What is data stewardship? Learn the best practices

Data stewardship is the practice of managing data across its lifecycle so it meets the needs of a business. The discipline demands that data be accurate and of high-quality, while also being secure and private to meet strict governance standards. 

Data stewardship requires data stewards — managers assigned to specific data elements (or departments) and responsible for how it’s used, stored, maintained, and disposed of.

What are the responsibilities of a data steward? 

A data steward is responsible for data processes, but what does that mean? With companies investing more heavily in data than ever before, the role requires a worker to wear many hats and shift seamlessly from technical input to strategic oversight. Data steward responsibilities include:

1. Data quality management

A data steward is responsible for data elements within systems: data warehouses, data lakes, APIs, etc. To maintain data integrity, they must ensure each element they are assigned follows specific rules and is within acceptable, predetermined values and parameters.

A data professional also ensures data elements in the metadata registry are not in conflict. They do this by removing duplicates, preventing overlaps, and verifying new entries are correct. By proactively identifying and resolving issues, data stewards improve the quality of data. 

Key job responsibilities can include

• Using intelligent automation tools to detect and rectify inaccuracies
• Tracking metrics including number of duplicates, percentage of complete records, and missing values
• Addressing anomalies, duplication, and misclassification

2. Data security and privacy

Data stewards double as data catalogers, working to identify and classify sensitive data into four key categories: public, internal, confidential, and restricted. They then conduct data minimization to dispose of unnecessary data, including personally identifiable information (PII), safely. Streamlining data makes it easier to safeguard. After organizing data, stewards encrypt and monitor it as it flows through the pipeline, keeping it safe and preventing unauthorized access. 

Key job responsibilities can include

• Defining role-based permissions and authentication methods
• Employing encryption standards and securing APIs
• Monitoring and ensuring compliance with regulations

3. Data governance

Data stewards also define and implement policies for business data. As data ambassadors, they don’t just deal with technical processes, but they also document terms, definitions, and metadata to create a shared understanding of data management. 

It’s also very common for large organizations to collate a glossary of terms with consistent data language that teams can use to interpret data and collaborate. Stewards also act as mediators between IT and other departments for data governance.

Key job responsibilities can include

• Collaborating with the data governance team to create frameworks for data ownership and data retention
• Ensuring data practices align with business objectives
• Engaging stakeholders to create a data-driven culture

4. Data lifecycle management

With data volumes exploding, stewards play a key role in curating data, so an organization has access to data and what it needs to succeed, but no more. The process is known as lifecycle management. A data steward will ensure the data lifecycle meets company requirements for each of its processes. 

They do this by putting into practice the retention, preservation, and disposal policies set out in data governance programs. Actively managing data in this way feeds back into security, ensuring data is protected from breaches and misuse and compliant with regulations for data lifecycle management. 

Key job responsibilities can include

• Defining data lifecycle policies based on business and regulatory requirements
• Establishing modern archival systems to retain data
• Ensuring data is disposed of correctly

What qualifications should a data steward have? 

A data steward is a data ambassador. They need to combine technical skills with business acumen to oversee the implementation of data governance frameworks.

A job in data stewardship requires specific qualifications and skills demonstrating data literacy. These include:

• A Bachelor’s degree in data science, information management, data engineering, or other related fields
• Relevant certifications include being a Certified Data Steward (CDS) or a Certified Data Management Professional (CDMP)
• 5+ years of experience in adjacent roles such as data management, data governance, or data analysis
• Proficiency in data governance tools and SQL scripting
• Strong understanding of metadata management, data modeling, and data lineage
• Excellent analytical and problem-solving skills

What are the benefits of data stewardship? 

With 80% of enterprise data still untapped and offering a potential goldmine of insights, the value of cataloging and managing data effectively cannot be overstated. The benefits of data stewardship include:

1. Improved data quality

Data stewards oversee data across its lifecycle, ensuring accuracy and integrity before decision-makers use it. By establishing quality standards and enforcing protocols to maintain them, data is kept clean, consistent, and reliable. This breeds trust as insights obtained from data can be applied to analysis, forecasting, reporting, and strategic planning.  

2. Enhanced data security

The average cost of data breaches rose 10% to $4.88 million in 2024. With threats to data growing, the work that stewards do to enhance security and protect sensitive information is invaluable. Effective data stewardship involves establishing role-based access controls (RBAC) to combat insider risks and working with cybersecurity teams to encrypt data and monitor network traffic. 

3. Increased data accessibility

Data stewards implement governance programs while balancing the need to make data accessible to those who need it the most. Creating a centralized data catalog is key here, empowering employees to discover and use data quickly without duplicating work or creating silos. The use of self-service data models, which stewards oversee, also increases data access without technical know-how or direct IT involvement.

4. Ensured regulatory compliance

Data protection is fundamental to compliance with GDPR, GLBA, and APA regulations. Data stewardship ensures data aligns with these standards by emphasizing audit trails and proper retention and disposal practices. With this oversight, it’s easier to secure data and keep it traceable at all times.

5. Competitive advantage

Good things flow from “healthy” data. Stewardship practices empower leaders to maximize value from data by seizing new opportunities and accelerating innovation. Instead of being reactive, businesses can proactively leverage data to respond to market changes and adapt to customer preferences, gaining an advantage over peers.

What are some challenges faced by companies engaging in data stewardship?

Even the best-laid plans can run into problems. Common challenges you might have to address when implementing data stewardship include:

1. Lack of clear policies and roles

Data stewards can be undermined by a lack of clarity about their role. When responsibilities and oversight aren’t clear, this causes confusion, with data managers more likely to make errors and mistakes. They can also be undermined by management viewing stewardship as a niche responsibility that is transferred to a smaller IT team rather than viewed as a broader strategic business function.

2. Resistance to change

Cultural resistance can also make data stewardship difficult. Managers and employees may balk at what they perceive to be disruptive or unnecessary data workloads. This is often due to a lack of awareness and understanding. Employees might also be accustomed to outdated processes and legacy systems, viewing modern stewardship as a threat to established routines.

3. Technical limitations

These legacy systems and older tools are also a drag on data stewardship. Maintaining data quality and security is more difficult when wrestling with silos and inefficiencies inherent to data systems no longer fit for purpose. A lack of integration and a dearth of viable tools can cause struggles when cleaning and validating data at scale. 

What are some simple yet effective data stewardship best practices to follow? 

Are you ready to overhaul your data management processes? These five data stewardship best practices will help:

1. Establish clear roles and responsibilities

As data stewards act as a bridge between different departments when applying data governance frameworks, the onus is on senior leaders, Data Governance Managers, CDOs, CIOs, etc., to clearly define their job responsibilities. Every data element must also have a data owner. Clarifying ownership of data domains enhances quality and accountability.

2. Build strong relationships with stakeholders

Stakeholders must engage with data stewardship. This can be achieved by regularly communicating data projects, policies, and standards and making them aware of new changes. Also, leverage tools in dashboards to send reports and provide newsletters about updates. 

3. Use data quality tools and technologies

Use data quality tools to verify data against predefined rules and standards to ensure it’s accurate and complete. Consider updating your data architecture by migrating to the cloud to validate, profile, and clean data.   

4. Monitor and improve data stewardship practices

Data stewards need executive support. The value of their work must be appreciated and actively promoted. This starts with clear and complete documentation of practices, which can be iterated over time. Data quality metrics must be monitored, tracked, and linked to KPIs to assess performance and identify areas for improvement.

Key tools and technologies 

Effective data stewardship relies on a suite of tools and technologies capable of keeping data accurate, accessible, and compliant across the entire lifecycle.

Data quality tools

Data quality tools validate, clean, and maintain the accuracy of data so it can be used to make strategic, informed decisions, like:

• Automating error detection and correction for invalid, incomplete, and inconsistent data
• Using real-time alerts to flag issues and analytics dashboards to monitor systems

Data governance platforms 

Data governance platforms establish and organize data standards and policies to enhance data handling practices, such as:

• Set Role-Based Access Control (RBAC) so only authorized individuals can use data
• Configure governance rules and link them to internal and external regulations
• Create audit trails by logging user interactions

Data cataloging tools

Data cataloging tools create data inventories to help users find and use the data they need

• Locate relevant datasets quickly by filtering based on fields, tags, and descriptions
• Link cataloged data to metadata for effective metadata management
• Create data glossaries with terms and data definitions to facilitate shared understanding between technical and business users

UCL data stewardship case study

Many companies successfully deploy strategies to control and use their data assets effectively, even when these assets are funneled away in outdated repositories. Now, let’s look at data stewardship in action

University College London (UCL) demonstrated how data stewardship programs bring new life to old data. The university’s Medical Research Council Unit for Lifelong Health and Ageing (MRC LHA) hired data stewards to enhance the data analysis pipelines for research findings first logged 75 years ago. The aim was to end reliance on legacy devices and modernize pipelines to meet open standards.

After producing scripts to extract the data successfully, the data stewards produced a codebase that met the exact requirements outlined by UCL. It was later exported to open-source software and made accessible in a public repository within the university’s developer platform, GitHub.

Legacy system integration can be a burden as legacy formats (XLSX, XML, etc.) must be converted into open formats like CSV. Standardizing data formats is an issue, too, especially with large data sets. 

The case study emphasizes the importance of ongoing collaboration and adaptability when addressing data quality issues.

Why is data stewardship constantly changing?

Answering what data stewardship is wouldn’t be complete without a nod to the future. What developments can we expect to see in this space during the next 5 to 10 years?

AI and machine learning

Data stewardship roles are vast and complex. AI and machine learning promise to streamline critical tasks by automating repetitive (and less valuable) tasks, such as data profiling, data cleaning, and data cataloging. This frees up specialized workers so they can focus on aligning data strategy with business objectives and other higher-level strategic responsibilities.

Data privacy and ethical considerations

Ethical and responsible data usage will soon be weighted more heavily when handling data, with data stewardship emphasizing respect for the user’s individual rights. In the future, we will see leaders leaning on newly established ‘data ethics committees’ to mandate privacy-first governance frameworks designed to build consumer trust. AI algorithms should ‘ethically’ respect ethical principles, but that’s something that’s definitely up for debate. 

Cloud-based data stewardship

Cloud-native data stewardship is coming - over 60% of corporate data is already in the cloud. Unified governance strategies tailored for data environments across multiple systems will ensure consistent data policies and compliance. Moving forward, data stewardship must also account for the Internet of Things (IoT) and edge computing. Data sets are growing: large-scale systems need to be seamlessly integrated to manage data effectively. 

Wrapping up

Data stewardship validates, cleans, and enriches data so it’s accurate, secure, and satisfies company requirements. Data stewards underscore these efforts and act as a bridge between technical teams and business objectives. 

Turning raw information into actionable insights has never been more important, especially as organizations grapple with large data sets while targeting increased revenue and cost savings. Cloud data storage can facilitate these objectives. 

At RecordPoint, we offer a cloud-native solution that is complete with data-driven solutions. All of our data-driven solutions are designed to help you manage data quality and extract more value from it. We can help you with data discovery, data cataloging, data minimization, data governance, and more.

Are you ready to transform your data stewardship practices? Contact us today to start your migration journey. 

FAQs 

What is the difference between data stewardship and data governance?

Data stewardship is the act of doing and taking action to coordinate and implement policies and procedures set out in data governance frameworks. Both are aligned toward the same goals. 

What is a data owner?

A data owner is responsible for managing an organization’s data. A data owner can be an individual or a group.

What is a data custodian?

A data custodian is responsible for the security of data: keeping it safe, accessible, and compliant. This contrasts with a data owner focused on data quality and accuracy. 

What is a data analyst?

Data analysts interpret data to derive value from it by providing actionable insights.