Introduction
In today’s data-driven world, the importance of effective data governance and the benefits of data stewardship cannot be overstated. Emerging as a formal discipline in the late 1990s and early 2000s, data governance encompasses the framework and processes organizations need to implement to manage their data assets effectively. This involves ensuring data quality, security, availability, and usability throughout an enterprise’s technological infrastructure. Central to this framework is the concept of data stewardship, which, while related to data governance, focuses on the practical aspects of managing data. Data stewards play a crucial role in ensuring that organizational data is accessible, trustworthy, and secure. Their responsibilities range from data quality management to facilitating collaboration among stakeholders, making them indispensable to any effective data governance strategy.
Although official data governance roles have been around for several decades now, the introduction of artificial intelligence (AI), machine learning, and deep learning into data management, the role of the corporate data steward has evolved significantly in the past few years. Data stewards are responsible for carrying out data usage and security policies. They act as a liaison between the IT department and the business side of an organization, overseeing specific activities such as data collection, data cataloging, and data inventorying.
What is Data Governance? Emerging as a formal discipline in the late 1990s and early 2000s, data governance refers to the comprehensive framework and processes that organizations utilize to effectively manage their data assets. This includes ensuring data quality, security, availability, and usability across key technology components of an enterprise.
As I state in my article, Data Governance in Business Intelligence and Analytics data stewardship responsibilities has correspondingly increased. According to Gartner , “Every year, poor data quality costs organizations an average $12.9 million.” That’s not small change, even for large corporations, so anything that helps improve data quality will be looked at by corporations, including data governance.
What is Data Stewardship? While data stewardship is linked to other enterprise data governance initiatives, the terms are not interchangeable. Data stewardship is a practice that ensures an organization’s data is accessible, trustworthy, usable, and secure. When companies decide to implement a data governance framework, they should also add a data stewardship element. This program works in conjunction with an organization’s data governance policy. While data stewardship helps promote effective data management within an organization while emphasizing tactical coordination, data governance programs focus on high-level policies.
The Oxford Learners Dictionary The Essential Role of a Data Steward in Modern Business Intelligence , Donal Tobin contends that data stewardship lies at the “intersection of data management and business strategy.” “Tasked with safeguarding data integrity and enabling informed business intelligence, data stewards are fundamental to modern organizations. They ensure data is clean, compliant, and utilized effectively,” sa y s Tobin .
Data Steward Responsibilities A data steward’s responsibilities and obligations include:
Data Quality Management — Monitoring and maintaining the accuracy, completeness, and consistency of data across systems. Data stewards identify and resolve data quality issues, implementing processes to proactively prevent any potential future data issues.Metadata Management — Overseeing the creation, maintenance, and accuracy of metadata, which provides context and meaning to data elements.Defining and Enforcing Data Standards — Collaborating with various teams to establish clear rules for data creation, storage, and usage.Data Governance Compliance — Implementing and monitoring all policies related to data governance, ensuring compliance with relevant regulations as well as acting as a champion for data governance practices within an organization.Facilitating Data Access and Data Sharing — Establishing processes for secure access to data while protecting sensitive information. Data stewards also ensure that data is available to authorized users who require it for decision-making.Collaboration with Stakeholders — Serving as a liaison between technical teams (IT) and business units, fostering communication about any data-related issues. Data stewards try to facilitate inter-departmental collaboration and educate stakeholders on data governance policies and best practices to promote a strong data culture.Monitoring Data Usage — Tracking how data is used within an organization, always trying to identify opportunities for improvement or potential risks. Data stewards also ensure data usage aligns with organizational goals as well as regulatory and compliance requirements.
In general, a data steward is the go-to person for queries related to data. They manage data from creation to deletion, ensuring the accuracy, consistency, and reliability of the data during its full life cycle. They bring domain-specific knowledge and provide guidance on best practices related to particular datasets or business areas. Acting as a liaison between IT and business users, they monitor and enforce data governance policies. They alse ensure data quality, security, and compliance.
Data Owners In comparison to data analysts and stewards, data owners are accountable for overseeing specific datasets or domains within an organization. They are typically senior managers responsible for the classification, protection, use, and quality of data, highly aware of all data governance outcomes. Data owners and data stewards work collaboratively, but their responsibilities differ. Their duties encompass various aspects of data oversight, ensuring that the organizatoin’s data is secure, compliant, and effectively utilized. Their key responsibilities include:
Data Security Management — Implement security measures to protect data from unauthorized access and breaches. They also establish protocols such as encryption, firewalls, and access controls to maintain data confidentiality.Access Control Management — Enforce policies that govern data access to ensure only authorized personnel can view or manipulate sensitive information.Data Lifecycle Management — Define policies for data retention, usage, and disposal based on business needs and compliance requirements.Data Decision-Making — Make authoritative decisions regarding how data is used, shared, and repurposed within their domain while guiding the application of data for various purposes, such as reporting and analytics.Compliance Oversight — Ensure data practices adhere to relevant regulations (e.g., GDPR, HIPAA) and organizational policies. They also collaborate with legal and compliance teams to define specific compliance measures for the company’s datasets.Risk Management — Assess risks associated with data handling and implement strategies to mitigate these risks.Collaboration with Data Stewards — Work closely with data stewards to provide guidance on data management practices that ensure the effective implementation of governance policies.Data Classification and Prioritization — Classify datasets based on their sensitivity, importance, and importance to the organization.Monitor Data Quality — Establish systems for monitoring the quality of data within their domain.Strategic Input — Provide insights into the strategic direction for data management initiatives within the organization.
Data Custodians & Administrators Data custodians are responsible for implementing and maintaining security data controls. They manage and maintain data assets, ensure data is accurate, complete, and secure as well as provide needed data access to authorized users. Data custodians typically hold technical roles focused on the infrastructure needed for data storage. They monitor and report on data access and usage, constantly identifying and addressing data quality issues.
As I state in my article, Data Governance Roles and Responsibilities: Key Titles and Organizational Structure
The roles of data owners, data stewards , data custodians, and data administrators may overlap depending on an organization’s size. It is important to understand these positions work in concert and do not exist in a silo.
Data Stewardship Challenges Organizations often operate without good data stewardship practices, but this can be dangerous. Poor data governance can effectively turn data from a valuable asset into an expensive liability. Without proper data governance, data becomes inaccessible, models inoperable. Many organizations encounter barriers to finding or accessing appropriate data when it is needed. Effective data stewardship requires a good governance framework, so data is always accessible and useful.
Data Stewards ensure data is a valuable asset to an organization, overseeing the creation, use, and even destruction of data. A lack of clear direction and agreement can make data stewardship challenging. Resistance to changing from old processes to new ones can hinder effective data stewardship. Organizations may struggle with transforming data into a valuable resource due to management issues.
A lack of data documentation, and standardization can lead to organizations not knowing what data they possess. Siloed data sets can occur even with established data steward roles. Data stewardship initiatives may face challenges in implementing effective communication across departments.
Data stewards play a crucial role managing and overseeing an organization’s data assets, ensuring their quality, security, and compliance. They are responsible for maintaining high data quality, which is essential for analytics and decision-making. However, they face several significant challenges that can hinder their effectiveness. Without adequate tools and authority, they can struggle to identify and rectify errors or inconsistencies in the data. A problem exacerbated by the sheer volume of data flowing through today’s corporate systems. Data often resides in disparate systems, creating silos that hinder effective data management. Data stewards must work to integrate these sources to provide a unified view of the data. This can be complicated by organizational politics and the inherent resistance to change found in many workers.
Ownership and Accountability With constantly evolving regulations such as GDPR and CCPA, data stewards must stay informed about legal requirements and ensure that all data handling practices comply with these laws. This task can be daunting as non-compliance can lead to severe financial penalties and reputational damage.
The rapid pace of today’s technological advancements necessitates that data scientists and data stewards continuously adapt to new tools and platforms for data management. Keeping up with these changes can be overwhelming, especially when they require new skills or processes.
Determining clear ownership of specific data sets can be difficult in large organizations, leading to confusion over accountability. This lack of clarity can complicate decision-making processes regarding data management.
Data Privacy and Security Ensuring the privacy and security of sensitive information is also a critical responsibility for data stewards. They must implement measures to protect business data against unauthorized access. Complying with privacy regulations is also important and this adds another layer of complexity to their role.
The challenges faced by data stewards are multifaceted, involving technical, organizational, and cultural dimensions. Addressing these challenges requires a comprehensive approach that includes adequate training, resource allocation, clear governance structures, and the fostering of a supportive organizational culture around their data quality programs and stewardship. By proactively tackling these issues, organizations can empower their data stewards to effectively enhance data quality and data governance.
Case Study: Freddie Mac In its article, Freddie Mac Boosts Home Lending Efficiency as Demand for Mortgages Grows , Informatica, the enterprise cloud data management solutions company, explains the work it has done with Freddie Mac, a U.S.-government-sponsored enterprise (GSE) that buys mortgages on the secondary mortgage market, then packages and sells them as mortgage-backed securities to investors on the open market. Freddie Mac has developed a comprehensive Data Stewardship Model aimed at enhancing data management and governance within its organization, states Informatica . This model is characterized by several key components and strategic initiatives that ensure effective oversight of data assets as well as compliance with regulations that align with business objectives. states, “By taking a platform approach – deploying automated quality controls and cloud-ready connectors – Freddie Mac protects data wherever it moves, mitigating exposure risks and meeting unexpected spikes in mortgage data processing with the same number of resources,” says Informatica.
Freddie Mac partnered with the Enterprise Data Management Council (EDM Council) to utilize their Data Management Capability Assessment Model (DCAM). This collaboration helped create a structured roadmap for developing robust data governance practices based on stakeholder input. The model empowers users by providing them with quality data when needed. It also enables self-service capabilities that can give users the ability to proactively address data issues. This includes creating data catalogs as sources of truth and offering robust tools for data access and analysis.
Data Standards and Privacy Controls Establishing clear data standards is critical in Freddie Mac’s data stewardship model. Data owners are required to provide metadata, while users must communicate their data usage needs. This collaborative effort ensures that everyone involved in data management understands their roles and responsibilities. It also fosters a culture of accountability.
Freddie Mac employed automated data quality tools used to monitor data quality and manage personally identifiable information (PII). Like any institution handling PII, “Freddie Mac’s data policies stipulate that the GSE know where PII is stored, who has access to it, and how to protect it. These data practices are critical not only to meet the requirements set forth by its regulator, the U.S. Federal Housing Finance Agency, but also to minimize exposure risk as the organization moves data to the cloud,” states Informatica. By utilizing platforms like Informatica for data quality management, Freddie Mac cleanses large datasets and ensures compliance with privacy regulations.
Data tables Our stakeholders needed to trust their data was being protected, said Aravind “Jag” Jagannathan, Vice President and Chief Data Officer at Freddie Mac Single-Family. “Although the manual process worked and successfully prevented adverse incidents, we needed to find ways to operate more efficiently, automate the discovery of PII wherever it’s located, and free our teams to be more innovative and take on new business challenges,” added Jagannathan.
A Data Cultural Shift A significant aspect of Freddie Mac’s model initiative involves shifting the organizational culture to prioritize data stewardship that is important to data governance. The leadership at Freddie Mac emphasized that effective data stewardship was not just a technological issue but also a matter of people, culture, and communication. Continuous training programs equipped data stewards with the necessary skills to manage data effectively. Support from top management fostered an environment conducive to successful data stewardship initiatives.
Freddie Mac’s Data Stewardship Model exemplifies a strategic approach to managing data assets that integrates business needs with effective governance practices. By focusing on collaboration, empowerment, standardization, and cultural change, Freddie Mac enhances its ability to leverage data for decision-making while ensuring compliance with evolving regulations. Freddie Mac’s model serves as a framework for other organizations looking to establish or improve their own data stewardship practices. “By empowering its teams to be more efficient at detecting and protecting PII, Freddie Mac is keeping personal data safe and remaining compliant, continuing to build trust in an already highly esteemed brand,” Informatica concludes.
Emerging Technology Emerging technologies, such as AI, will increase data stewardship complexity and requirements. Organizations need to adapt to an increasing number of legal requirements related to data and AI. Machine learning and deep learning are enabling data stewards to automate routine tasks that were previously manual and time-consuming. For instance, AI can assist in metadata extraction, automated classification, and data quality control. This shift allows data stewards to focus on more strategic activities rather than day-to-day operational tasks. By automating processes such as error detection, duplicate record removal, and compliance checks, AI empowers data stewards to significantly enhance their productivity and efficiency.
Conclusion Effective data stewardship is crucial for organizations aiming to transform their data from a potential liability into a valuable asset. It’s important for companies to foster a culture that values data stewardship across the organization. However, this is challenging. Resistance from employees who may not understand the importance of data management can impede efforts to establish robust data stewardship programs and practices. Data education is key. High-quality data is imperative for today’s corporate AI, machine learning, and deep learning initiatives.
By addressing the multifaceted challenges of data management—such as ensuring data quality, compliance with regulations, and fostering interdepartmental collaboration—data stewardship can play an essential role in enhancing data governance frameworks. As demonstrated by Freddie Mac’s comprehensive data stewardship model, organizations that prioritize clear ownership, accountability, and the integration of advanced technologies can significantly improve their data practices. Ultimately, investing in robust data stewardship not only mitigates risks but also empowers organizations to leverage their data effectively for informed decision-making and strategic growth. The average cost of hiring a data steward is $95,685 , a small price to pay for what he or she brings to the data governance roundtable.
