Program Charter | Align: Data Governance

Typical Scenarios

I. Introduction

1. Background

• Why is the program necessary?

The University of Alabama Data Governance Program will support and advance a clear oversight and direction for the university's data assets to ensure availability, usability, integrity, privacy, and security throughout the institution.

• What problem is being solved?

Data leveraging inefficiencies, decreased trust, access legal and regulatory data, nonvalue-added work.
The University of Alabama Data Governance Program will formalize and apply best practices to work already being done at the university. This will result in the work that University of Alabama users already do being standardized, shared, and leveraged across a wider audience, eliminating siloed data efforts, and improving data literacy.
The purpose of this charter is to summarize the mission, define the scope, establish objectives, and identify initial roles and responsibilities for the University of Alabama Data Governance Program.

The University of Alabama's Data Governance Program is built on a collaborative foundation dedicated to furthering the University's Flagship Goals.

II. Scope

• Initiate an institution-wide Data Governance practice based upon a best practice Data Governance Framework, inclusive of methods and tools to monitor, enforce and remediate adherence to policies, practices, and standards.

• Develop standardized methods and tools to define and document common terms, definitions, data standards, proper use and data lineage.

• Develop an approach to identify and remediate data-centric risks and issues.

• Develop training that improves data awareness for policies, roles and responsibilities, processes, metadata availability, etc.

• Develop training for users of data management tools.

• Develop a consistent approach to ensure proper and timely access to data, while monitoring to ensure data security risks/issues are promptly remediated.

2. Goals

• Improved Data-Informed Decision-Making empowered by trusted data.

• Increased data literacy including common understanding of available data, where the data resides and how and when to use it, leading to increased self-service.

• Reduction in duplicative data curation efforts and improved resource focus on analytics.

• Enhanced transparency of data accountability and responsibility.

• Improved collaboration in defining data including descriptions, standards, and appropriate use cases.

3. Business Benefits or Use Cases

• Clearly defined processes and procedures

• Improved engagement in the data-centric decision-making process

• High-quality data for operations and analytics

• Better controls over privacy and security (both internally and externally)

• Improved regulatory compliance and risk reduction

4. Key Metrics

• The following are the key metrics areas that the Data Governance Program is authorized to track and audit:

Training and Awareness
Assigned Accountability and Responsibility
Metadata Management
Data Management Program Engagement
Data Quality

5. Assumptions

• Executive leadership actively and vocally supports the Data Governance Program

• Data Governance identified resources are allocated within sufficient time to execute their responsibilities.

• Data Governance Chair and Data Governance Council have the authority to enforce Data Governance

6. High-Level Risks

• Program is not resourced appropriately.

• Continuing to lose ground with respect to making data-informed decisions as we focus on low-level data cleansing and curation.

• Continuing to make decisions on sub-par data, leading to non-desired results.

III. Organization

Data Governance Executive Steering Committee

Responsibilities

Provides the data governance strategy
Facilitates data governance funding, resources, and prioritization
Resolves issues escalated from the Data Governance Council

Membership

Executive Sponsor (or Co-Sponsors) – Chair
Typically, AVP or above
DG Program Director
DG Lead – Facilitator, non-voting
Legal representative
IT representative
Other functional areas as appropriate

Typical Scenario

Sets the annual agenda for data governance and aligns priorities and funding to it.

Data Governance Office

Responsibilities

Drives development of data governance practices
Enterprise data governance leadership
Facilitates development, deployment, and execution of data governance standards, processes, and practices.
Develops, produces, and monitors data governance metrics.
Develops, produces, and monitors data quality metrics.
Professional development and training

Membership

DG Lead
Other positions (e.g., data analyst) as appropriate

Typical Scenarios

On-boarding new Data Stewards or Custodians, managing the metadata management workflow, and monitoring data quality metrics.

Data Governance Council

Responsibilities

Provides the data governance operational and tactical framework
Ensures consistency in data governance standards, practices, and procedures
Prioritizes and approves cross-domain data initiatives
Resolves issues submitted for consideration

Membership

DG Lead – Chair
Typically, Director or above
Legal representative as needed.
IT representative as needed
Other functional areas as appropriate

Council Roles

Data Governance Lead (role described in a separate section in this document)
Data Executor (role described in a separate section in this document)
Function Executor
- Business Leader in the organization who represents the Data needs of one or more major processes or applications.
  - Active Participant in the Data Governance Council
  - Represent their functional area during data strategy discussions.
Advisor
- Business or Technical Leaders in the organization who contribute insights and strategy to ensure strategic decisions are made in the interest of their area and the enterprise as a whole.
  - Participates in the Data Governance Council on an as-needed basis to represent their specialty.
  - Reviews Data Governance deliverables

Typical Scenario

Resolving a university-wide issue with student data.

Data Steward Community

Responsibilities

Provides the data governance operational and tactical framework
Ensures consistency in data governance standards, practices, and procedures
Prioritizes and approves cross-domain data initiatives
Resolves issues submitted for consideration

Membership

DG Lead – Chair
Data Stewards
Functional managers
Other functional areas and SMEs as appropriate

Collaborating to define university-wide definitions for the various stages of the prospect-student-graduate lifecycle.

Data Governance Organizational Model

IV. Success Factors

• Data Governance must be viewed as an on-going program, not a project, with regular reviews leading to appropriate updates or enhancements to stay relevant to business needs.

• Long-term Data Governance must have executive sponsorship from the highest levels of the organization. Executive sponsors must be involved, take significant ownership of the effort, and champion the initiative.

• Data Governance programs must have real authority which includes the ability to resolve data management issues, review project data issues, settle disputes, and hold leaders accountable for adherence to standards.

• All data-management projects/efforts (e.g., Cloud ERP) should adhere to the university’s data governance practices.

• Data Governance guiding principles should be instituted throughout the organization and cannot be viewed as optional.

• Business Process Owner and Business Specialists should be leaders in the area they represent.

• Data Stewards must be Subject Matter Experts (SMEs) in their respective process, function, or domain.

• The responsibilities of Data Executors and Stewards should be fundamental attributes of their role; their responsibilities should be clearly communicated and maintained.

• There should be a clearly defined set of Data Governance, Data Stewardship, and Data Quality metrics which can be used to measure the overall program success.

• There must be a clear and timely communication method for Data Governance initiatives at all levels.