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Data Quality

Data Management1/5/2026Basic Level

The measure of how accurate, complete, consistent, timely, and valid your product information is.

What is Data Quality? (Definition)

Data quality is a measure of how useful and reliable information is for its intended purpose. In a PIM system, it ensures your product details are correct and dependable for customers. High-quality data helps you avoid errors that lead to product returns or lost sales. It means your information is: * Accurate: Correct values. * Complete: No missing fields. * Consistent: Uniform across all channels. * Timely: Up to date. * Valid: Follows defined business rules.

Why Data Quality is Important for E-commerce

Data quality determines how reliable and useful your product information is for shoppers. High-quality data builds customer trust and leads to more sales. When descriptions are accurate, customers return items less often because they receive exactly what they expected. Search engines also rank complete product pages higher in search results. Poor data quality causes lost revenue and creates extra work for your team. Using WISEPIM ensures your data stays consistent and accurate across every platform.

Examples of Data Quality

  • 1Check that all product weights and sizes are accurate.
  • 2Ensure brand names use the same spelling for every product.
  • 3Make sure all EAN codes use the correct format and length.
  • 4Check that all required fields are complete before you export data.
  • 5Update price and stock levels often to keep the information correct.

How WISEPIM Helps

  • Validation Rules let you set automatic checks. The system looks for missing or wrong information as soon as you add or import data.
  • Completeness Scores show you exactly how much data is finished. You can see at a glance which products are ready for specific sales channels.
  • Bulk Editing helps you fix mistakes or update details for many products at once. This keeps your information consistent across your entire catalog.
  • Data Profiling scans your product data to find odd patterns or errors. It helps you find and fix quality problems before they reach your customers.
  • Controlled Workflow sets up a review process for your team. This ensures someone checks the data for accuracy before it goes live on your webshop.

Common Mistakes with Data Quality

  • Many companies treat data quality as a one-time task. This causes information to become outdated and inaccurate over time.
  • Businesses often fail to set clear rules or assign people to manage data. This results in messy and inconsistent information.
  • Teams often forget to set goals or metrics for their data. Without these, you cannot track progress or find major errors.
  • Some organizations fix errors but ignore why they happen. Bad forms or a lack of rules will cause the same mistakes to repeat.
  • Cleaning data by hand is slow and leads to mistakes. Use automated checks to catch errors quickly and save time.

Tips for Data Quality

  • Create a clear plan for your data. Assign specific tasks to team members so everyone knows who creates, updates, and checks the information.
  • Use automated rules in your PIM system to catch mistakes early. These rules check if information is missing or formatted incorrectly as soon as someone enters it.
  • Check your data health often using reports and dashboards. These tools show you where errors happen most so you can fix them quickly.
  • Fix the most important errors first. Focus on cleaning up product prices and key details that directly affect whether a customer buys an item.
  • Train your team regularly on how to handle data. Give them simple guides that explain your standards so everyone follows the same rules.

Trends Surrounding Data Quality

  • AI and Machine Learning for Automated Data Validation and Enrichment: AI-driven tools analyze data patterns, identify anomalies, and suggest corrections or enrichments automatically, reducing manual effort.
  • Real-time Data Quality Checks: Integration of data quality validation directly into transactional systems and PIM platforms ensures data is clean at the point of entry and throughout its lifecycle.
  • Data Quality as a Core Component of Headless Commerce: Ensuring consistent, high-quality product data across all touchpoints (web, mobile, IoT) is crucial for a unified customer experience in headless architectures.
  • Predictive Data Quality: Leveraging analytics to identify potential data quality issues before they impact operations or customer experience, allowing for proactive intervention.
  • Emphasis on Data Sustainability: Businesses increasingly focus on reducing data waste, ensuring data is not only accurate but also relevant and ethically sourced, aligning with broader sustainability goals.

Tools for Data Quality

  • WISEPIM: A PIM solution that centralizes product data, offering robust validation rules, completeness checks, and data governance features to ensure high data quality.
  • Akeneo PIM: Provides comprehensive data quality dashboards, validation rules, and enrichment capabilities to manage and improve product information.
  • Salsify PIM: Focuses on product experience management with strong data quality features, content syndication, and digital asset management.
  • Informatica Data Quality: An enterprise-grade platform offering extensive capabilities for data profiling, cleansing, monitoring, and governance across various data sources.
  • Talend Data Quality: Offers tools for data profiling, cleansing, matching, and monitoring, available in both open-source and commercial editions for improved data reliability.

Related Terms

pimdata governancecompletenessdata cleansingdata validationdata integritydata enrichmentbusiness rules

Also Known As

Data AccuracyData IntegrityInformation Quality