In the modern digital economy, data is a critical asset that, when properly managed, can drive innovation, insights, and competitive advantage. However, for organizations to leverage data effectively, they must maintain trust in its quality, reliability, and governance/management. Central to this effort is the concept of data stewardship.

While it may appear to be a relatively recent phenomenon, the principles underlying data stewardship have deep roots from the early days of organizational information management. Exploring the history of data stewardship, its relationship with broader fields like data governance and data quality, and the persistent challenges organizations face when implementing data stewardship programs can help professionals and organizations implement and sustain effective data stewardship programs.

Early Foundations: The Emergence of Data Management

The seeds of data stewardship were sown in the mid-20th century alongside the rise of computerized information systems. As businesses began to automate operations in the 1950s and 1960s, the volume of data produced and stored grew exponentially. Mainframe computers enabled organizations to maintain large files with many data elements, but the absence of formal data/information management practices often resulted in fragmented, inconsistent, and poorly documented data.

During this era, the focus was primarily on technical aspects of information administration, such as hardware maintenance, programming, and basic record-keeping activities. While early IT professionals often served as de facto data stewards, ensuring data was input and processed correctly, the notion of data stewardship as a distinct discipline did not yet exist.

The 1970s witnessed the development of relational database models, which further highlighted the need for structured, standardized approaches to data management to enable organizations to use this content more effectively. The explosion in relational database development and usage highlighted the importance of treating data as a valuable resource, setting the stage for the emergence of formal data stewardship and data governance practices.

The Rise of Data Governance and Data Stewardship (1980s–1990s)

As organizations accumulated more complex and critical data assets, the 1980s saw the birth of data governance – a framework for defining and implementing policies, standards, and procedures for data collection, ownership, storage, and use. In this era, the lack of formal oversight and implementation of standard practices was causing organizations to experience challenges with data, such as underutilization or ineffective usage, allowing errors and other data quality issues to proliferate, and exposing data to misuse and other security issues.

Data governance is focused on three primary goals:

• Defining ownership and accountability for data and its management within an organization
• Establishing policies for data definition and standardization, data access, data usage, and data protection
• Ensuring regulatory compliance and risk mitigation for data and information within and across organizational boundaries

It became increasingly apparent that effective data governance frameworks needed operational enablers – individuals responsible for carrying out day-to-day activities to uphold data governance standards. These operational enablers were called “data stewards.”  Thus, the role of data stewards was created to support the implementation of data governance and other data management initiatives.

Data stewardship refers to the responsible management of data assets by designated individuals, ensuring data quality, consistency, and reliability throughout an organization. Unlike broader data governance functions (which focus on policy-making and oversight/management), data stewards are execution-focused, often embedded within business units as subject matter experts to ensure that data in each business area aligns with standards and policies developed by the data governance organization and is usable according to expectations.

Enterprise resource planning (ERP) systems like SAP and Oracle became widespread in the 1990s, and organizations’ need for data stewardship became more evident. ERP implementations revealed that poor-quality data could cripple enterprise operations and result in increasing costs as well as lost revenue. Large organizations began appointing business-focused data stewards who collaborated with IT staff to remediate data issues, implement data standards, and support system integrations.

Formalization and Expansion: 2000s–2010s

By the early 2000s, the professionalization of data stewardship was established in many organizations of varying sizes, especially in industries such as finance, insurance, and healthcare. Influential frameworks such as the Data Management Body of Knowledge (DAMA-DMBoK), first published in 2009, formalized data stewardship as a critical component of comprehensive data management and helped raise the level of awareness for this role in different environments.

The DAMA-DMBoK defined data stewardship as a role that works to ensure that the data content and metadata are consistent with the organization’s policies, standards, and business rules, resulting in an appropriate level of data quality for its effective use. 

This period saw data stewardship aligned tightly with three critical data management areas:

1. Data Quality

Data stewards, especially those with a business area focus, were tasked with monitoring and improving the accuracy, completeness, consistency, timeliness, and validity of data. The growing emphasis on data-driven decision-making meant that poor-quality data could no longer be tolerated, and that data must be defined properly to be used effectively. Data stewards acted as guardians of data quality, identifying anomalies, coordinating remediation efforts, and establishing preventive controls, often in conjunction with data stewards from related business areas.

2. Data Governance

Data governance frameworks became more established, and organizations recognized that data stewards played an operational role in executing data governance mandates. Data stewards worked closely with organizational bodies such as data governance councils, supported business area data owners, and coordinated with IT staff, bridging the gap between high-level policies and operational and technical realities.

3. Master Data Management (MDM)

Master data management (MDM) is a process that ensures an organization has a single, accurate, and consistent version of its critical data, such as customer, product, and location information, across all systems and applications. As MDM initiatives gained traction, business-focused data stewards became key players in defining and maintaining authoritative sources of truth for core business entities (customers, products, suppliers, etc.). In many organizations, data stewardship processes were embedded into MDM workflows to ensure sustained data accuracy and alignment across systems, thereby raising data stewardship’s profile.

The 2010s also saw increased regulatory pressures in industries such as healthcare and in processes to improve data privacy and protection that further emphasized the need for rigorous and consistent data stewardship practices.

The Modern Era: Data Stewardship in a Digital World

Today, in leading-edge organizations, data stewardship is at the heart of data-driven transformation initiatives, such as DataOps, AI governance, and improved metadata management, which have evolved data stewardship beyond traditional data quality control. Data stewards can be found in every industry and in organizations of any size.

Modern data stewards interact with:

• Automated data quality tools that identify and resolve data issues at scale
• Data catalogs and data lineage applications that organize business and technical metadata and provide searchable inventories of data assets
• AI/ML models that require extensive monitoring to ensure they are trained on unbiased, accurate datasets

The scope of data stewardship has expanded to include ethical considerations, particularly concerning data privacy, algorithmic bias, and responsible AI. Data stewards are increasingly seen as the conscience of data within organizations, championing not only compliance but also fairness, transparency, and accountability.

New organizational models, such as federated data stewardship – in which data stewardship responsibilities are distributed across teams – can promote improved collaboration and enable scaling data stewardship efforts alongside agile and decentralized business units.

Common Challenges in Data Stewardship Throughout History

Despite its increasing importance, data stewardship has faced several enduring challenges:

1. Ambiguity in Roles and Responsibilities

One persistent challenge has been the lack of clarity around data stewardship roles. Organizations often struggle to distinguish between data owners, stewards, and custodians. Without clear role definitions, data stewardship efforts can stall or duplicate efforts, leading to inefficiency and frustration.  Additionally, the questions about who is best suited to be a data steward (e.g., businessperson or IT staff member) continue to cause confusion and a lack of sustained support for the role.

2. Organizational Resistance

Data stewardship initiatives often meet resistance from business units, who may perceive data stewardship tasks as additional work without clear immediate benefits. Embedding data stewardship into daily operations requires implementing change management strategies and producing clear communication about value creation. Those resistant to change fail to realize that many data stewardship tasks are performed currently within the normal course of operations, often reducing the need to attend to other issues such as data quality errors.

3. Funding and Resources

Historically, data stewardship initiatives have been undervalued compared to more dynamic (and technically focused) data science and analytics programs. Without sustained executive sponsorship and dedicated resources, data stewardship efforts can languish, limiting their effectiveness. It is worth noting that the data science and analytics initiatives need high-quality data that originates from consistent data stewardship practices.

4. Technological Complexity

As data environments have become increasingly complex, from cloud computing, big data platforms, and hybrid architectures, data stewards have faced greater challenges in tracing, monitoring, and correcting data issues across sprawling ecosystems. The lack of historical documentation for these systems can reduce data stewards’ ability to execute their responsibilities properly.

5. Metrics and Measurement

Demonstrating the value of data stewardship is difficult without clear metrics. Many organizations have struggled to define KPIs that convincingly show the impact of data stewardship on business outcomes, making it harder to justify continued investment or to persuade good candidates to accept the role.

6. Dearth of Training and Continued Support

In many organizations, the role of data stewardship is a simple appointment that identifies the most knowledgeable subject matter expert for a business area’s data – or subset of it. Many data stewards are not offered any formal training opportunities in “how to be a data steward” and “the role of data stewards in a data governance program,” etc. The lack of trained data stewards diminishes the role’s importance and effectiveness. Over time, poor practices emerge, implementation of data governance policies declines, and the level of organizational data quality is reduced. All these challenges contribute to the view that “data stewardship is ineffective” and not worth the investment.

Conclusion

The history of data stewardship reflects the broader evolution of data management from a technical afterthought to a strategic business imperative. While data stewardship has faced enduring challenges – ranging from role ambiguity to organizational resistance – it remains a cornerstone of effective data governance and quality management. Today, as data continues to be a foundation of innovation, compliance, and competitive differentiation, the role of the data steward is more vital than ever. Organizations that recognize, empower, and modernize data stewardship functions will be better positioned to harness the full potential of their data assets in an increasingly complex digital world.