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Data Stewardship

Data management and qualityIntermediate Level

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

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What is Data Stewardship?

Data stewardship is the practice of managing a company's information to keep it useful and safe. People called data stewards act as caretakers for this data. They make sure information is accurate, organized, and easy to find. These experts connect technical IT teams with the business staff who use the data every day. A data steward has several main tasks: * Creating rules for how to enter and format information. * Fixing mistakes or adding missing details. * Checking and approving updates to important records. * Writing clear definitions for data terms so everyone understands them. * Making sure the company follows privacy laws and security rules. This role ensures that a company has high-quality information in all its systems. When data is clean, businesses can trust their reports and launch new products with confidence. WISEPIM supports these teams by providing a central hub to manage and monitor product data in one place.

Why Data Stewardship matters for e-commerce

Data stewardship is the practice of managing and protecting product information to keep it accurate. It ensures that every detail, from prices to descriptions, is correct before a customer sees it. Poor data leads to unhappy shoppers and more product returns. Data stewards act as guardians of your information. They fix errors early and keep data consistent across all sales channels. This process helps teams make better decisions based on facts. Tools like WISEPIM help data stewards organize this work in one place.

Examples of Data Stewardship

  • 1A PIM manager is a data steward for product descriptions. They ensure all new content follows brand rules and has no errors.
  • 2An IT specialist is the data steward for technical details. They check that all data uses the right formats and units.
  • 3A marketing team member is the data steward for media files. They make sure images and videos are high quality and link to the right products.
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How WISEPIM Helps

  • WISEPIM lets you assign specific roles to team members. This clarifies who is responsible for each part of your product data.
  • Data stewards can create quality rules directly in WISEPIM. These rules find and fix errors before they reach your online store.
  • WISEPIM tracks data changes and manages the approval process. This ensures all product information meets your company standards.

Common mistakes with Data Stewardship

  • Not assigning specific people to manage data. This creates confusion about who is responsible for fixing errors.
  • Failing to set clear rules for data. Without these standards, information becomes messy and hard to use across the company.
  • Treating data management as a one-time project. You must check data regularly or it will become outdated and lose its value.
  • Not providing staff with the right tools or training. Employees cannot manage data effectively without proper support and knowledge.
  • Managing data separately from the rest of the business. Data stewardship works best when it connects to daily tasks and goals.

Tips for Data Stewardship

  • Give each data steward clear tasks and specific data areas. This stops confusion about who is responsible for which information.
  • Write simple rules and definitions for your data. Ensure all employees can easily find and understand these guidelines.
  • Check your data quality often. Use clear measurements to find and fix mistakes before they cause bigger problems.
  • Provide regular training and helpful tools to your data stewards. This helps them manage data accurately as your company grows.
  • Link data tasks directly to your PIM system like WISEPIM. This makes keeping data clean a natural part of every employee's daily work.

Trends around 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

Access Control (PIM)Attribute Dependency ManagementAttribute InheritanceAttribute ManagementAttribute mappingAttribute Scoping

Also Known As

Data custodianData ownerData governance role

Frequently Asked Questions

Data governance establishes the policies, processes, and standards for data management across an organization, while data stewardship is the operational implementation of these policies. Data stewards are the individuals or teams responsible for executing the governance framework for specific data sets.

Data stewardship improves product data quality by assigning specific individuals responsibility for data accuracy, completeness, and consistency. These stewards actively monitor data, resolve discrepancies, enforce data entry standards, and ensure that product information meets all internal and external requirements, reducing errors before publication.

Successful implementation of data stewardship in e-commerce involves defining clear roles and responsibilities for managing specific data assets, such as product information. It requires establishing robust data quality standards and processes for data creation, enrichment, and approval. Additionally, leveraging PIM systems and data governance tools can significantly streamline stewardship efforts and ensure consistent data across all platforms.

Data stewardship is crucial for multichannel e-commerce because it ensures product information consistency and accuracy across all touchpoints, from websites to marketplaces and social commerce. Without it, discrepancies can arise, leading to customer confusion, increased returns, and damage to brand reputation. Stewards actively manage data, preventing errors and ensuring a unified customer experience.

In an e-commerce company, data stewardship responsibilities are often distributed among various roles or departments, depending on the organizational structure. This can include product managers, PIM specialists, IT data owners, marketing teams, or even a dedicated data governance committee. The key is to assign formal accountability for specific data domains to ensure quality and compliance.

An e-commerce organization should consider establishing formal data stewardship roles when facing challenges like inconsistent product data, increasing regulatory compliance requirements, or expanding into new sales channels. It becomes particularly important as data volume and complexity grow, making manual data quality control unsustainable. Proactive establishment helps prevent data issues rather than reacting to them.

Success is typically measured through KPIs such as data completeness, accuracy rates, and a measurable reduction in product returns caused by incorrect information. You can also track the time-to-market for new products, as efficient stewardship speeds up the enrichment process. Ultimately, improved conversion rates often reflect the higher quality of product data maintained by stewards.

A data steward requires features like bulk editing tools, automated validation rules, and workflow management to ensure data consistency across thousands of SKUs. They also rely on audit trails to track historical changes and data quality dashboards that highlight missing or incorrect attributes. Integration capabilities are also vital for syncing validated data seamlessly between the ERP and sales channels.

Data stewards resolve conflicts by establishing a hierarchy of truth where specific trusted suppliers or internal departments are prioritized over others. They use automated matching and merging logic to identify discrepancies and manually intervene when automated rules cannot determine the most accurate value. This process ensures the customer sees a single, verified version of the product story regardless of the data source.

Yes, small teams can implement data stewardship by assigning specific data quality responsibilities to existing roles like product managers or content editors. To be successful, these team members must have clearly defined data standards and use automated validation tools to minimize manual oversight. As the product catalog grows, the workload usually necessitates transitioning to a specialized role to maintain high standards.

Many businesses fail by treating data stewardship as a one-time cleanup project rather than a permanent operational process. A frequent pitfall is assigning stewardship tasks to IT staff who lack business context, leading to data that is technically valid but commercially useless. Organizations also struggle when they don't document their data standards; without a clear rulebook, different team members will inevitably categorize products or format attributes inconsistently, recreating the very mess they intended to fix.

The return on investment typically comes from a mix of lower operational costs and higher conversion rates. Effective stewardship reduces the labor hours spent manually fixing errors and lowers the rate of customer returns caused by inaccurate product descriptions. By maintaining 'clean' data, retailers can launch new products across marketplaces much faster, directly impacting revenue while reducing the overhead required to manage a growing catalog.

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