Key Takeaways
- PIM solves product data complexity that spreadsheets and ERPs can't handle cleanly. Under 200 SKUs, simple attributes, and one sales channel: you probably don't need it yet.
- Assign a Project Owner, Data Manager, and Technical Architect before you start. Without clear roles, data prep stalls, and decisions stall with it.
- Data modeling is the most consequential step. Getting product families, attribute groups, taxonomy, and variant logic right before import saves weeks of rework. In our projects, this phase alone took two to four weeks to complete properly.
- Clean your data before migration. Importing 8,000 products with inconsistent attributes and duplicate records doesn't give you a cleaner catalog. It gives you the same mess in a more expensive system.
- UAT with real data and real users catches what technical testing misses. Workflow gaps and missing validations surface when product editors use the system, not before.
- A phased go-live consistently outperforms a big-bang launch. Start with the product lines that drive the most revenue, get them right, then expand.
- Understand your integration architecture before selecting a PIM. ERP and e-commerce connectivity matters more during selection than most buyers realize.
- Without a named data owner post-launch, a PIM accumulates stale data and teams stop trusting it. Two years in, the company is back to spreadsheets.
This PIM implementation guide is written for product managers, operations leads, and anyone stepping into a PIM project for the first time. Most implementations we've seen run into the same problems in the same order: skipped data modeling, underestimated migration effort, and governance that gets set up too late. The steps below are sequenced to address those failure points directly.
Before You Start: Is PIM Actually What You Need?
Not every product data problem is a PIM problem. Buying a PIM when you have a process problem, or a data hygiene problem, just adds infrastructure without fixing anything.
A few signs you do need PIM:
- You manage product data across multiple channels (webshop, print catalogs, marketplaces, retailer portals) and keeping them in sync is manual and error-prone.
- Your catalog has real attribute complexity: different product families with different specs, many variants, rich media per product.
- Multiple teams touch product data and there's no single source of truth.
- You're spending significant time on data exports and reformatting for different recipients.
- You're preparing for digital product passport compliance, which requires structured, traceable product data by regulation.
Signs you might not need PIM yet:
- You have a few hundred simple SKUs and one sales channel. A well-structured spreadsheet or basic ERP product module may be enough.
- Your real problem is that nobody owns the data. A PIM won't fix ownership issues. That's a process and org problem.
- You're primarily solving a media storage problem. A DAM may be what you actually need, though the two often come bundled.
The data quality problem is larger than most companies realize before they start looking. Around 30% of all e-commerce returns are attributed to inaccurate or incomplete product descriptions. That cost lands in logistics, customer service, and lost repeat business before anyone opens a conversation about PIM. PIM projects fail when companies underestimate the data work involved and overestimate what the tool will do on its own.
Assemble your team before anything else
A PIM implementation needs three roles covered, whether by three people or fewer wearing multiple hats.
The Project Owner is accountable for scope, timeline, and budget. This person makes decisions when priorities conflict and keeps the project from drifting. It's usually a product manager, head of marketing, or operations lead, not an IT manager.
The Data Manager owns product data quality before, during, and after migration. They run the data audit, coordinate cleanup across source systems, define attribute standards, and become the internal authority on what goes into the PIM. Without this role, data prep becomes everyone's problem and therefore nobody's.
The Technical Architect handles system integration, import logic, and infrastructure. They own the connection between the PIM and your ERP, e-commerce platform, and any other systems in scope. In smaller companies, this is often a senior developer or an external implementation partner.
These roles don't require full-time headcount, but they do require clear ownership. Ambiguity here surfaces on go-live day.
PIM Implementation Step 1: Map Your Product Data Landscape
Before you configure anything, you need a clear picture of what you're working with.
Start by listing every data source: ERP, spreadsheets, supplier portals, legacy databases, and agency-managed files. Most companies discover three or four more sources than they thought they had. Raw SKU count matters less than complexity. 500 industrial pump SKUs with 80 technical attributes each is a bigger migration project than 5,000 apparel SKUs with 10 attributes each.
Inventory your media assets too: images, technical drawings, safety data sheets, certificates. Are they filed consistently? Are they linked to specific products anywhere, or sitting in a folder structure someone created in 2014? For most manufacturers, the media situation is messier than the structured data.
Also, map your data consumers. Your webshop, print catalog agency, marketplace feeds, distributors, retailers, and internal sales team may all need different formats, different attribute subsets, and different completeness levels. That variation matters for how you design the data model in the next step.
Be honest about current data quality. Incomplete attributes, inconsistent naming conventions, duplicate records, and missing translations are all common. Documenting them now means they don't surprise you during migration. Poor product data quality costs enterprises an average of $12.9 million per year, so the inventory step has direct financial consequences.
The output of this step should be a simple data inventory document. It doesn't need to be elaborate. It needs to be accurate.
PIM Implementation Step 2: Define Your Data Model
This is the step most PIM projects get wrong. A flawed data model creates structural problems that compound over time and are expensive to fix once data is in the system.
Your data model defines how products are structured inside the PIM: what product families exist, what attributes belong to each family, how variants relate to parent products, and how products connect to each other (accessories, spare parts, sets).
Taxonomy is your product classification system: the hierarchy of categories and subcategories that organizes your catalog. It's separate from product families, though the two interact closely. A well-designed taxonomy reflects how your customers and sales teams think about your products, not how your ERP happens to have them coded.
For a kitchen appliance manufacturer, the taxonomy might be: Appliances > Cooking > Ovens > Built-in Ovens. Each level serves a purpose. Category pages, navigation, and marketplace feed mappings all depend on getting this hierarchy right from the start.
Taxonomy design also includes controlled vocabularies: the standardized value lists for attributes like material, color, or certification type. If ten people can enter free text for "color," you'll end up with "Red," "red," "RED," "signal red," and "RAL 3001" all meaning different things to different systems. Define controlled vocabularies as part of your data model, not as an afterthought.
Product families group products that share the same attribute set. A power tool manufacturer might have families for drills, grinders, and batteries. Each family has its own attribute template. Getting family boundaries right matters because changing them later means remapping data.
Attributes are the individual data fields: voltage, weight, color, material, certification, description. For each attribute, define its type (text, number, boolean, list, date), whether it's required, and whether it varies by channel or locale.
Variants represent product configurations that share a base product but differ on specific axes, typically size, color, or material. The variant logic needs to be modeled explicitly. A product that comes in 6 sizes and 4 colors is one parent with 24 variants, not 24 separate products.
Relations cover how products connect to other products. An industrial sensor might relate to compatible mounting brackets, calibration accessories, and replacement parts. These relations are often ignored in early PIM implementations and then bolted on awkwardly later. That's a predictable regret. Model them now.
In practice, the data modeling phase regularly took two to four weeks. That's not a sign that something is wrong. That's the work being done properly. Rushing it to get to the "real" implementation is how you end up rebuilding your product structure six months in. A poorly designed data model just gives you an expensive place to store messy data.
If your PIM supports a fully configurable data model, use that flexibility deliberately. AtroPIM lets you define and modify product families, attribute groups, and relations without developer involvement, which makes iteration during modeling much faster. You can restructure product families or add attribute groups as your model evolves without touching code. But configurability is only useful if you have a clear model to configure toward.
PIM Implementation Step 3: Choose the Right PIM
By the time you're seriously evaluating PIM software, you should have a clear data model draft, a known integration landscape, and some sense of how many users will work in the system. That context makes the selection much more concrete.
On-premise vs. SaaS. On-premise gives you data control and the ability to customize deeply. SaaS reduces infrastructure overhead. For companies with strict data sovereignty requirements or complex customization needs, on-premise or self-hosted open source is often the better fit. For companies that want to minimize IT involvement, SaaS makes sense.
Open source vs. proprietary. Open source PIMs offer full code transparency, no vendor lock-in, and often lower total cost at scale. The trade-off is that you need internal technical capacity or a reliable implementation partner. Proprietary SaaS PIMs are faster to start with, but lock you into the vendor's roadmap and pricing model.
Beyond the deployment model, the criteria that matter most in practice are:
- Data model flexibility: can you define your own product families and attribute structures, or are you constrained by the vendor's defaults?
- Integration options: native connectors to your ERP and e-commerce platform, or will you be building custom integrations?
- API quality: a well-documented REST API matters if downstream systems consume product data programmatically.
- Scalability: can it handle your catalog five years from now, not just today?
- Module structure: can you start with core functionality and add capabilities as you need them, or do you pay for everything upfront?
Run a proof of concept before committing. Import a representative sample of your real data, one or two product families, a few hundred products, and configure the data model you designed in Step 2. This surfaces integration friction, data model mismatches, and usability issues that no vendor demo will show you.
AtroPIM is worth evaluating seriously if you need a configurable, open source solution with built-in DAM, native PDF catalog and datasheet generation, and a clean REST API with per-instance documentation. It's built on the AtroCore data platform, which covers more than classic PIM use cases: integration management, business process automation, and general data management are all within scope. It supports both on-premise and SaaS deployment and follows a start-small-and-scale model through free and paid modules. For manufacturers with complex catalogs and real integration requirements, that combination is often a better fit than SaaS-only options with limited configurability.
One area worth asking about during evaluation is AI-assisted enrichment. Around 35% of PIM users have already <a href="https://wifitalents.com/product-information-management-industry-statistics/" target="_blank" rel="noopener nofollow">integrated generative AI into their product description workflows. Whether that matters for your project now or in 18 months, it's worth knowing what your chosen platform supports natively versus through third-party tooling.
PIM Implementation Step 4: Plan Your Data Migration
Migration is where good intentions meet bad data. It's the step in a PIM implementation that separates projects with clean go-lives from those that spend six months firefighting after launch.
Every data source needs a responsible person. The ERP export needs someone from IT or operations. The spreadsheets need whoever manages them. Supplier data files need a procurement or category manager. Without that ownership, preparation work sits in limbo.
Clean before you migrate. This is the "garbage in, garbage out" problem in practice. Before any import, deduplicate records across all source systems. Standardize attribute values: a field that contains "yes", "Yes", "YES", "y", and "1" for the same boolean needs to be resolved before import, not after. Correct obvious errors: wrong units, misassigned categories, broken image references. Fill gaps where you can with reasonable effort. Flag what can't be fixed quickly and decide whether to import it incomplete or hold it for a later batch.
The productivity gains from doing this properly are real. With a well-configured PIM, the average time to enrich a product drops from around 4 hours to 15 minutes, and the cost of creating a new product SKU falls by up to 25%. Those numbers only hold if the underlying data is clean. Migrating dirty data erases most of that efficiency from day one.
This phase takes longer than most teams expect. Budget for it explicitly.
Build a migration mapping document that shows where each source field lands in the PIM. This will surface mismatches between how your ERP structures product data and how your PIM expects it. Some transformation logic will be needed. Build it into your import scripts or ETL process, not as manual corrections after the fact.
Run test imports before the real thing. Import a subset first, validate completeness, attribute mapping, media linkage, and variant structure, then fix errors in the source data or mapping. Fix problems at the source, not by hand in the PIM after import.
You don't have to migrate everything at once. Migrate the product families you need for go-live and handle the rest in subsequent phases. One pattern we see often: a manufacturer spends months preparing a complete catalog migration, discovers problems late, and delays go-live by weeks. A phased migration of two or three core product families would have put them live earlier and given them real experience with the system before tackling the complex parts.
PIM Implementation Step 5: Set Up Integrations
A PIM that isn't connected to your systems is just a database. Integration is what makes it operational.
ERP integration is usually the most critical. Your ERP is typically the source of truth for product identifiers, pricing, and stock data. The PIM needs to receive master product records from the ERP and, in some cases, write enriched data back. Define clearly which system owns which fields. Overlapping ownership creates sync conflicts that are tedious to diagnose.
E-commerce integration determines how your webshop consumes product data from the PIM: descriptions, attributes, media, categories, relations. Decide whether the PIM pushes data to the shop on a schedule, on change, or whether the shop pulls via API. Each model has different implications for data freshness and error handling.
Print and PDF outputs are often underestimated. If you produce printed catalogs, datasheets, or price lists, your PIM should generate them natively or feed a structured print workflow. AtroPIM includes native PDF generation for product datasheets and catalogs, with configurable templates. For manufacturers who produce datasheets across hundreds of SKUs, that removes the dependency on manual InDesign work and external tooling for standard output formats.
Marketplace and retailer feeds require ongoing operational attention. If you distribute through marketplaces or supply product data to retail partners, your PIM needs to format and export data to their specifications. Automating this through the PIM rather than handling it manually is worth the setup effort.
Before go-live, test each integration with real data. Verify that product updates in the PIM propagate correctly to downstream systems. Check that ERP changes, new products, discontinued items, are reflected in the PIM. Confirm that media assets are delivered at the right resolution and format for each channel.
PIM Implementation Step 6: Go Live Incrementally
Once integrations are validated, the question is how to go live. Waiting until everything is ready and launching the whole thing at once consistently produces delayed launches and chaotic first weeks in production.
Start with the product categories that matter most to your business right now. Not the easiest ones, not the smallest ones: the ones where better product data has the most immediate commercial impact. For a manufacturer of industrial equipment, that's likely the two or three product lines that drive the majority of revenue. Get those into the PIM, validated, and live first.
Each phase needs clear exit criteria: which product families are migrated, which channels are receiving data from the PIM, which integrations are active. Without exit criteria, phases blur into each other and scope creeps in both directions.
Phase one should cover your core channels and most important product families with primary attributes. Later phases add complexity:
- Additional product families with more involved attribute structures
- Secondary channels: marketplaces, retailer portals, additional locales
- Deeper attribute coverage: technical specs, richer media, regulatory data
- Automation: workflow rules, approval processes, automated channel publishing
- Advanced relations: accessory mapping, spare part linkage, product sets
This sequencing matters because phase one will teach you things that change how you approach phase two. Edge cases in variant logic, integration quirks with your ERP, attribute structures that don't quite fit the real data: these surface in production, not in planning.
Run UAT before go-live. Have your Data Manager and a few product editors work in the system for a week before launch. They will find workflow gaps, missing attribute validations, and confusing navigation that no amount of technical testing surfaces. UAT should also cover system performance: if your e-commerce platform pulls a full catalog refresh nightly, test that under realistic load. Fix what you find before launch, not during it.
Assign data ownership before go-live, not after. Every product family needs a named owner: someone responsible for completeness, accuracy, and ongoing maintenance. This doesn't have to be a dedicated role, but it has to be someone's explicit responsibility. At minimum, define who can create new attributes, who approves products before they publish, and how incoming supplier data gets validated. A few simple rules, consistently applied, prevent most of the entropy that kills PIM data quality over time.
The companies that get the most out of PIM long-term are not the ones who implemented the most features. They're the ones who kept their data clean and their processes honest.
Common PIM Implementation Mistakes Beginners Make
Skipping data modeling. The most common version of this is rushing into software configuration to feel like progress is happening. The data model gets defined on the fly, product families get created ad hoc, and six months later the team is remapping everything. Slow down before Step 2, not after.
Importing dirty data. The cleanup doesn't disappear when you hit import. It just moves into a system that's harder to bulk-edit than a spreadsheet. Do it before migration, not after.
Over-scoping phase one. The ambition to go live with the entire catalog, all channels, and all integrations at once is understandable. It's also the most reliable way to delay go-live by months. Scope phase one to what's essential. Get it live. Then build.
Buying for features you won't use for years. Some PIM vendors sell on the breadth of their feature list. Evaluate against your actual requirements for the next 18 months. A modular PIM that lets you add capabilities as you need them is more useful than a fully loaded system you'll spend years configuring.
No data owner post-launch. Without ownership, nobody fixes errors, nobody maintains completeness standards, and the PIM gradually becomes unreliable. Teams stop trusting it and build workarounds instead. That's a predictable and avoidable outcome.
Treating PIM as an IT project. PIM touches product management, marketing, sales, and sometimes procurement and compliance. IT can own the technical implementation, but the business functions who will use the system need active involvement from the start. A system designed by IT without that input will fit IT's interpretation of what product editors need, not what they actually need.
The companies that complete a PIM implementation successfully tend to share one trait: they treated the data work as seriously as the software selection.