Chief data officer
Data governance manager
Data steward
IT operations director
Business intelligence lead
Compliance officer
This process is used on an ongoing basis to manage the operational stewardship of the organization’s data assets. It applies when data stewards must monitor data quality within their domains, investigate and resolve data issues, enforce data standards and policies, coordinate with source system owners on remediation, and report on stewardship activities to the data governance council. It is common when stewardship spans multiple business units, systems, and data types. Ideal for enterprises managing complex data environments across financial services, healthcare, retail, manufacturing, and technology.
The data stewardship process typically involves data stewards who are accountable for quality and compliance within their domains, data governance managers who oversee the stewardship program, IT and data engineering teams who support technical remediation, business analysts who identify and escalate data issues, and the data governance council that reviews stewardship performance.
Accountable domain ownership with each data domain having a designated steward responsible for quality and standards compliance. Faster data issue resolution because issues are routed to the responsible steward with context and tracked against resolution SLAs. Consistent data standards enforced across the enterprise through active steward engagement rather than passive policy documentation. Governance visibility through regular stewardship reporting on data quality metrics, issue volumes, and resolution rates. Reduced downstream data problems by resolving quality issues at the source rather than compensating for them in reporting and analytics.
Your version of this process may vary based on roles, systems, data, and approval paths. Moxo’s flow builder can be configured with AI agents, conditional branching, dynamic data references, and sophisticated logic to match how your organization runs this workflow. The steps below illustrate one example.
Stewardship assignment and domain definition
The process includes assigning data stewards to each major data domain and defining their scope, responsibilities, and authority. Each steward’s domain is documented, including the systems, data entities, and quality standards they are accountable for. An AI Agent can assist by mapping data domains to organizational systems and identifying domains that lack active steward assignments.
Data quality monitoring
Stewards monitor data quality within their domains through automated profiling, quality rules, and periodic review. When quality issues are detected — such as duplicates, inconsistencies, completeness gaps, or standard violations — they are logged and assigned for investigation.
Issue investigation and remediation coordination
The steward investigates each issue, identifies the root cause, and determines the appropriate remediation. If the fix requires changes to a source system or process, the steward coordinates with IT or the system owner. An AI Agent may track issue resolution progress and flag items approaching SLA deadlines.
Standards enforcement and exception management
Stewards enforce data standards within their domains, reviewing proposed data changes, new data sources, and integration requests for standards compliance. When exceptions are requested, the steward evaluates the justification and either approves the exception with documentation or requires the standard to be met.
Stewardship reporting
Stewards report regularly to the data governance council on data quality metrics, issue volumes and resolution rates, standards compliance, and any emerging risks within their domains. Reporting informs governance priorities and resource allocation.
Program review and steward performance
The data governance manager periodically reviews the stewardship program’s effectiveness, including steward engagement, issue resolution performance, and domain coverage. Gaps are addressed through additional training, reassignment, or expanded stewardship resources.
This process commonly relies on inputs such as data quality profiling results, stewardship assignments, data standards documentation, issue logs, and governance reporting requirements. It may be triggered by automated quality alerts, business unit escalations, or scheduled stewardship review cycles. Connected systems often include data governance platforms like Collibra or Alation, data quality tools like Informatica or Talend, master data management systems, and reporting dashboards.
Key decision points include which data domains require active stewardship and how steward assignments align with organizational structure, whether data quality issues require source system remediation or can be resolved through data management processes, whether exceptions to data standards are justified and appropriately documented, and how stewardship performance informs governance program investment.
Stewards assigned but not actively engaged, reducing stewardship to a nominal role without operational impact. Data quality issues identified but not remediated because the steward lacks authority or resources to coordinate with source system owners. Standards enforcement inconsistent because stewards apply different interpretations across domains. Stewardship reporting not connected to governance decisions, making the program invisible to leadership. Steward turnover not managed, leaving domains without active stewardship for extended periods.
Orchestrates the ongoing data stewardship program across data stewards, IT, governance leadership, and business units in a coordinated workflow.
AI Agents monitor steward assignments and domain coverage, flagging domains without active stewards and tracking issue resolution against SLAs.
Routes data quality issues to the responsible steward with context, severity, and resolution deadlines within the workflow.
Manages standards enforcement and exception requests within the workflow so steward decisions are documented and traceable.
Connects to data governance and quality platforms like Collibra, Alation, and Informatica so stewardship activities and quality metrics are synchronized.
Preserves the complete stewardship record including assignments, issue resolution, standards decisions, exception approvals, and reporting for governance review and audit.
