WHAT DOES A DATA STEWARD DO?
Updated: Mai 19, 2025 - The Data Steward curates and maintains research data sources according to FAIR principles to ensure data is findable, accessible, interoperable, and reusable. This role involves developing a comprehensive data model to meet laboratory needs for data management and quality while serving as the primary contact for Local Master Data with stakeholders. Support is provided to the Master Data Lead and business teams to implement controls and ensure compliance with data management policies.
A Review of Professional Skills and Functions for Data Steward
1. Data Steward Responsibilities
- Data Quality Assurance: Help to resolve data quality problems by providing support through the appropriate choice of error detection and correction.
- Process Improvement: Process control and improvement, or process design strategies collaborating with subject matter experts (SMEs).
- Data Management: Execute updates to Biomarker Assay Portfolio master data (e.g. customer, vendor, status, assay, etc.).
- Automation Development: Develop macros or other automation systems for gathering and updating the portfolio.
- Collaboration: Obtain information and documentation with other functional data owners.
- Data Quality Management: Assist in data management and data quality of portfolio requirements with other functional data owners.
- Data Integrity: Ensure functional data integrity is consistent.
- Data Quality Maintenance: Ensure data integrity in key systems and maintain the processes to support the data quality.
- Business Collaboration: Work closely with the business owners to ensure data rules.
- Database Alignment: Ensure the portfolio structure aligns with the structure of other functional areas' databases.
- Mass Update Responsibility: Take responsibility for performing mass updates.
- Project Participation: Participate in projects and initiatives across multiple functional areas and regions.
2. Data Steward Job Summary
- Data Management: Building a knowledge base of what data is crucial and how data is managed.
- Policy Improvement: Improving data policies and procedures to enhance data capture.
- Stakeholder Engagement: Meeting with senior management to represent the department.
- Training and Development: Staff training to ensure consistent data entry.
- Quality Assurance: Responsible for data quality and continuous improvement of the data.
- Best Practices Review: Reviewing and improving data storage best practices.
- Quality Improvement Identification: Identify areas for data quality improvements.
- Data Management Oversight: Manage data in the Core Integrated Operations Group.
- Data Requirement Definition: Identify valuable data sources together with the business and help define data requirements.
- Advanced Analytics: Combine data sources and apply advanced analytics techniques to discover trends, patterns, and insights and target/enable omnichannel personalization.
- Actionable Insights Translation: Translate key findings into actionable insights that business owners understand.
3. Data Steward Accountabilities
- Data Stewardship: Acting in the capacity of responsible steward of detailed information and data policies and standards, supporting the MOD Data Governance organization.
- Policy Implementation: Implementing information and data policies and standards through the data stewardship process.
- Team Contribution: Contributing to the Data Services team to support the formulation, evolution, and development of policies, standards, and processes.
- Process Ownership: Being the process owner for the Information and Data Stewardship Process.
- Leadership: Leading Information and Data Stewardship activities, formulating, organizing, and running such groups and teams.
- Secretariat Role: Acting as the secretariat for the Data Governance and Protection Board.
- Business Engagement: Managing the interface to the business and engaging with and responding to requests for information.
- Governance Management: Take responsibility for managing data governance.
- Staff Development: Develop and grow one direct report (Data Analyst).
- Collaboration: Closely collaborate with business subject matter experts (Scientists/Lab Technicians), IT, digital teams, and internal/external partners.
- Digital Transformation: Contribute to the digital transformation of the organization by conveying new ways of working that are related to digital.
4. Data Steward Job Description
- Data Management: Management of Local Item Master Data within the M3 ERP system of both Ledbury and Nantwich OIEUK sites.
- Global Data Creation: Assisting with global data creation tasks via the data identification software portal.
- BoM Management: Managing BoM changes through effective communication with the relevant stakeholders using workflow software (Item approval portal).
- Technical Support: Supporting and resolving day-to-day local master data queries of both sites on technical and system-related issues.
- Training Documentation: Review and update training documentation and process notes for all local master data processes to enable coverage of key tasks if required and provision of future training.
- Continuous Improvement: Continuous improvement of local master data processes and related systems to ensure efficient and accurate ways of working.
- Process Review: Review of master data-related business processes and workflows, ensuring they meet the needs of the business effectively and engaging with stakeholders to steer changes.
- Data Governance: Maintenance and governance of all business global and local master data.
- Reporting Development: Development of value-adding reporting, corrections, and communication of changes where relevant (maintenance/housekeeping tasks).
- Budget Support: Support budget processes concerning underlying related master data.
- Team Collaboration: The key point of contact for the global master data team, encouraging a collaborative team working atmosphere and promoting continuous improvement across both teams.
5. Data Steward Overview
- Data Management: Establish, curate, and maintain research and early development data sources and interfaces according to the FAIR (Findable, Accessible, Interoperable, Re-usable) principles.
- Standard Definition: Define the standards for FAIR data generation in labs and their usage and support the users with implementing the standards.
- Data Integration: Establish and maintain a comprehensive data and access model for research and early development (e.g., pharmacology) data and integrate the model and data into the overall landscape.
- Laboratory Needs Assessment: Understand the needs of laboratories regarding their data, data management, and quality and contribute to solutions to deliver high-quality FAIR data.
- Stakeholder Coordination: Main point of contact for local master data with business stakeholders for projects and business changes.
- Continuity Coordination: Coordinate cover for local master data administrator in the event of absence, ensuring business continuity for all day-to-day master data requirements.
- Project Support: Assist the OIEUK master data lead with embedding and owning the master data global design and new Evolve project ways of working.
- Workstream Support: Support all business workstream teams with relevant projects or other ad-hoc requirements from time to time.
- Process Control Guidance: Guide businesses to implement necessary controls and remediate gaps within operations business processes to comply with MUAH data management policy.
- Risk Management Guidance: Guide on data governance risks and controls during operations risk and control self-assessments (RCSA).
- Education and Partnership: Partner with and educate business process owners and division risk managers on data concepts, including data governance, data policy, data quality, metadata, business glossary, lineage, taxonomies, and data consumers.