How Bad Quality Product Data Impacts PIM Migrations and Implementations

Migrating to a new Product Information Management (PIM) system or implementing one from scratch is a complex task. One of the biggest challenges companies face during this process is dealing with bad quality product data. Poor data quality can slow down the migration, increase costs, and even cause the new system to fail in delivering its expected benefits. Understanding how bad product data affects PIM projects helps businesses prepare better and avoid costly mistakes.

Inconsistent product data sheets causing migration delays

Why Product Data Quality Matters in PIM Projects

Product data is the foundation of any PIM system. It includes descriptions, specifications, images, pricing, and other details that define each product. When this data is inaccurate, incomplete, or inconsistent, it creates problems at every stage of migration and implementation.

Key reasons product data quality matters:

• Data consistency ensures products are represented uniformly across channels.

• Data completeness guarantees all necessary information is available for customers and internal teams.

• Data accuracy prevents errors that can lead to customer dissatisfaction or regulatory issues.

• Data structure affects how easily data can be imported and mapped into the new system.

  
  

Without good quality data, the PIM system cannot deliver reliable product information, which defeats its purpose.

Common Issues Caused by Bad Product Data During Migration

When migrating to a new PIM, bad product data causes several specific problems:

• Extended timelines: Cleaning and fixing data takes time, delaying the migration schedule.

• Increased costs: More resources are needed to correct data errors and rework imports.

• Mapping errors: Poorly structured data makes it difficult to match fields between old and new systems.

• Data loss: Incomplete or corrupted data can be lost during transfer, causing gaps.

• User frustration: Teams relying on the PIM get frustrated with unreliable or missing product info.

  
  

For example, a retailer migrating thousands of SKUs found that 30% of their product descriptions were inconsistent or outdated. This forced a lengthy manual review before migration could proceed, pushing the project back by several months.

How Bad Data Affects PIM Implementation Success

Even after migration, bad product data continues to impact the success of PIM implementations:

• Poor customer experience: Inaccurate or missing product details confuse buyers and reduce trust.

• Operational inefficiencies: Teams spend extra time fixing data issues instead of focusing on growth.

• Channel inconsistencies: Different sales channels show conflicting product information.

• Compliance risks: Incorrect data can lead to regulatory violations, especially in industries like food or electronics.

• Limited analytics value: Bad data skews reports and insights, leading to poor decision-making.

  
  

A manufacturer that implemented a PIM without cleaning their product data first found that their online catalog displayed conflicting specs across regions. This inconsistency led to increased returns and customer complaints.

PIM dashboard highlighting product data errors during implementation

Steps to Prevent Bad Data from Derailing Your PIM Project

To avoid the pitfalls of bad product data, companies should take proactive steps before and during PIM migrations and implementations:

• Conduct a thorough data audit to identify gaps, duplicates, and inconsistencies.

• Clean and standardize data by fixing errors, filling missing fields, and applying consistent formats.

• Define clear data governance policies to maintain quality over time.

• Involve cross-functional teams including product managers, IT, and marketing to validate data.

• Use data migration tools that support validation and error reporting.

• Test data imports in stages to catch issues early.

• Train users on the importance of data quality and how to maintain it.

  
  

By investing time upfront in data preparation, companies can reduce migration risks and ensure their PIM delivers value quickly.

Real-World Example: Successful PIM Migration with Clean Data

A global apparel brand planned to migrate to a new PIM system to support their expanding online presence. Before migration, they performed a detailed data audit that revealed 25% of product attributes were missing or inconsistent. They dedicated a team to clean and enrich the data, standardizing product descriptions and images.

During migration, they used automated tools to validate data and fix errors. The result was a smooth transition with minimal downtime. Post-implementation, the brand saw improved product data consistency across channels and a 15% increase in online sales attributed to better product information.

This example shows how addressing data quality early can lead to a successful PIM project and measurable business benefits.