Data Stewardship Practices
Overview
Data stewardship practices help with managing, protecting, and ensuring the quality of organizational data assets throughout their lifecycle. Rather than being a separate job title, stewardship is a role or relationship to data many employees already act as stewards because they define, produce, or use data as part of their existing responsibilities. These stewardship activities bridge technical data management capabilities and business requirements, ensuring that data remains accurate, accessible, and valuable for decision-making while maintaining compliance with regulatory and organizational standards.
Purpose
This handbook page intends to clarify data stewardship responsbilities and the benefits of implementing data stewardship acorss data domains. The page is a good starting point for data team, application (system) owners and functional analytics teams to know about data stewardship practices and responsbilities.
Core Responsibilities of Business Data Stewards
Data Quality Management
- Monitor data accuracy, completeness, and consistency across systems
- Collaborate with Governance team to help establish and maintain data quality metrics and thresholds
- Support in data quality issue identification, impact assessment and solution validation
- Support implementation of data validation rules and quality checks
- Coordinate data cleansing initiatives
Data Documentation and Metadata Management
- Create and maintain comprehensive data dictionaries
- Collaborate with analytics engineers to document data lineage and transformation processes
- Ensure proper data classification and tagging
- Maintain business glossaries and definitions
- Update system documentation and process flows
Data Access and Security
- Ensure correct stakeholders have access to appropriate dashboards
- Ensure compliance with data privacy regulations and SAFE guidelines
- Monitor data usage and access patterns
- Validate data masking and anonymization procedures
Stakeholder Collaboration
- Serve as liaison between business users and technical teams
- Participate in data governance stewardship committee meetings
- Provide training and support to data consumers
- Help resolve data-related disputes and conflicts such as right metrics to use, aligning on metric definitions and calculataions etc.
Process Improvement
- Identify opportunities for data process optimization
- Drive automation of data stewardship activities
How Data Stewardship Practices Help
Having Data Stewardship provides multifold benefits to the organization, department and the inidividual itself. Some of these benefits such as financial impacts aren’t directly related to the stewardship responsibilities but an outcome of overall improved operating model.
Organizational Benefits
Enhanced Decision Making
- Reliable, high-quality data supports better business decisions
- Consistent data definitions eliminate confusion and misinterpretation
- Improved data accessibility accelerates analysis and reporting
Risk Mitigation
- Lower operational risk through better data quality controls
- Enhanced data security and privacy protection
- Decreased likelihood of data-related business disruptions
Operational Efficiency
- Streamlined data processes reduce manual effort and errors
- Standardized procedures improve consistency across teams
- Better data discovery capabilities reduce time spent searching for information
- Automated quality checks prevent downstream issues
Financial Impact
- Cost savings from reduced data errors and rework
- Improved revenue through better customer insights and targeting
- Enhanced operational efficiency leading to cost optimization
Technical Benefits
Data Quality Improvement
- Consistent monitoring and remediation of data issues
- Proactive identification of quality problems before they impact business
- Improved data integration and interoperability
System Reliability
- More stable data pipelines through quality controls
- Reduced system downtime due to data-related issues
- Better performance through optimized data structures
Individual Career Benefits
Professional Growth for Stewards
- Development of business acumen and domain expertise beyond technical skills
- Better understanding of enterprise data architecture and systems
- Improved project management and stakeholder relationship capabilities
- Recognition as a trusted advisor and subject matter expert within the organization
Common Misconception About Data Stewardship
Misconception 1: Data Stewardship is Purely Technical
Reality: Data stewardship is primarily a business function that requires deep domain knowledge, business acumen, and strong communication skills. While technical understanding is helpful, the role focuses on ensuring data meets business needs and requirements.
Misconception 2: Data Stewards are a Separate Job Title
Reality: Stewardship is a role or relationship to data, and many employees are already stewards because they define, produce, or use data. Stewardship is often an aspect of their existing job rather than a new hire.
Misconception 3: Data Stewardship is Only About Data Quality
Reality: While data quality is important, stewardship encompasses access management, compliance, documentation, stakeholder engagement, and strategic data initiatives.
Misconception 4: One Data Steward Can Handle All Data
Reality: Effective data stewardship requires domain expertise. Organizations typically need multiple stewards specializing in different business areas such as finance, marketing, operations, and compliance.
Misconception 5: Data Stewardship Slows Down Business Processes
Reality: Well-implemented stewardship practices actually accelerate business processes by providing reliable, accessible, and well-documented data that reduces confusion and rework.
Data Stewardship in Modern Data Governance
Evolution of Data Governance
Modern data governance has evolved from traditional, centralized approaches to more collaborative, federated models that recognize the distributed nature of contemporary data landscapes. Data stewardship serves as the operational foundation of these governance frameworks, translating high-level policies into day-to-day practices.
Integration with Modern Technologies
Data stewards are expected to understand the data landscape and navigate them easily.
- Data stewards ensure governance consistency across cloud platforms
- Maintain visibility into distributed data assets
- Ensure training data quality and representativeness
- Manage model data lineage and versioning
- Coordinate responsible AI practices
- Implement quality monitoring for continuous data flows
- Establish governance for event-driven architectures
Data Democratization and Self-Service Analytics
Data stewards play a crucial role in enabling safe self-service analytics by:
- Creating comprehensive data catalogs with business-friendly descriptions
- Implementing graduated access controls based on data sensitivity
- Providing training and data literacy programs for data consumers
- Establishing feedback mechanisms for continuous improvement
Regulatory Compliance and Privacy-First Approach
- Implementation of privacy-by-design principles
- Management of consent and opt-out processes
- Coordination of data subject rights fulfillment
- Regular privacy impact assessments
Data Product Management
With the future of building trusted data products for the organization, modern data stewardship will be essential in:
- Defining data product specifications and service level agreements
- Managing data product lifecycles and versioning through monitoring the data product usage and adoption
- Coordinating with data product managers and engineers
- Establishing customer feedback loops for data consumers
- Educating data consumers about relevant data products
Measuring Data Stewardship Success
Key Performance Indicators (KPIs) Framework
Measuring the effectiveness of data stewardship practices requires a multi-dimensional approach that captures both quantitative metrics and qualitative outcomes. As we are at the very initial phase of the adoption of data stewardship practices we will measure its effectiveness with some preliminary improvements. Overtime, success measurement should align with organizational objectives and demonstrate both operational improvements and strategic value creation.
- Issue Resolution Time: Days to resolve data quality problems (Target: <7 days). Expectation: Downward trend in issue resolution time for issues of similar complexity. Source of Data: GitLab Issues having “Data Quality” labels.
- Data Documentation Coverage: Percentage of key datasets with basic documentation (Target: 80%). Expectation: Increased documentation coverage with up to date information. Source of Data: Atlan metadata coverage by data domain.
- Support Ticket Volume: Number of data-related help requests (Track monthly trends). Expectation: Downward trend, demonstrating improved data literacy and self-serve. Source of Data: Slack queries to #data channel and GitLab issues.
- Data Discovery Efficiency: Time users spend finding required data. Expectation: Downward trend demonstrating improved metadata coverage. Source of Data: Feedback from data consumers.
- Documentation Usage: Frequency of access to data dictionaries and metadata. Expectation: Upward trend indicated by improved Weekly and Monthly active users for Atlan. Source of Data: Atlan usage data.
To review the progress and improvements alongside addressing challenges the data stewards across functional teams will meet on a monthly cadence. This meeting will be organized and facilitated by data governance team.
Recognition and Rewards for Data Stewardship Practitioners
As the data governance team works towards defining and establishing data stewardship practices, roles and responsbilities, we also recognise the need to create some excitement and gamification around this. We plan to :
- Annouce the assigned data stewards across domains to provide orgnizational visibility.
- Recognise the data stewards contributions and spotlight one data steward every month and announce it in monthly data stewardship committee meeting.
- Gamify the stewardship process to encourage contributions from our data consumers and data stewards.
Conclusion
Data stewardship practices are essential for organizations seeking to maximize the value of their data assets while managing associated risks. The role of data stewards continues to evolve with technological advances, but their fundamental mission remains constant: ensuring that data serves the organization’s strategic objectives while maintaining the highest standards of quality, security, and compliance.
94df3e17)
