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Data Stewardship

Data management11/27/2025Intermediate Level

Data stewardship involves the responsibility for managing, overseeing, and ensuring the quality and integrity of an organization's data assets.

What is Data Stewardship? (Definition)

Data stewardship refers to the formal responsibility assigned to individuals or teams for managing and overseeing specific data assets within an organization. Data stewards ensure that data adheres to defined quality standards, policies, and regulatory requirements. Their tasks often include defining data standards, resolving data quality issues, approving data changes, documenting data definitions, and ensuring compliance with data governance policies. This role is critical for maintaining trustworthy and reliable data across all systems and operations.

Why Data Stewardship is Important for E-commerce

In e-commerce, effective data stewardship directly impacts product data quality, which is fundamental to customer trust and operational efficiency. Poor data quality leads to incorrect product descriptions, missing attributes, and outdated pricing, resulting in customer dissatisfaction, increased returns, and lost sales. Data stewards ensure that product information is accurate, complete, and consistent across all digital touchpoints, from product detail pages to marketing campaigns. This proactive management of product data assets minimizes errors and supports confident decision-making.

Examples of Data Stewardship

  • 1A PIM manager acts as a data steward for product descriptions, ensuring all new content follows brand guidelines and is free of errors.
  • 2An IT specialist is the data steward for technical specifications, verifying that all attribute values conform to predefined formats and units.
  • 3A marketing team member is responsible for managing media assets, ensuring images and videos are high-resolution and correctly linked to products.

How WISEPIM Helps

  • Clear data ownership: WISEPIM helps define and assign data stewardship roles, ensuring clear accountability for specific product data sets.
  • Integrated quality checks: Data stewards can implement and monitor data quality rules directly within WISEPIM, preventing issues at the source.
  • Streamlined data governance: Support data stewards with tools for version control, audit trails, and workflow approvals to enforce governance policies.

Common Mistakes with Data Stewardship

  • Not formally assigning data stewardship roles, leading to ambiguous data ownership and accountability.
  • Failing to establish clear data quality standards, policies, and definitions, resulting in inconsistent data.
  • Treating data stewardship as a one-time project rather than an ongoing operational process, causing data quality to degrade over time.
  • Providing insufficient training and resources to data stewards, hindering their ability to effectively manage data assets.
  • Implementing data stewardship in isolation, without integration into broader data governance frameworks or business processes.

Tips for Data Stewardship

  • Clearly define the scope, roles, and responsibilities for each data steward to avoid overlaps and gaps in data ownership.
  • Establish clear data definitions, quality rules, and governance policies that are easily accessible and understood by all stakeholders.
  • Implement a robust data quality monitoring framework with measurable KPIs to track data health and identify issues proactively.
  • Provide continuous training and support to data stewards, equipping them with the necessary skills and tools to perform their duties effectively.
  • Integrate data stewardship activities into existing business workflows and PIM systems to embed data quality into daily operations.

Trends Surrounding Data Stewardship

  • AI and Machine Learning integration for automated data quality checks, anomaly detection, and data enrichment, reducing manual effort for data stewards.
  • Increased focus on data ethics and privacy, with data stewards ensuring compliance with evolving regulations like GDPR and ethical use of data in AI applications.
  • Shift towards proactive data governance, where data stewards leverage automation to prevent data issues rather than reactively resolving them.
  • Adoption of Data Mesh architectures, localizing data stewardship responsibilities within domain-specific teams for greater agility and ownership.
  • Emphasis on sustainability data management, with data stewards ensuring the accuracy and consistency of environmental and social impact data for reporting and compliance.

Tools for Data Stewardship

  • WISEPIM: A PIM solution that centralizes product data, enabling data stewards to define, enrich, and validate product information for consistency and quality across channels.
  • Collibra: A leading data governance platform offering data cataloging, data lineage, business glossary, and data quality capabilities to support data stewards.
  • Akeneo: An open-source PIM system that provides workflows for data enrichment, validation, and approval, crucial for data stewards managing product information.
  • Salsify: A Product Experience Management (PXM) platform combining PIM, DAM, and syndication, helping data stewards manage product content across diverse sales channels.
  • Informatica Data Governance & Compliance: A comprehensive suite of tools for data catalog, data quality, and master data management, empowering data stewards in large enterprises.

Related Terms

data-governanceproduct-data-qualitydata-validationmaster-data-management-mdmproduct-data-management

Also Known As

Data custodianData ownerData governance role