Back to E-commerce Dictionary

Data Ingestion

Data management11/27/2025Intermediate Level

Data ingestion is the process of collecting, importing, and processing raw data from various sources into a system like a PIM, often involving initial validation and transformation.

What is Data Ingestion? (Definition)

Data ingestion refers to the process of transferring raw data from one or more sources into a destination system, such as a Product Information Management (PIM) system, a database, or a data warehouse. This process involves collecting data from disparate origins, which can include ERP systems, supplier portals, spreadsheets, or third-party data providers. Once collected, the data undergoes initial processing steps like validation, cleansing, and sometimes basic transformation to ensure it meets the requirements of the receiving system. Effective data ingestion is critical for maintaining a comprehensive and up-to-date central repository of product information.

Why Data Ingestion is Important for E-commerce

For e-commerce, efficient data ingestion is fundamental to keeping product catalogs current and accurate across all online channels. It enables rapid onboarding of new products and updates to existing ones, which is essential for dynamic online markets. Poor data ingestion processes can lead to outdated information, product listing errors, and delays in bringing products to market, directly impacting sales and customer trust. A well-managed ingestion process ensures that the PIM system receives all necessary product data promptly and in the correct format, laying the groundwork for further enrichment and syndication.

Examples of Data Ingestion

  • 1Automatically importing new product SKUs and basic attributes from an ERP system into a PIM using an API connector.
  • 2Uploading supplier product data sheets (e.g., in Excel or CSV format) into a PIM for initial processing.
  • 3Integrating with a third-party data provider to pull in enriched content like compliance certifications or lifestyle imagery.
  • 4Receiving daily inventory updates from a warehouse management system into the PIM.

How WISEPIM Helps

  • Flexible connectors: Connect to various internal and external data sources for seamless data ingestion.
  • Automated data mapping: Map incoming data fields to your PIM's data model, reducing manual effort.
  • Pre-ingestion validation: Implement rules to check data quality and consistency before it enters the PIM.

Common Mistakes with Data Ingestion

  • Failing to validate data at the ingestion point, leading to dirty or inaccurate information propagating through systems.
  • Lack of data standardization across sources, resulting in inconsistent formats, units, and terminology.
  • Relying heavily on manual ingestion processes, which are prone to human error, slow, and not scalable.
  • Ignoring data governance, leading to unclear data ownership, quality standards, and update protocols.
  • Inadequate error handling and logging during ingestion, making it difficult to identify and resolve data flow issues.

Tips for Data Ingestion

  • Implement robust data validation rules at the source and during ingestion to catch errors early.
  • Standardize data formats, units, and attributes across all source systems before ingestion.
  • Automate data ingestion workflows using connectors and APIs to minimize manual intervention and improve efficiency.
  • Establish clear data governance policies, including data ownership, quality standards, and regular auditing.
  • Monitor data ingestion processes continuously for performance, errors, and data quality to ensure smooth operation.

Trends Surrounding Data Ingestion

  • AI-driven data quality and mapping: Utilizing AI and machine learning for automated data cleansing, enrichment, and intelligent schema mapping during ingestion.
  • Real-time data ingestion: Shifting from batch processing to streaming data pipelines to enable immediate updates and real-time product information availability.
  • Automated data pipelines: Implementing end-to-end automation for data ingestion, transformation, and loading (ETL/ELT) to reduce manual effort and accelerate data flow.
  • Event-driven architecture: Adopting event-driven approaches for data ingestion, where data changes trigger immediate updates across connected systems.
  • Integration with headless commerce: Developing ingestion strategies that support flexible data delivery to various headless frontends and channels.

Tools for Data Ingestion

  • WISEPIM: A PIM system that centralizes product data, offering robust connectors and APIs for efficient data ingestion from various sources like ERPs, supplier portals, and spreadsheets.
  • Akeneo PIM: Provides comprehensive product information management capabilities, including tools for importing and structuring product data from diverse origins.
  • Salsify PXM: A Product Experience Management platform that facilitates data ingestion, enrichment, and syndication across multiple channels.
  • Talend: An open-source data integration platform offering extensive ETL capabilities for complex data ingestion and transformation workflows.
  • Informatica PowerCenter: An enterprise-grade ETL tool widely used for large-scale data integration, warehousing, and ingestion projects.

Related Terms

data-modelapi-application-programming-interfacedata-transformationproduct-onboardingpim-integration-strategy

Also Known As

Data importData loadingData acquisition