Migrating data from Excel to a Product Information Management (PIM) system allows companies to optimize product data handling and improve operational efficiency. This process typically involves transferring large collections of product information—including descriptions, prices, images, and specifications—into a centralized, accessible, and feature-rich system. Below, we explore the practical elements of this migration, including preparation, technologies, and execution.

Understanding PIM

PIM (Product Information Management) systems are tools designed to centralize, organize, and systematically manage product information. They ensure data consistency across multiple platforms, such as e-commerce websites, ERP systems, or marketplaces. All of this makes PIM indispensable for companies handling large amounts of product data.

While Excel spreadsheets are commonly used for managing product data, scaling businesses typically creates the need for the advanced features of PIM systems. PIM provides functionalities like automated data verification, enhanced categorization, and real-time updates, which are crucial for maintaining accurate and consistent product information.

There are 2 most common ways to initially import data into PIM software.

Using Data Import Feature

You can import excel files (XLSX) directly, if your chosen PIM software support it, or convert them to CSV files. CSV format is usually supported by all PIM systems.

Using REST APIs

APIs enable seamless integration for large-scale migrations or frequent updates. While highly efficient, APIs require significant development resources. Therefore this migration method is not recommended unless absolutely necessary.

In this article we concentrate on manual data import via import feeds.

Steps for Migrating Data from Excel to PIM (Initial Data Import into PIM)

Data Preparation

Importing an Excel file directly into a PIM system is rarely a seamless process. The data format and structure often require adjustments before importing. It’s essential to understand the import capabilities of your chosen PIM software and modify your data accordingly.

To streamline the process, prepare separate sheets for each data entity. For example, create individual sheets for products, attributes, product categories, and any custom data entities. Each sheet should be structured to allow line-by-line processing during import. Often you may have more flexibility during import if you prepare sheets for data relations separately - e.g. for linking products with categories, or linking products with digital assets and other files, linking products with classifications.

Some foundational data, known as dictionaries, may need to be entered manually before importing other data. Dictionaries include general reference data used across your system, such as measurement units, countries, and currencies. Many PIM systems require these to be set up in advance.

If manual data transformation is too time-consuming, consider using ETL (Extract, Transform, Load) tools like Talend or Apache NiFi. These tools can extract data from Excel, transform it to match the PIM’s required format, and load it efficiently, saving time and reducing errors.

Data Type Unification

The key advantage of PIM over Excel is its strict data typing. In Excel, you can enter any value into a cell without restrictions, whereas in a PIM system, each attribute must have a defined data type, ensuring that only valid values are assigned.

PIM systems can support a wide range of data types—some offering more than 20. Common types include string, text, HTML, list, date, integer, and float. Additionally, data types can be simple or combined. For example, a value range like "1..3 mm" consists of multiple elements: a starting value (integer), an ending value (integer), and a measurement unit (list).

Using the appropriate data types will enable the product filtering on your website or online store, ensuring more accurate and efficient search results.

To prevent import errors, ensure that all attribute values are formatted correctly during data preparation. Think twice about of which data type should be every attribute you want to use.

Data Cleaning

Eliminate duplicates, standardize formats (e.g., dates and currencies), unify data types for values, and correct errors.

Data Mapping

The initial stage of the migration procedure involves aligning the data from your Excel sheet with the relevant fields and attributes in the PIM system. This is crucial since product data in Excel may not consistently align with the PIM system's structure. For instance, Excel may include product details such as names, descriptions, and product attributes in individual columns, whereas the PIM system might require these fields to be organized in a particular format.

Many PIM solutions like AtroPIM provide clear and detailed instructions on data mapping and preparation.

Executing the Data Import

Once data is prepared and mapping rules are configured, you can execute the import. For CSV files, upload them using the PIM’s import functionality. After importing, review the data for errors, inconsistencies, or missing entries. Use the PIM’s error logs to address any issues. After you corrent the errors you can reimport data again. Make sure you imported everything you had.

Testing and Finalizing

Start with a small test import to verify data alignment and correct field displays. Address any issues before proceeding with the full migration.

Once complete, evaluate the imported data across platforms (e.g., e-commerce websites). Optimize product categorization and workflows to enhance system performance.

Best Practices for Data Migration

These best practices are designed for businesses implementing a Product Information Management (PIM) system for the first time. They focus on the initial data import, ensuring a smooth transition from Excel or other manual methods to a fully integrated PIM. Once the import is complete, businesses can rely solely on the PIM for managing and maintaining product data.

Adjust the PIM Data Model Before Import

Customize the PIM’s data model to meet business needs, ensuring attributes and relationships are correctly defined. For example, if you sell apparel, ensure the system includes attributes like "Size," "Material," and "Color."

Keep in mind that product data varies significantly between different users. While some businesses require standard attributes like color and size, others need additional configurations to properly describe their products. For instance, some users may need to visualize product features using icons. To accommodate such needs, the PIM data model should be adjusted accordingly. All of this configuration can be prepared in Excel before import, ensuring a smooth transition and accurate data representation.

AtroPIM allows users to configure any kind of export feeds, depending on your custom data model configuration, and then convert it into an import feed. This method provides a ready-made template with properly structured data, eliminating the need to manually create separate import feeds. By generating an import feed from an export file, you can see how the data should be formatted, ensuring all required columns are correctly named. This simplifies the process and reduces the risk of errors.

Understand Data Structure

A thorough understanding of how the PIM structures data is crucial for a successful migration. Start by analyzing existing data structures through export feeds, which can provide insight into key fields like "Product ID" and "Description." This step helps businesses identify gaps and inconsistencies in their data before attempting an import.

Once the structure is understood, the next step is to align import files with the PIM’s format. Ensuring that fields like "SKU" and "Price" are consistently named and structured minimizes errors and improves compatibility between exported and imported data. Businesses should also validate and clean the data before importing to eliminate inconsistencies, correct errors, and ensure that all necessary information is included. Organizing data properly before migration prevents potential issues and ensures a smoother transition into the PIM system.

Plan Data Types

Selecting the right data types for product attributes is crucial to ensure smooth filtering and seamless data import. Different systems have varying requirements, so preparing an Excel file with properly structured data is essential.

For attributes, numeric fields should be used for prices, weights, dimensions, and quantities, while text fields work best for product names, descriptions, and brand names. Dropdown lists with predefined values are useful for categories like "Electronics" or "Furniture", as well as for sizes and colors to maintain consistency. Boolean fields (Yes/No) can be applied to attributes such as "In Stock" or "Free Shipping," and date fields should be used for information like expiration dates, promotions, or warranty periods.

Users often overlook data classification, which can lead to incorrect imports, filtering issues, and system errors. Standardizing data formats prevents failures and ensures compatibility across different platforms. To prepare an import-ready Excel file, each column should align with a specific attribute and follow the correct data type. Using validation rules or dropdown lists helps maintain consistency, and properly formatting date fields avoids misinterpretation. Testing a sample import before full implementation helps identify potential errors.

By structuring data correctly, businesses can enhance filtering accuracy, improve user experience, and ensure successful product imports across various systems.

Organizing Product Attributes for Different Uses

In a product information system (PIM) or e-commerce platform, different attributes serve different purposes depending on the department that uses them. Some attributes are essential for customers, while others are crucial for internal operations but not displayed on product pages.

For example, marketing-related attributes like "Product Title," "Key Features," and "Promotional Tags" are crucial for driving sales and customer engagement. Support-related attributes such as "Warranty Period" or "User Manual Link" help provide post-purchase assistance. Logistics-related attributes like "Warehouse Location," "Stock Keeping Unit (SKU)," and "Supplier Details" are vital for inventory management but are not necessary on the customer-facing product page.

To ensure efficient data management, businesses should clearly separate customer-facing attributes (visible on e-commerce pages) from internal attributes (used for operations, logistics, and support). This distinction helps streamline product data storage in the PIM system while ensuring customers only see relevant information.

Prepare Data for Each Product Classification Separately

Organizing and preparing data separately for each product classification is crucial for maintaining data accuracy, consistency, and usability. Different product classifications often have distinct attributes and data types. For example, technical specifications for electronic devices differ significantly from those for clothing or furniture. Preparing separate sheets for each classification will save you significant time by allowing you to use dedicated columns for relevant attribute values.

Use CSV, not XLSX

CSV files are typically processed more quickly, making them ideal for handling large datasets. However, their main drawback is the lack of support for multiple tabs. If you need to organize your data across multiple sheets, Excel is a better choice.

Use Import Feed Templates

Follow the import templates provided by your PIM system to ensure compatibility. If the template includes fields like "Product Name," "Description," and "Image URL," your file should match this structure to prevent import errors.

Many PIM systems come with pre-configured import feeds, but modifying your data model may render them unsuitable, as they will no longer align with your structure. Some systems, like AtroPIM, do not have predefined import feeds but offer the flexibility to configure them according to your needs.

Test, Monitor, and Validate Data

Conduct a small-scale test import, such as uploading 10 products, to verify that key fields like "Price" and "Category" are correctly mapped. After the import, monitor the data closely and review the PIM’s error logs for any discrepancies, such as mismatched categories, missing images, or formatting issues. Address any errors by adjusting the mapping or correcting data, ensuring everything aligns properly before proceeding with the full import. This process helps ensure a smooth migration and prevents larger-scale issues later on.

Import and Link Assets with Products Efficiently

Use URLs to reference files whenever possible. For example, instead of manually uploading images, include links like "http://example.com/images/product1.jpg" in the image fields. This ensures a smoother import process and keeps file associations intact. It's also important to import main images and related assets in a single job to maintain proper linkage with the corresponding products.

However, in cases where images are stored locally and URLs are not available, users must first upload the files manually, often through a bulk upload feature. Once uploaded, these images can then be linked to products via an import feed, ensuring a structured and efficient asset management process.

Pitfalls to Watch Out For

Not all PIM systems support the data types users expect. Some may not allow attribute ranges like "10 to 20 cm" or lists of countries, limiting how data can be structured. Each system has its own constraints, and not all support every possible data format.

Additionally, some PIM systems may restrict which data types can be imported, even if they are available within the system. There can also be limitations on how imports and exports interact with the data model. A key advantage of AtroPIM is its flexibility—it allows the import and export of all data types, regardless of the data model, ensuring greater adaptability.

When preparing data for import, it’s best to organize it in Excel but save files in CSV format before importing. CSV files are typically processed more efficiently by PIM systems, as they are lightweight and avoid potential formatting issues that can occur with Excel files. While Excel supports over a million rows, working with very large files can slow performance and cause crashes, whereas CSV files are generally more reliable for handling extensive datasets.

Key Takeaways

Migrating data from Excel to a PIM system enhances product data management and operational efficiency. Before importing, data must be cleaned, mapped, and formatted to match the PIM system’s requirements. Using CSV files instead of Excel ensures better performance and fewer import issues. Some PIM systems have limitations on data types and import capabilities, while flexible solutions like AtroPIM allow full customization. Efficient asset management involves linking images via URLs or bulk uploads. Testing with sample data helps identify errors early, and reviewing error logs ensures data accuracy. Structuring product attributes properly optimizes both internal operations and customer-facing information.