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1. Data Basics

Understand the basic ideas that help W&M use data consistently and responsibly. 

Data is created and used throughout university activities. A few shared concepts make it easier to understand information, ask questions, use reports, and know when additional context is needed. 

Data: Information created, collected, maintained, or used through university activities. 

Trusted Data: Information that can be appropriately relied upon because its meaning, source, quality, timing, and use are understood. 

Operational Reporting: Reporting used to support day-to-day university activities and answer specific operational questions. 

Analytics: Using data to understand patterns, trends, relationships, outcomes, and broader university questions. 

Data Governance: The shared responsibilities, standards, and practices that help W&M define, protect, manage, share, and use institutional data responsibly. 

Data Governance and Data Management: Data Governance sets the rules and accountability for how W&M data should be defined, accessed, used, protected, and shared. 

Data Management puts those rules into practice through the processes, controls, data structures, and technical practices used to create, maintain, integrate, protect, store, move, retain, and use data. Together, Data Governance and Data Management help W&M make data understandable, trusted, protected, and usable. 

University Activity → Data → Trusted Information → Reporting & Analytics → Decisions 

Official and Public Data: W&M publishes official institutional information for use by the university community and the public. Some information, including census-based institutional reporting, is prepared at established points in time so it can be used consistently across reporting periods. 

When an official published answer already exists, start there. 

Access Public Data

Why It Matters

Shared understanding helps people interpret information consistently, know whether it is appropriate for a particular use, and recognize when questions about source, quality, meaning, or stewardship should be raised. 

2. How W&M Organizes Data

W&M needs a practical way to connect university work, data, stewardship, and use. 

Â鶹Æƽâ°æ is a complex institution with many activities, systems, offices, and downstream information needs. Like a city relies on maps to understand how roads, utilities, services, and responsibilities connect, W&M needs a way to understand how information is created, changed, managed, and used across the university. 

W&M’s Concept Model provides that structure. 

University Activity → Data Object → Steward → Data Fact → Reporting, Analytics & Other Uses 

Example: Facilities Management → Work Order → Facilities Team (Steward) → % Work Orders Pending (Data Fact) → Work Order Report 

Why It Matters 

The model helps people: 

• know who to ask, even without an established relationship 
• understand where important information is created and changed 
• identify who helps define and steward it 
• see where information is used downstream 
• understand impacts when systems, processes, or data change 
• capture what W&M learns so it can be reused 
• provide clearer meaning and context for analytics, automation, and responsible AI 

Built Over Time 

W&M does not need to document everything at once. 

The framework can grow based on university priorities. When projects, committees, or data challenges clarify a definition, steward, trusted source, business rule, or important Data Fact, that understanding can be documented and reused. 

The goal is to build shared understanding through real university work, not create a large documentation exercise in advance. 

How the Pieces Fit Together 

Data Subject → Data Domain → Data Sub-Domain → Data Category → Data Object 

A Data Object is a reusable way to describe important university information about an activity, person, place, thing, event, or relationship. 

A Data Fact is a trusted institutional answer supported by agreed definitions, business rules, timing, trusted sources, and stewardship. 

Work Order → % Work Orders Pending 

When a Data Fact legitimately differs for a particular purpose, the difference can be documented as a Data Fact Variant so people understand when and why results differ. 

The Business Glossary and Data Catalog provide a lightweight way to document and connect definitions, rules, timing, stewardship, trusted sources, Data Facts, and approved variants. 

Data Lives Across Many Systems 

W&M information is created and used across many systems, platforms, reports, and activities. 

Operational systems support day-to-day work, while reporting and analytics platforms may bring information together for broader institutional use. The system where data originates is not always the appropriate trusted source for every reporting or analytic purpose. 

Purpose, timing, definitions, business rules, stewardship, and reporting needs help determine the appropriate source. 

3. Data Roles & Responsibilities

Good data depends on people understanding their responsibilities and knowing who to involve. 

W&M has had data stewardship in place for years in major data areas. The next step is to expand and mature that stewardship so it is more consistent, connected, and integrated into the development and operational work already underway across the university. 

We are building on existing stewardship, not replacing it. People may serve in more than one role depending on the activity, information, or project. 

Use & Support Data 

How the Roles Work Together 

When a question crosses responsibilities, systems, or Data Subjects, the relevant roles work together and may engage broader governance when needed. 

4. Data Quality

Trusted information depends on data being fit for its intended use. 

Data quality begins when information is created or entered and continues as data moves through systems, integrations, reports, and analytics. 

What Good Data Looks Like 

• Accurate — Correctly reflects what it is intended to represent. 
• Complete — Important information is present when needed. 
• Consistent — Information is represented and interpreted consistently. 
• Timely — Available and current enough for the intended purpose. 
• Reliable — Can be consistently depended on for its intended use. 
• Reasonable — Falls within expected ranges or patterns and makes sense given the activity or rules. 
• Fit for Intended Use — Appropriate for the process, report, analysis, or decision it supports. 

Everyone Has a Role 

Data Users help create quality data through careful entry and use. Data Stewards and functional experts define expectations and meaning. Data Custodians and technology professionals help address technical issues. Reporting and analytics teams often identify issues when information is brought together. 

When Something Does Not Look Right 

Identify Concern → Understand Data & Source → Involve Steward or Expert → Evaluate Impact → Resolve or Document → Monitor 

Not every difference is an error. Differences in definitions, timing, populations, or business purposes may be valid and should be understood and documented. 

Official and Regulatory Reporting 

Data used for regulatory, institutional, or other official external reporting may require additional validation. 

When appropriate, involve Institutional Research to help check alignment with official institutional definitions, census-based reporting, previously reported information, and other institutional uses. 

Data Prepared → Steward / Functional Validation → Institutional Research Check when Applicable → Official Reporting 

Learn Through the Work 

A quality issue may reveal an unclear definition, missing rule, inconsistent process, unclear stewardship, or downstream impact. Resolving the issue should improve both the data and W&M’s understanding of it. 

5. Responsible Data Use

Use the right data, for the right purpose, with the right approval, protections, and accountability. 

Responsible data use means accessing, sharing, and using W&M data in ways that support university work while meeting privacy, security, legal, regulatory, compliance, records-retention, and other institutional responsibilities. 

Questions to Ask 

• Why am I using this data? Use information for a legitimate university purpose. 
• Am I authorized to use it? Consider role, business or academic need, approval, and sensitivity. 
• Am I using only what I need? Limit information to what is necessary for the intended purpose. 
• Am I protecting it appropriately? Follow applicable privacy, security, and sharing requirements. 
• What requirements apply? Consider legal, regulatory, compliance, records-retention, contractual, and university requirements. 
• Can I share or reuse it? Confirm that a new audience or purpose is consistent with authorization, sensitivity, requirements, and appropriate use. 

Data Requests and Approval 

Requests for institutional data are tracked and reviewed based on the information requested, intended use, sensitivity, and applicable requirements. 

The appropriate Data Steward or Stewards help determine whether access or use is appropriate. Some requests may require additional approval or a signed data-use agreement documenting permitted use, protections, sharing expectations, or other conditions. 

The request, approval, and related agreement should remain traceable to the data provided and its approved use. 

Request → Steward Review → Approvals / Agreement if Needed → Data or Access Provided → Approved Use → Retention / Disposition 

Data Users do not need to be experts in every applicable requirement. They should know when to involve the appropriate steward, privacy, security, compliance, legal, records-management, or other expert. 

Data Has a Lifecycle 

Good data hygiene includes knowing when information should be created, maintained, retained, archived, or disposed of. 

Some university information must be kept for defined periods because of legal, regulatory, records-management, contractual, or university requirements. 

Data Users and Stewards do not need to memorize every retention rule. They should know that data has a lifecycle, follow applicable university practices, and ask when requirements are unclear. 

Create / Receive → Use → Maintain → Retain → Archive or Dispose 

Good lifecycle practices help W&M keep information current, reduce unnecessary or duplicate data, meet institutional requirements, and reduce privacy, security, and compliance risk. 

6. Trusted Reporting, Analytics & AI

Trusted insight depends on shared meaning, trusted sources, quality, stewardship, and appropriate use. 

The Data & Analytics section of the site explains W&M’s capabilities and platforms. This Learning Resource focuses on what helps make a report, measure, analysis, or AI-enabled use trustworthy. 

What Makes Reporting and Analytics Trusted? 

• Clear Meaning: Important terms and measures are understood consistently. 
• Data Facts: Measures use agreed definitions, business rules, timing, and calculation logic. 
• Trusted Sources: Information comes from sources appropriate for the intended use. 
• Data Quality: Information is fit for the purpose it supports. 
• Stewardship & Appropriate Use: Functional expertise, access, context, and use expectations are understood. 

From Question to Trusted Report 

Business Question → Define Data Fact → Confirm Rules & Source → Build & Validate → Use → Maintain 

A report or dashboard is more than a technical product. Its purpose, definitions, sources, users, appropriate use, and continued need should remain clear over time. 

Make Data Easy to Verify 

When data supports a recommendation, decision, presentation, or analysis, identify where it came from and provide enough detail for someone else to verify it. 

Depending on the source, include: 

Source · Date or Reporting Period · Report / Qlik App · Sheet or View · Filters or Population Used 

Example: Qlik Student Enrollment App · Enrollment Summary · Fall 2026 · Undergraduate · Census 

Citing data helps people verify numbers, understand context, reproduce an analysis, investigate differences, and reuse trusted information. 

If data is important enough to support a recommendation or decision, make it easy for someone else to understand where it came from and verify it. 

Data as a Guardrail for AI 

AI increases the importance of understanding the information being used. 

Governed data does not make every AI use appropriate, but it provides clearer meaning, context, responsibility, and boundaries for evaluating that use. 

7. How Data Governance Works in Practice 

Data governance becomes real through the way W&M resolves data questions and decisions. 

W&M already has experienced Data Stewards. The work now is to connect them with one another and with the projects, systems, reporting, analytics, and operational work where important data decisions are being made. 

This includes: 

• making stewardship roles and expectations clearer 
• connecting Stewards across Data Subjects, systems, and organizations 
• involving Stewards earlier in projects, system changes, reporting, and analytics work 
• giving cross-functional questions a clearer path through DGT 
• documenting important decisions so they can be reused 
• expanding stewardship as W&M’s data environment grows 

We do not need to wait until every rule is defined. W&M already has stewardship expertise. The DGT connects that expertise to the development and operational work underway, helps resolve cross-cutting questions, and turns what we learn into clearer, reusable practices. 

A simple maturity progression: 

Existing Stewardship → Connected Stewardship → Embedded in the Work → Reusable Institutional Practice 

Start with the Data Steward 

Many questions can be resolved directly by the people who understand the activity and information best. 

Data Question → Data Steward → Resolve → Document if Useful → Reuse 

When the Question Is More Complex 

If a question crosses Data Subjects, organizations, systems, or downstream uses, it may be brought to the Data Governance Team (DGT). 

DGT helps engage the appropriate Data Stewards and other experts to work through the issue and develop a resolution. 

Complex Question → DGT → Relevant Stewards & Experts → Resolution 

When Escalation Is Needed 

If broader institutional direction, authority, or agreement is needed, a recommendation may be elevated to the Data Governance Advisory Council (DGAC). 

Data Steward → DGT if Needed → DGAC if Escalation Is Required 

Traceable Data Requests 

Data requests outside normal operational system access should follow a light, traceable process. 

Request → Steward Review → Approval / Conditions → Fulfill → Confirm → Record 

The request should make clear: 

• what data is needed and why 
• intended use and audience 
• relevant population, timing, or filters 
• Steward approval and any conditions 
• what data or report was ultimately provided 

If the request changes, the record should be updated so there is a clear connection between what was requested, what was approved, and what was delivered. 

Some requests may require additional review or a data-use agreement based on sensitivity, intended use, regulatory requirements, or sharing conditions. 

Traceability helps W&M reduce misunderstandings, support appropriate review and approval, and make it easier to understand later what information was shared, with whom, and for what purpose. 

Build Understanding Through the Work 

Important decisions can strengthen W&M’s shared data framework by clarifying: 

definitions · business rules · stewardship · trusted sources · Data Facts · Data Fact Variants · quality expectations · appropriate use · downstream impacts 

Projects and major system or process changes should identify important data questions early so impacts can be addressed before implementation. Resolve questions at the lowest appropriate level, involve broader governance when needed, and retain what W&M learns so it can be reused. 

8. Why This Matters Now

W&M has changed for more than 334 years. Data is driving the next change. 

The university’s systems, technology, reporting, and ways of working are changing. Information that once stayed mostly within one office or system now moves across many parts of W&M. 

Universities are also using data, analytics, automation, and AI in new ways. Strong data practices help W&M keep pace, compete, and continue to be recognized as a leading institution. 

Data is driving change and helping W&M advance toward preeminence. 

A Simple Way to Think About It 

Think of W&M as a railroad. 

Schools, offices, and functions are like stations. Systems and technology provide the tracks. Data moves across the university in cars—created in one place, changed in another, and used somewhere else. 

No single station sees the whole journey. 

Why Meaning Matters 

Technology can move data quickly. Automation and AI can work with it even faster. 

But technology still needs to know what the data means. 

Shared definitions, business rules, trusted sources, stewardship, and quality provide that meaning. 

Better Meaning → Better Automation → More Responsible AI 

Governance provides the direction; data management makes it real in everyday work. 

What’s in It for Me? 

Better data practices can mean: 

• less time figuring out who to ask 
• fewer conflicting numbers and definitions 
• better information for everyday work 
• problems identified earlier 
• fewer surprises when systems or processes change 
• decisions and solutions that can be reused instead of recreated 
• greater confidence in reporting, analytics, automation, and AI 

Change is part of W&M. W&M has successfully adapted through generations of change—new programs, new ways of teaching and researching, new technologies, and new expectations. 

The goal is not to change everything at once. It is to improve the most important areas over time, learn through real university work, and build on what W&M already knows. 

We’re in It Together 

Functional experts understand the work. Data Stewards understand meaning and rules. Technology teams understand systems and movement. Analysts understand reporting and use. Data Users see needs and problems through everyday work. 

“Who knows this?” → “We know who to ask.” 

“Why are these numbers different?” → “We understand the definition and source.” 

“Will this change affect anything else?” → “We can see the downstream impact.” 

“We solved this before.” → “We documented it so others can reuse it.” 

“Can AI use this data?” → “We understand the data well enough to make that decision responsibly.” 

For W&M Faculty and Staff 

The DataHub (coming soon) provides the working resources behind these concepts. 

W&M faculty and staff can use the DataHub for detailed role guidance, stewardship information, Business Glossary and Data Catalog content, Data Facts, trusted sources, standards, procedures, templates, process guidance, and other working governance resources as they are developed.