How to prepare data for Product Information Management: Checklist for a smooth PIM implementation
A PIM project starts well before the software goes live. One of the first tasks is preparing the product information that will enter the system. How do you turn spreadsheets, supplier files, and scattered documents into data your team can use with confidence?
PIM data preparation is the process of collecting, mapping, cleaning, enriching, and validating product information before importing it into a Product Information Management system. The aim is to make sure each product can be identified correctly and its information fits the agreed data model.
In this guide, we explain the PIM data prep process step by step, share examples, and provide a checklist you can use with your team. Whether your data lives in spreadsheets, an enterprise resource planning (ERP) system, or shared folders, start by identifying what you have and what needs attention.
Why is PIM data preparation so important?
Many companies want to start using PIM as quickly as possible. A useful first question is: are your product data and working practices ready for the import?
Preparation helps you agree on sources, product structures, attribute mappings, and quality rules. This gives the team a clear view of what is ready and what still needs a decision or correction.
- Easier mapping: consistent names, units, and attributes are easier to match to the target model.
- Fewer corrections after import: duplicates, missing values, and conflicts can be addressed earlier.
- Clearer quality checks: the team knows which information is required for each product type and channel.
- A more manageable start: users receive structured records and agreed rules for maintaining them.
- More useful product information: consistent descriptions, specifications, and images help customers compare products and make a choice.
If you need support with these decisions, our PIM implementation services cover analysis, configuration, integrations, and team preparation.
What are the best practices for PIM data preparation?
Good PIM data preparation starts with a defined scope, clear ownership, and rules that the whole team can apply. These practices help you prepare a batch that can be checked and imported consistently:
- Choose a manageable scope. Specify the product families, languages, markets, and channels included in the first batch.
- Assign an authoritative source for each attribute. Agree which source provides the approved value when systems disagree.
- Approve the model before mapping. Confirm how products, variants, categories, and assets relate to one another.
- Document transformations. Record how you convert units, standardize values, and handle missing information so the process can be repeated.
- Test realistic cases. Include complex products and updates to existing records in your pilot.
- Ask business users to review the results. Technical checks should be complemented by people who understand the products and publishing requirements.
Keep the source register, mapping file, validation results, and approval decisions together. These records become a practical reference for future imports.
A step-by-step guide to the PIM data preparation process
Step 1: Identify sources and export product data
Before preparing an import, build a clear picture of where your product information lives.
What to do:
↳ List relevant spreadsheets, supplier files, PDFs, databases, ERP systems, and content management systems.
↳ Identify the data they contain: product identifiers, names, descriptions, specifications, images, and documents.
↳ Record the owner and last update date of each source. Export working copies while keeping the originals protected.
↳ Agree which source provides the approved value for each type of information.
Example: Marketing keeps product descriptions in a spreadsheet, the technical team maintains specifications in the ERP, and images sit in shared folders. A common identifier or an approved mapping table lets you connect the records. The source inventory also makes conflicting values visible before they enter the PIM.
You can find more examples in the article about product data sources in PIM.
Step 2: Map fields and define the product model
The next task is to match the source data to the structure of your PIM.
What to do:
↳ Define product types, categories, variants, bundles, and relationships.
↳ Map source columns to target attributes, including their data types and language or channel settings.
↳ Identify required fields and document the rules used to transform values.
Example: A source field contains 20 × 30 × 15 cm. Confirm the dimension order with the source owner before splitting it into height, width, and depth. If the agreed order is height × width × depth, the three attributes become 20 cm, 30 cm, and 15 cm respectively.
A field combining color, dimensions, and warranty details also needs a clear rule for separating the information. Missing or ambiguous values should be reviewed by someone who knows the product.
Ask your implementation partner to visualize the model. A simple diagram helps your team check the relationships before importing data. If Pimcore is your target platform, our Pimcore implementation services can support modeling and integration planning.
Step 3: Clean and standardize the data
Now focus on errors, duplicate records, and inconsistent values.
What to do:
↳ Identify duplicates and decide which record and values to retain.
↳ Standardize units and date formats. Convert numeric values when changing units.
↳ Use controlled lists for attributes such as color, material, and size.
↳ Correct spelling, inconsistent terminology, and incomplete entries.
Example: After confirming they mean the same size, map XL, Extra Large, and X-Large to one approved value. Keep the mapping so the same rule can be applied to future supplier files.
Product status and stock availability describe different things. An active product can temporarily be out of stock, so keep these values in separate fields with their own source and update rules.
Step 4: Enrich product information
Once the data is organized, add the information customers need to understand and compare your products.
What to do:
↳ Complete descriptions and specifications using verified source information.
↳ Link images, videos, manuals, and other documents to the correct products and variants.
↳ Prepare approved language versions and market-specific content.
↳ Check each channel's requirements for attributes, images, titles, and descriptions.
Example: A record labeled “Laptop XYZ, 15.6-inch display” can be expanded with the confirmed screen resolution, processor model, memory, storage, dimensions, and warranty. Each specification should be supported by a reliable source.
Keep import requirements separate from publication requirements. A product may be accepted into PIM while still needing a translated description or image before it is ready for a particular market.
Check out what information customers look for before making a purchase.
Step 5: Validate data and agree on update rules
Before importing, check the prepared files against the model and quality rules your team has approved.
What to do:
↳ Verify required values, formats, units, allowed options, and product relationships.
↳ Check that identifiers match the correct records, including any leading zeros or case rules.
↳ Ask product, marketing, and other relevant reviewers to check meaning and accuracy.
↳ Decide how blank cells, missing columns, and explicit deletion instructions will be handled.
Example: An empty description in an update file might mean “keep the current value” or “clear the existing description.” Agree on the intended behavior and test the import configuration before updating existing products.
Import behavior depends on the platform and configuration. For example, Akeneo's import documentation describes a field-level option for clearing an existing value when the source cell is empty. This is a decision to verify in your import profile. Our Akeneo implementation services can help configure and test the rules for your project.
Step 6: Test and import the prepared data
Your data has been cleaned, enriched, and checked. The next step is to test how it behaves in the target PIM before importing the agreed batch.
Step 6A: Run a representative test import
Choose a sample that covers the product types and data cases in scope. Its value comes from what it tests, so include simple products, variants, bundles, languages, assets, missing values, and updates to existing records.
What to do:
↳ Run the import in a suitable test environment where available.
↳ Review created, updated, skipped, and rejected records against the source file.
↳ Check attributes, relationships, translations, and linked assets with business users.
↳ Correct the source data or transformation rules and repeat the test, including a re-import of existing products.
↳ Where channel integrations are ready, verify the resulting product information there as a separate check.
Example: If your catalog includes electronics, clothing, and furniture, test a smartphone with storage and color variants, a garment in several sizes, and a sofa with related accessories. Also include a product whose description is being updated.
Keep corrections reproducible. If you fix a record directly in PIM, decide how that correction will be preserved when the source file is imported again.
Step 6B: Import the approved batch
After the pilot is accepted, import the agreed scope using a supported file format or a configured system integration. This might use an API, an interface that allows systems to exchange data.
Review the import logs and reconcile the results with the source data.
Release products to connected channels according to the approved publishing rules. A successful import confirms that data has entered the PIM; channel readiness also requires the relevant content, configuration, and integration checks.
Step 7: Keep data quality under control
Product data continues to change after the first import. Establish a repeatable process for new products and updates.
What to do:
↳ Assign responsibility for creating, editing, reviewing, and approving information.
↳ Monitor completeness, rejected records, and unresolved data issues.
↳ Review mappings when source files, product models, or channel requirements change.
↳ Use the validation and approval features supported by your PIM configuration.
Example: After marketing updates a product description, the configured workflow sends it to a product specialist for review before publication. Define this responsibility and test the workflow as part of the setup.
If your project uses Ergonode, our Ergonode implementation services can help align the configuration and integrations with your team's way of working.
What are the common issues in PIM data preparation and how can you resolve them?
Common preparation issues include ambiguous identifiers, conflicting values, mixed formats, and unclear update rules. Use the checks below while preparing the source files.
| Issue in the prepared data | What to do before import |
|---|---|
| An identifier such as 00123 loses its leading zeros in a spreadsheet. | Preserve identifiers as text and verify them against the approved cross-system mapping. |
| Product status and stock availability are combined in one field. | Separate their meanings, source systems, and update rules. |
| Dimensions contain three numbers in an unknown order. | Confirm the order and units before splitting them into attributes. |
| The same measurement appears in centimeters and inches. | Convert values to the agreed unit and record the conversion rule. |
| Images are linked to the wrong color or language version. and declarations | Check the product, variant, market, and language assignments in the asset list. |
| Empty cells may clear existing information during an update. | Define and test how the import handles blank values, omitted fields, and deletion instructions. |
For a broader review of technical and organizational import risks, see our guide to PIM data import issues and ways to prevent them.
What is the user's role in PIM data preparation?
Your users bring the product knowledge needed to decide whether information is correct, complete, and useful. Assign responsibility for specific decisions:
- Product specialists confirm specifications, variants, relationships, and the meaning of attributes.
- Marketing and channel owners review descriptions, images, translations, and publication requirements.
- Data owners resolve conflicting sources and approve the prepared batch or documented exceptions.
- Administrators and integration specialists configure mappings, access, import rules, and test runs.
Ask these users to review representative records together. A file can pass format checks and still contain a description or relationship that is wrong for the product. For the wider project, read our guide to client and technology partner responsibilities in PIM.
Practical lesson from Tandemite's ZARYS project
In our work for ZARYS, we connected SAP, Pimcore, and Shopware. The project required coordination between teams, detailed documentation, and testing of how the systems worked together.
The practical lesson for data preparation is to document where each value comes from, who approves it, and how it reaches the target system. This is why the checklist includes source ownership, mappings, and evidence from test imports. Read the ZARYS case study.
How should you protect data during PIM preparation?
Start with least-privilege access: each person and integration should have only the permissions needed for its task. Apply this to source files, working copies, import profiles, and the PIM itself.
- Use named accounts and review who can view, edit, approve, export, and import data.
- Store files in approved locations and use protected transfer methods.
- Keep passwords and API credentials in approved credential storage, separate from product spreadsheets.
- Keep a protected source copy and agree how to recover from an incorrect import.
- Include only the data needed for the product information process, and review access to confidential or unreleased materials.
Check which controls your platform and hosting arrangement support, and assign responsibility for them. For example, permission to run an import and permission to change its configuration may be managed separately.
Final thoughts
A PIM system is only as good as the data you feed into it.
Preparing product information becomes more manageable when you work through a clear sequence: identify sources, model and map the data, clean and enrich it, validate the results, and test the import. Keep the decisions and checks documented so your team can apply them to the next batch.
You ensure that your product data isn’t just “moved” to a new system but truly optimized to support business growth.
Would you like a data preparation checklist tailored to your business, product model, and sales channels? Or would you like to discuss a specific stage? Contact us.
At Tandemite, we work with your team to understand the data, configure the PIM, and plan the integrations around your requirements. We can also support your team after launch as your catalog, processes, and channels develop.
FAQ
What is the purpose of PIM data preparation?
The purpose of PIM data preparation is to make product information accurate, consistent, and compatible with the target PIM model before import. It brings together source records, matches products and variants, standardizes values, and defines quality requirements. The result is a prepared dataset with documented mappings, owners, and checks that the team can use for the first import and future updates.
What are the best practices for PIM data preparation?
Start with a defined product scope, assign data owners, and agree on an authoritative source for each attribute. Approve the product model, document mappings and transformations, and define rules for missing values and updates. Test a representative sample that includes complex products and existing records. Ask business users to review the results and record their approval before importing the full agreed batch.
What are the common issues and solutions in PIM data preparation?
Common issues include duplicate or altered identifiers, conflicting values, inconsistent units, missing assets, and unclear handling of blank fields. Resolve them by maintaining an identifier mapping, assigning authoritative sources, standardizing values, checking asset links, and testing update behavior. Keep an issue list with an owner and next action for each unresolved item so the team can assess whether the batch is ready.
What is the role of a user in PIM data preparation?
Users supply and validate the business meaning of product data. They confirm specifications, approve descriptions and images, resolve source conflicts, and check whether records meet channel requirements. Data owners approve the prepared batch, while administrators or integration specialists configure and test the import. Involving these people early helps identify errors that a format check alone may miss.
What is the best practice for security in PIM data preparation?
A useful starting point is least-privilege access: give users and integrations only the permissions their tasks require. Combine this with approved file storage and transfer methods, protected credentials, a recoverable source copy, and clear responsibility for import execution. Review access to confidential materials and retain records of important changes. The exact controls depend on your PIM, hosting setup, and company security policies.
