Data Stewardship: The Keystone to Enable Data Governance

Many public sector agencies are dedicating resources to enterprise data management and governance initiatives, yet they are encountering similar implementation challenges. Organizations struggle to navigate the balance of executive leadership and tactical practitioner involvement, often yielding a program that is either too bureaucratic or lacking strategic focus. Data stewards represent the missing link between the folks who understand the mission and those who understand the details. Investments to empower data stewardship will help connect tactical data governance and execution with top-level enterprise data priorities.

Data management policy should direct organizations to manage, control, and share enterprise data by designating data stewards, a formal role in a mature data organization. A data steward is defined as an individual with assigned responsibility to provide service and leadership with respect to data management, making decisions based on the enterprise perspective. In performing these duties, appointed data stewards apply the standards of both data governance and data quality. The overarching goal of instituting data stewardship is to establish an organizational framework that facilitates information sharing, establishes data quality standards and processes, and fosters maturation of data management practice areas. Data management maturity is achieved when data is managed as an enterprise asset on par with other critical enterprise assets.

 

Principles of Effective Data Stewardship

Data stewardship is fundamental to successful execution of data governance. To be successful in this role, data stewards need to have clear roles and responsibilities, tools, processes, and empowerment. By promoting accountability for data as an enterprise asset and fostering collaboration across stakeholders, data stewardship optimizes mission performance. Principles of effective data stewardship include:

Empowerment and Capacity

• Establish clear accountability and authority for data assets by identifying, vetting, and assigning data stewards
• Align data stewards with roles, security rights, and privileges for enterprise applications, as well as data management tools and repositories
• Provide initial and ongoing training for data stewards in enterprise data management best practices and quality standards for data definition, production, maintenance, and use
• Dedicate ample data stewardship support resources to manage the data domains and data assets most critical to the mission

Collaboration and Communication

• Enable efficient collaboration and transparent communication among data stakeholders, and between data stakeholders and IT personnel
• Share information broadly yet securely
• Foster trust between data users, suppliers, and IT personnel who support data systems

Process Integration and Quality

• Integrate data management processes with business and IT processes that impact enterprise data assets, such as investment management, strategic planning, and project life cycle management
• Describe and maintain data assets in the context of the enterprise via data profiles and data catalog records
• Institute repeatable processes for cross-functional consistency

 

Data Stewardship Roles

As an organization continues to evolve through defining and implementing the scope of data stewardship, the data steward’s role moves beyond cataloging toward seeking a deeper understanding of the data needs of stakeholders from multiple functional areas, both within and external to the organization, and how data must be managed to achieve a trust level that enables reuse across the organization.

Data stewardship roles and responsibilities may vary depending on the needs of the organization they support but typically include one or more of the following:

Strategic or Executive Data Stewards — Enterprise

• Make resource decisions for data-focused efforts
• Inform and communicate strategic vision and objectives for data governance
• Set guidelines and principles for data stewardship
• Adjudicate issues that are escalated from collaborative working groups

Collaborative Data Stewards — Geographic/ Functional/Cross-Domains

• Lead a data domain or functional working groups comprising operational data stewards, which may be constituted on a permanent basis or established to address a specific issue
• Work closely with other data stewards across different data domains and/or functional areas

Operational Data Stewards — Functional “Front Line”

• Have a business/mission-oriented perspective, possess in-depth subject matter expertise in their specific areas/systems, and know the data best and “feel the pain” of data issues
• Collect, store, and maintain dataset relevant to their organizational alignment

Reference Data Stewards — Data Domain

• Focus on maintaining reference data definitions and associated codes in a rigorous way such that they can be mapped and exchanged between discrete systems across the organization

Project Data Steward — Project Level

• Support the implementation or upgrade of a major information system where data migration is often required
• Help ensure that data issues are addressed from project inception and socialized across the stakeholder community and interfacing organizations to maintain an enterprise perspective

 

A common theme among the various roles is that stewardship responsibilities should be performed as a logical extension of the current core job and mission responsibilities to which data stewards are already assigned. The purpose of implementing data stewardship is to provide repeatable models that will guide in the planning, identification, and appointment of data stewards, and drive their work based on the priorities of the data strategy.

 

Setting The Keystone to Enable Effective Data Governance at Scale

Organizations can begin to establish a successful data stewardship program to promote effective data governance through the following steps:

1. Identify and prioritize data domains and datasets for stewardship focus and investment
2. Interview stakeholders to assess challenges associated with these data domains and datasets, capture user stories, and identify baseline metrics for measuring progress (e.g., business rules for data quality)
3. Define the data stewardship model that works best for the organization, including the data steward types, roles and responsibilities, processes, training, and tools to enable effective collaboration, management, and accountability of data assets
4. Determine the level of data stewardship support (i.e., full time versus other duty as assigned) by data domains to align resources to mission priorities
5. Develop a plan of action with milestones to roll out the stewardship program, assign stewards, educate stewards and stakeholders on responsibilities, and initiate the components of the operating model defined in Step 3
6. Pilot the model, learn, refine, and continue the journey
7. Empower data stewards through various mechanisms, such as but not limited to relevant data training, a data stewardship playbook, and collaborative virtual workspaces
8. Bring the program to life and demonstrate value by implementing data quality actions, creating and improving reference data standards, and capturing collaborative decisions and success stories from data practitioners
9. Measure and visualize results, reinforce the right behaviors, and continuously improve against the baseline and user stories captured in Step 2
10. Evaluate the impact of data stewardship relative to the investments, adjust resources, update priorities, and revise the data stewardship model and operating model accordingly

 

Conclusion

In the end, people are the key to managing data at scale. Whether an organization has a fledgling or a formal data management program, investing in data stewardship will be the keystone for bearing the weight of current and future data needs.

As you contemplate how to establish and / or mature your organization’s data stewardship capabilities, please contact our Data Management Leaders for further information.