Migrating Data from Excel to PIM: A Comprehensive Guide

Key Takeaways

While Excel can handle initial product data management, it quickly becomes limited in scalability, consistency, and collaboration. The way you perform your initial PIM Excel migration has a direct impact on the long-term success of your PIM system because early decisions set the foundation for data quality, structure, and usability.

Mistakes early on can create errors, inconsistencies, and complications in future updates, while proper planning, strict data types, and clean, mapped Excel data enable smooth imports, integrations, and multi-channel distribution. Post-import validation and governance keep data accurate and efficient. Read the full guide to understand the complete Excel to PIM transition process, from learning how PIM systems work to executing a successful data migration and avoiding common pitfalls.

Understanding PIM

A PIM system centralizes, organizes, and manages product information across multiple channels.

Unlike Excel spreadsheets, Product Information Management (PIM) systems provide the functionality required for managing product data at scale:

• Automated data verification and validation
  PIM applies predefined rules to identify missing, inconsistent, or invalid data.

• Product categorization and classification
  The system maintains structured product hierarchies and attribute sets.

• Real-time data updates
  Propagates approved changes to connected systems as they occur.

• Multi-channel data distribution
  Supplies product information to e-commerce platforms, ERP systems, and marketplaces from a single source of truth.

• Strict data typing and constraints
  Enforces formats, value ranges, and required fields to reduce errors.

• Collaborative workflows and version control
  Supports multi-user editing with change tracking and approval processes.

• Search and filtering functionality
  Enables efficient querying of large product catalogs.

Migration Methods: Quick Comparison

Manual Data Import (Recommended for Initial Migration)

This guide covers manual data import through import feeds that use CSV or XLSX files. This method is perfect for businesses using PIM for the first time, as it requires minimal technical expertise and lets you prepare and check your data carefully.

API Integration

APIs allow easy connections for large migrations or regular updates. Although they are very effective, APIs need a lot of development resources. They are best used when ongoing synchronization with other systems is essential.

The PIM Excel Migration Process: Step-by-Step

Phase 1: Pre-Migration Planning

1. Assess Your Current Data

Before starting pim excel migration migration, audit your existing Excel data carefully:

• Identify all sources of product information (multiple spreadsheets, databases, files)
• Document the current data structure and organization
• Count the total number of products and SKUs to migrate
• Identify data quality issues (duplicates, inconsistencies, missing values)
• Decide which data is essential and which is optional

2. Customize Your PIM Data Model

Set up the PIM system to fit your business needs before importing data:

• Define product types and classifications (e.g., apparel, electronics, furniture)
• Create custom attributes for your industry
• Set up attribute groups for logical organization (marketing, logistics, technical specs)
• Configure measurement units, currencies, and languages
• Establish product relationships and hierarchies

Pro tip: Some PIM systems allow you to export a sample feed to help you understand the required data structure. You can then use this as a template for your import file. This reverse-engineering method can simplify the preparation process. More advanced systems like AtroPIM can convert export feeds into import feed templates, automatically displaying the correct column structure and naming conventions.

3. Set Up Foundational Data (Dictionaries)

Enter reference data manually before importing products:

• Measurement units (kg, lb, cm, inches)
• Currencies (USD, EUR, GBP)
• Countries and regions
• Tax categories
• Product categories and attribute sets

Phase 2: Data Preparation

1. Organize Data by Entity Type

Create separate sheets or files for different data entities:

• Products - core information (SKU, name, description)
• Attributes - specifications and features
• Categories - classification and taxonomy
• Relationships - product-to-category, product-to-asset links
• Assets - images, videos, documents
• Variants - size, color variations
• Pricing - base prices, tiered pricing

Structuring data separately provides flexibility and simplifies troubleshooting.

2. Implement Data Type Standardization

The key advantage of PIM over Excel is strict data typing.

PIM systems enforce predefined data types for each attribute, validating values at entry and preventing incorrect formats. Excel allows flexible cell input with minimal enforcement, which often leads to mixed or inconsistent data.

Therefore plan data types carefully, because these definitions determine how data is validated, stored, and shared across all systems, and mistakes can create issues later.

Common PIM Data Types:

• String/Text - product names, brand names, short descriptions (limited characters)
• HTML/Rich Text - long descriptions, formatted content
• Integer - quantities, counts, whole numbers
• Float/Decimal - prices, weights, dimensions
• Boolean - Yes/No flags (In Stock, Featured, Discontinued)
• Date/DateTime - launch dates, expiration dates, warranty periods
• List/Dropdown - predefined values (colors, sizes, categories)
• Multi-select - multiple values from predefined list
• Range - value ranges like "10-20 cm" (requires special formatting)
• URL - links to external resources or assets

Best practices:

• Use numeric types (integer, float) for any values requiring calculations or filtering
• Apply dropdown lists for standardized values to ensure consistency
• Implement Boolean fields for simple yes/no attributes
• Format dates consistently (ISO format: YYYY-MM-DD recommended)
• Store measurement units in separate columns - keep numeric values and units apart (e.g., "weight_value" and "weight_unit" instead of "10kg")
• Split combined values into multiple columns - range values like "10..20mm" should be divided into three distinct columns: "range_min" (10), "range_max" (20), and "range_unit" (mm). This structure enables proper filtering and data validation.

3. Clean and Validate Data

Eliminate data quality issues before import:

• Remove duplicates - identify and merge duplicate products
• Standardize formats - consistent date formats, currency symbols, measurement units
• Correct errors - fix spelling mistakes, invalid values, formatting issues
• Fill gaps - complete missing required fields
• Normalize values - unify variations (e.g., "Blue", "blue", "BLUE" → "Blue")
• Validate references - ensure category IDs, attribute codes exist

For large-scale transformations, consider ETL (Extract, Transform, Load) tools like Talend, Apache NiFi, Pentaho, or Microsoft Power Query (built into Excel).

4. Map Excel Columns to PIM Fields

Create a mapping document that aligns your Excel structure with PIM requirements:

Mapping Examples:

Example: Splitting Combined Values

Instead of storing combined measurements in a single column:

Use separate columns for each component:

This structure enables proper data validation and filtering, and ensures compatibility with PIM range data types.

Phase 3: Execution

1. Prepare Import Files

• Use CSV for large datasets (faster processing, more reliable)
• Use XLSX when you need multiple tabs for organization during preparation
• Always convert to CSV before final import for best performance

File preparation checklist:

• Column headers match PIM field names exactly
• All required fields are populated
• Data types are correctly formatted
• No empty rows between data
• UTF-8 encoding for special characters
• One entity type per file
• Separate files for each product classification - different product types (electronics, apparel, furniture) should have dedicated import files since each classification uses distinct attribute sets

2. Import Assets Efficiently

Method 1: URL References (Recommended)

Include direct URLs to images and files in your import feed:

product_sku,image_url
PROD-001,https://example.com/images/product001.jpg
PROD-002,https://example.com/images/product002.jpg

The import process is faster, with automatic file association and no need for manual uploads. It also supports images hosted on a CDN or external servers.

Method 2: Bulk Upload with Asset Linking

For locally stored files:

1. Bulk upload all images/files to the PIM media library
2. Note the internal file IDs or paths
3. Create an import feed linking products to uploaded assets
4. Import the relationship file

Pro tip: Import main product images and supplementary assets in a single import job to maintain proper linkage.

3. Execute Test Import

Always test before full migration:

1. Select 10-20 representative products covering different product types
2. Execute test import
3. Review imported data in the PIM interface
4. Check error logs for issues
5. Verify data displays correctly on all channels (e-commerce, mobile, etc.)
6. Validate relationships (categories, assets, variants)

Common test import findings include field mapping errors, data type mismatches, missing required fields, incorrect category assignments, broken image links, and formatting issues in descriptions.

4. Full Migration Execution

Once testing is successful:

1. Import foundational data first (categories, attributes, dictionaries)
2. Import main product data
3. Import product relationships (category assignments, related products)
4. Import assets and link to products
5. Import variants and pricing
6. Import additional metadata

5. Post-Import Validation

After importing:

• Review PIM error logs for any import failures
• Verify data completeness (all expected products imported)
• Check data accuracy (fields populated correctly)
• Test product search and filtering
• Validate multi-channel distribution
• Confirm image and file associations

If errors are found, document the specific issues from the logs, correct the data in the source files, re-import only the failed records if the PIM supports incremental updates, and then validate the corrections.

Phase 4: Optimization and Finalization

1. Organize Attributes by Use Case

Structure attributes based on their purpose:

• Customer-Facing (visible on product pages): Product title, features, specifications, promotional tags
• Marketing (campaigns and SEO): Meta descriptions, keywords, marketing copy, search terms
• Operations (internal only): Warehouse location, supplier info, internal notes, cost data
• Support (customer service): Warranty info, user manual links, troubleshooting guides

This separation ensures customers see only relevant information while teams access the operational data they need.

2. Optimize Product Classifications

Prepare separate import files for each product classification, as different classifications require distinct attribute sets:

• Electronics require technical specifications (voltage, power consumption, connectivity standards, processor type)
• Apparel needs size charts, material composition, care instructions, and fit type
• Furniture requires dimensions, weight capacity, assembly requirements, and material finish
• Food products need nutritional information, allergens, expiration dates, and storage conditions

Why separate files matter: Each product classification uses a different set of attributes. Creating classification-specific import files allows you to include only relevant columns for that product type, making the import process cleaner and more efficient. For example, your electronics file includes "voltage" and "warranty_period" columns, while your apparel file has "fabric_type" and "care_instructions", with no overlap. This approach reduces file complexity and minimizes errors.

3. Establish Ongoing Data Governance

Set up processes for maintaining data quality post-migration:

• Define data ownership and responsibilities
• Create data entry guidelines and standards
• Implement approval workflows for changes
• Schedule regular data quality audits
• Train team members on PIM best practices

Best Practices for Successful Excel PIM Data Migration

Before Import

Before importing data, it is important to understand your PIM's data structure by exporting sample data to see the required format. Start with clean data, addressing quality issues before import rather than after. Use the import templates provided by the PIM system exactly as given, and plan for relationships by mapping out how entities connect prior to importing.

During Import

During the import process, follow a logical order, starting with dictionaries, then categories, products, relationships, and finally assets. Monitor error logs immediately after each import to catch issues early, and maintain backup files by keeping the original Excel files for reference.

After Import

After importing data from Excel to PIM, validate the data thoroughly across all channels, test filtering and search functionality to ensure data types work as expected, and document the process by recording mapping decisions and configurations.

Common Pitfalls in Excel PIM Data Migration and How to Avoid Them

Data Type Limitations

Not all PIM systems support every data type you might need. To avoid issues, research your PIM's supported data types before migration, test complex types such as ranges or multi-values early, have workarounds ready (for example, store ranges as text if not natively supported), and consider PIM flexibility, as some systems offer more comprehensive import/export capabilities than others.

Import/Export Restrictions

Some PIM systems restrict which data types can be imported, even if they exist in the system. To prevent problems, review import documentation thoroughly before selecting a PIM, test import capabilities during the evaluation phase, look for systems that support full data model import/export flexibility, and verify that specific data types like ranges, multi-select, or custom fields are supported for both import and export.

Performance Issues with Large Files

Large datasets can slow down or crash during import. Mitigate this by splitting large files into smaller batches (10,000–50,000 rows each), using CSV format for better performance, importing during off-peak hours, and gradually increasing batch sizes based on system performance.

Relationship Mapping Errors

Products may not be properly linked to categories, variants, or assets. To avoid errors, import parent entities before children (categories before products), use consistent identifiers (IDs or SKUs) across all files, validate that all referenced IDs exist before importing relationships, and handle relationships in a separate, dedicated import step.

Character Encoding Issues

Special characters may display incorrectly after import. Always use UTF-8 encoding for CSV files, test special characters during a test import, check your PIM's encoding requirements, and avoid proprietary formats.

Missing Required Fields

Imports can fail if mandatory data is missing. Document all required fields before starting, validate them in Excel before export, use conditional formatting to highlight empty required fields, and run completeness checks before importing.

Conclusion

Excel PIM data migrating transforms product data management from a manual, error-prone process to an automated, scalable system. The initial investment in proper migration pays dividends through improved data quality, efficiency, and scalability. A well-executed PIM migration lays the foundation for growth and multi-channel success.

Remember: migration is not just a technical exercise, but an opportunity to establish data governance practices that will serve your business for years to come. Take the time to do it right, and your PIM system will become an invaluable asset for product management, marketing, and sales operations.