Data owners, data stewards, and data custodians are three common roles that work closely to ensure data platforms operate effectively. Each role has distinct responsibilities and a different level of accountability. These three roles are often found working together on a single data platform, especially when data management tasks are complex and require clear divisions of responsibility based on accountability and seniority.
The data owner is accountable for the overall success of the data solution. This includes providing strategic direction and guidance to drive the development and operation of the platform. The data steward is responsible for maintaining the quality of core data assets. Data custodians handle the day-to-day maintenance activities, such as managing access, reviewing incidents, and monitoring the platform’s performance.
Data Ownership Definition
Effective data management is not just about bits and bytes; it’s about making the right decisions to ensure your data remains a valuable asset. This is where the concept of data ownership becomes critical. Having a clear decision-maker responsible for the future of a data platform is essential for its success. This individual needs the authority to make strategic choices that guide the platform’s evolution and address challenges that inevitably arise.
Data ownership goes beyond simply managing the infrastructure. It’s about owning the data itself: its quality, its accessibility, and its alignment with business needs. Think of it as treating your data like a valuable product, not just something that sits on a server.
Many organizations that shift towards treating their data as a product often separate infrastructure ownership from data ownership. This allows data owners to focus specifically on:
- Data health: Ensuring data quality, accuracy, and consistency.
- Maintenance: Keeping data up-to-date, relevant, and secure.
- Integration: Connecting data platforms to facilitate data sharing and avoid silos.
Crucially, data ownership also involves strong communication. Data owners must be advocates for their data, promoting its value and demonstrating how it can drive positive outcomes for the organization. They need to communicate effectively with stakeholders across different departments, explaining how the data can be used to support decision-making and achieve business goals.
Effective data management is not just about bits and bytes; it’s about making the right decisions to ensure your data remains a valuable asset. This is where the concept of data ownership becomes critical. Having a clear decision-maker responsible for the future of a data platform is essential for its success. This individual needs the authority to make strategic choices that guide the platform’s evolution and address challenges that inevitably arise.
Data ownership goes beyond simply managing the infrastructure. It’s about owning the data itself: its quality, its accessibility, and its alignment with business needs. Think of it as treating your data like a valuable product, not just something that sits on a server.
Many organizations that shift towards treating their data as a product often separate infrastructure ownership from data ownership. This allows data owners to focus specifically on:
- Data health: Ensuring data quality, accuracy, and consistency.
- Maintenance: Keeping data up-to-date, relevant, and secure.
- Integration: Connecting data platforms to facilitate data sharing and avoid silos.
Crucially, data ownership also involves strong communication. Data owners must be advocates for their data, promoting its value and demonstrating how it can drive positive outcomes for the organization. They need to communicate effectively with stakeholders across different departments, explaining how the data can be used to support decision-making and achieve business goals. By establishing clear data ownership, organizations can ensure their data is treated as a strategic asset, managed effectively, and used to its full potential.
Data Stewardship Definition
While Data Owners provide the strategic direction, Data Stewardship is the guiding force behind ensuring data is trustworthy, usable, and aligned with business needs. It’s about treating data as a valuable resource that needs careful management throughout its lifecycle. Data Stewardship focuses on maximizing the value of data by ensuring it meets quality standards, complies with regulations, and serves its intended purpose.
Think of Data Stewards as guardians of data quality. They are responsible for establishing and enforcing data standards, identifying and resolving data quality issues, and promoting data literacy across the organization. They act as a bridge between technical teams and business users, ensuring data is understood and used effectively.
Key aspects of Data Stewardship include:
- Data Quality: Establishing and enforcing data quality rules, monitoring data for errors and inconsistencies, and implementing data quality improvement initiatives.
- Compliance: Ensuring data adheres to relevant regulations and industry standards, such as GDPR, HIPAA, or industry-specific guidelines.
- Metadata Management: Defining and documenting data definitions, data lineage, and other metadata to enhance data understanding and usability.
- Data Governance: Contributing to the development and implementation of data governance policies and procedures to ensure data is managed consistently across the organization.
Data Stewardship is essential for building trust in data. By ensuring data is accurate, reliable, and compliant, Data Stewards enable organizations to make informed decisions, mitigate risks, and achieve their objectives. They play a vital role in fostering a data-driven culture where data is treated as a valuable asset.
Data Maintenance Activities
As data platforms grow in size and complexity, serving numerous users and departments, the need for ongoing maintenance becomes critical. Even seemingly small tasks, like granting access to new users or providing introductory training, can consume significant time and resources.
Consider a scenario where an organization must comply with ISO data security standards. This might require ensuring all personnel receive proper training before accessing any data platform. These types of recurring tasks, while essential, can divert valuable resources from other strategic initiatives.
While advancements in AI and automation can streamline some aspects of data maintenance, many tasks still require human intervention. Think of activities like:
- Access Management: Granting and revoking user access, managing permissions, and ensuring data security.
- Incident Response: Investigating and resolving data quality issues, addressing access violations, and troubleshooting system errors.
- Performance Monitoring: Tracking key metrics, identifying performance bottlenecks, and optimizing system efficiency.
- Regular Reviews: Even with automated processes, periodic reviews are necessary to ensure accuracy, compliance, and identify areas for improvement.
These ongoing activities demand dedicated personnel who can act as the first line of support for the data platform. They need to be responsive to user requests, proactive in identifying potential issues, and diligent in performing routine maintenance tasks. Without this dedicated focus on data maintenance, even the most valuable data platforms can become inefficient, insecure, and ultimately fail to deliver on their potential.
Data Owner Responsibilities
As discussed earlier, data ownership plays a critical role in the success of any data platform, distinct from the hands-on work of data stewardship. Data owners are typically individuals positioned high within the organizational chart, holding roles that require decision-making authority and budget control. This empowers them to allocate resources effectively and address urgent or strategic needs.
The primary responsibility of a data owner is to steer the data platform towards achieving business goals. This involves making strategic decisions, resolving high-impact issues, and ensuring the platform remains aligned with the organization’s overall data strategy.
Here are some key activities a data owner undertakes:
- Defining Data Strategy: Establishing a clear vision for the data platform, outlining its objectives, and aligning it with the organization’s broader goals.
- Approving Data Policies: Setting policies for data access, security, quality, and retention to ensure data is managed responsibly and effectively.
- Prioritizing Investments: Allocating budget and resources to support data initiatives, ensuring the platform receives necessary funding for maintenance, upgrades, and new features.
- Risk Management: Identifying and assessing data-related risks, such as security breaches or data loss, and implementing mitigation strategies.
- Resolving Conflicts: Addressing disagreements or conflicts related to data ownership, access, or usage, ensuring fair and consistent data management practices.
- Monitoring Performance: Tracking key performance indicators (KPIs) to assess the effectiveness of the data platform and identify areas for improvement.
- Communicating Value: Promoting the value of the data platform to stakeholders, demonstrating its impact on business outcomes, and fostering a data-driven culture.
Data Steward Responsibilities
In any organization, certain datasets rise above the rest in terms of their importance and impact. These “critical data elements” require special care and attention to ensure their quality, accuracy, and reliability. This is where Data Stewards step in. They possess a unique blend of business and technical knowledge that allows them to act as guardians of these crucial data assets.
To be effective, a Data Steward needs a deep understanding of the business processes that generate and utilize the data. This includes:
- Business Process Knowledge: Knowing how users interact with business applications and how data is collected, whether through manual entry, automation, or ingestion from external sources.
- Data Domain Expertise: Possessing a strong understanding of the specific data domain they oversee, such as customer data, financial data, or product data.
- User Perspective: Understanding how different users and departments utilize the data, enabling them to anticipate data needs and identify potential issues.
On the technical side, Data Stewards need sufficient knowledge to:
- Understand Data Models: Interpreting data models to understand how data is structured and related, enabling them to identify potential inconsistencies or errors.
- Query and Update Data: Using basic data querying and manipulation techniques to investigate data quality issues, perform data profiling, and identify areas for improvement.
With this combined expertise, Data Stewards take on a range of responsibilities, including:
- Data Quality Management: Monitoring data quality, identifying data quality issues, and recommending improvements. This might involve implementing data quality rules, performing data cleansing, or establishing data quality metrics.
- Metadata Management: Ensuring data is properly defined and documented, including creating and maintaining data dictionaries, business glossaries, and data lineage information.
- Master Data Management: Taking a leading role in managing and curating critical data elements, such as customer lists, product catalogs, or employee records. This might involve establishing data governance rules, resolving data inconsistencies, and ensuring data accuracy across different systems.
- Compliance Monitoring: Ensuring data adheres to relevant regulations and industry standards, such as GDPR, HIPAA, or internal data governance policies.
- Collaboration: Working closely with Data Owners, Data Custodians, and other stakeholders to ensure data is managed effectively across the organization.
- Training and Support: Providing guidance and training to data users on data quality best practices, data governance policies, and data access procedures.
Data Custodian Responsibilities
While Data Owners define the strategy and Data Stewards ensure data quality, the day-to-day operation of a data platform relies heavily on Data Custodians. They are the technical guardians of the data, responsible for ensuring its availability, security, and performance.
Many essential data management tasks cannot be fully automated. They require personal contact, careful review, and a deep understanding of the technical environment. For example, resolving a complex access issue might involve interacting with the user, understanding their needs, and configuring appropriate permissions. Similarly, investigating a performance problem often requires analyzing system logs, identifying bottlenecks, and optimizing configurations.
Data Custodians possess the technical expertise to handle these tasks effectively. They have a deeper understanding of the data platform’s infrastructure, security mechanisms, and operational processes compared to Data Stewards or Data Owners. This allows them to:
- Manage Access Control: Granting and revoking user access, configuring security roles, and ensuring compliance with data access policies.
- Monitor Performance: Tracking system performance, identifying and resolving bottlenecks, and ensuring optimal data availability and responsiveness.
- Maintain Systems: Performing routine maintenance tasks, such as applying software updates, managing backups, and ensuring data integrity.
- Respond to Incidents: Investigating and resolving data-related incidents, such as data quality issues, security breaches, or system outages.
- Troubleshoot Problems: Providing technical support to users, diagnosing and resolving data-related problems, and escalating complex issues to appropriate teams.
- Manage Data Lifecycle: Archiving or decommissioning outdated datasets, ensuring data retention policies are followed, and optimizing storage resources.
Data Quality Responsibilities
Maintaining and improving data quality is crucial for ensuring a data platform remains trustworthy, reliable, and compliant with regulations. As a data platform evolves and new records are added, data quality can be impacted by various factors: changes to application code, data transformation processes, or even shifts in business processes that affect how data is collected.
Therefore, data quality requires a collaborative effort from all three roles:
- Data Owners: They provide the high-level guidance by:
- Identifying critical datasets that require continuous data quality monitoring.
- Setting overall data quality expectations and defining acceptable health thresholds.
- Establishing the process for handling and resolving data quality issues.
- Data Stewards: They take a more hands-on approach by:
- Defining specific data quality rules and thresholds.
- Monitoring data quality metrics and identifying areas of concern.
- Investigating the root cause of data quality issues and recommending solutions.
- Collaborating with data custodians to implement data quality improvements.
- Data Custodians: They provide the technical support by:
- Implementing data quality rules within the data platform.
- Configuring data quality monitoring tools and processes.
- Resolving data quality issues identified by data stewards.
- Maintaining data quality documentation and reporting on data quality metrics.
Data quality best practices - a step-by-step guide to improve data quality
- Learn the best practices in starting and scaling data quality
- Learn how to find and manage data quality issues
What is the DQOps Data Quality Operations Center
DQOps is a data observability platform designed to monitor data and assess the data quality trust score with data quality KPIs. DQOps provides extensive support for configuring data quality checks, applying configuration by data quality policies, detecting anomalies, and managing the data quality incident workflow.
DQOps is a platform that combines the functionality of a data quality platform to perform the data quality assessment of data assets. It is also a complete data observability platform that can monitor data and measure data quality metrics at table level to measure its health scores with data quality KPIs.
You can set up DQOps locally or in your on-premises environment to learn how DQOps can monitor data sources and ensure data quality within a data platform. Follow the DQOps documentation, go through the DQOps getting started guide to learn how to set up DQOps locally, and try it.
You may also be interested in our free eBook, “A step-by-step guide to improve data quality.” The eBook documents our proven process for managing data quality issues and ensuring a high level of data quality over time. This is a great resource to learn about data quality.
