work through before it reaches a product page.

How do you reduce risk in a PIM migration?

• Agree one source of truth. Decide which system is the master for each type of data during the migration, so everyone knows where to make changes.
• Name owners on both sides. Give your team and the vendor one point of contact each, so questions reach the right person quickly.
• Start with a quick test on a simple category. Move a small set of products from your simplest category first. It shows how the mapping works end to end, and what to adjust before moving on to more complex categories.
• Build in a training phase. Review a set of migrated products with your team and note what needs improving. This is where you add rules or tighten the definitions for specific fields. Keep refining and running new batches until you're confident of the outputs.
• Keep the old system running until you have compared the new system's output and are happy with it.
• Test at full volume. Load the whole catalog into a test environment before go-live. Issues that only appear at scale show up there first.
• Test every connection end to end. Run each import and export in the test environment and check what arrives on the other side.
• Switch off old exports last. Keep the old system's exports running until the new ones are confirmed, so your storefront always receives updates.
• Agree a rollback plan. Decide which systems revert and who makes the call.
• Agree what done looks like for each phase, for example attributes complete and channel output checked. Clear criteria make it easy to close one phase and start the next.

What should you do after a PIM migration goes live?

Once the catalog is live in the new system, a few habits help it settle in.

• Monitor channel output and data quality. Check that storefronts and marketplaces receive complete and correct data, especially for the first batches.
• Assign ongoing ownership. Decide who owns enrichment and data quality from here on, so standards hold as new products arrive.
• Retire the old system. Once the new system runs without issues, switch the old one off so nobody keeps editing in two places.

PIM migration checklist

Before you start

• Owners named on both sides, with one point of contact each
• Success measures agreed, such as attribute completeness and channel output accepted
• Source data audited for duplicate products and missing or stray values
• Attribute model documented, including field types and allowed values
• Taxonomy mapped from old to new, with unmapped values listed
• Hierarchy defined in the new system, and any brands set up differently noted
• Standard values agreed for fields, especially those with multiple spellings, variations (like colours), mixed units (like cm and centimetres) or inconsistent capitalisation
• Customer-facing fields separated from internal fields, so only text shoppers read goes to translation

Connections and data flow

• Data flow agreed, from what comes into the new PIM to what goes back out
• Storefront and marketplace requirements confirmed, using sample exports from each destination
• Connections and access set up for every source and destination, in both test and production
• Import and export schedules agreed, so updates are not missed or pulled twice
• Links between assets and products checked
• Roles and approval workflows set up in the new system

Testing

• Test migration run on a simple category and reviewed
• Channel output previewed and checked before anything goes live
• Full catalog volume loaded in the test environment before go-live
• Every integration point tested end to end

Go-live

• Source of truth agreed for each type of data
• Rollback plan agreed
• Definition of done agreed for each phase
• Team trained on the new system before their category moves
• Exports from the old system switched off only after the new exports are confirmed

After go-live

• Channel output and data quality monitored
• Ownership of ongoing enrichment and data quality assigned
• Old system retired once the new one is running without issues

If you are still choosing a platform, our comparison of the best PIM software for ecommerce covers the main options.

How does Hypotenuse AI help with PIM migration?

Hypotenuse AI is an AI-native PIM with a DAM built in. Enrichment and content creation run inside the same platform as your product data and images, so the gaps in your catalog can be filled as it moves.

Enrichment as part of the move. Missing attributes are filled from supplier feeds, internal documents, product images and trusted web sources as records arrive. Each value carries a confidence score and shows the source it came from.

White-glove onboarding. You get a dedicated account manager and solution engineer who work with your team through the move. Training includes personalised sessions and on-site workshops. Refresher sessions are available as your team grows.

A data mapping built with you. We map your data together, from what comes into Hypotenuse AI to what goes back out to your storefront. Sample exports from your storefront confirm formats and allowed values before the integration is built.

Start working before the full integration is live. Teams can begin with a simple file import and export while the full integration is built, then switch over once it has been tested.

AI trained on your catalog. Bespoke AI models learn your taxonomy and brand guidelines. During onboarding we run sample products, then retrain on your team's feedback until the output matches your standards.

Standards checked before publishing. Content and attributes are validated against your brand rules and channel requirements, and anything that does not pass is routed to review.

While some enterprise brands and retailers use Hypotenuse AI in parallel with their PIM to enrich data and create content, many migrate fully onto Hypotenuse AI because of the value of managing product data, images, enrichment and content in one platform, with one workflow from raw data to publish.

To see how a migration to Hypotenuse AI would work for your catalog, talk to our team.