Data-Driven Strategy for Higher Education
When data is treated as an institutional asset — measured, governed, and integrated into operations — it becomes the foundation for every strategic decision. Datacentricity brings practitioner-level expertise in building that foundation.
Challenges Facing Higher Education
Higher education institutions sit on vast reserves of data — enrollment records, space utilization, research portfolios, financial systems, student outcomes — yet most operate with fragmented information, inconsistent definitions, and reporting processes that consume resources without producing actionable insight.
Data Silos & Inconsistency
When Enrollment reports a headcount of 7,200 and Institutional Research reports 6,800, the resulting confusion erodes trust in both numbers and in the offices that produced them. Leaders debate methodology instead of making decisions.
Reactive Instead of Strategic
Without standardized metrics and certified Sources of Truth, institutions spend cycles reconciling data rather than acting on it. Annual surveys become data-entry exercises instead of defensible institutional assets.
Compliance & Accreditation Risk
Federal reporting bodies that receive inconsistent submissions expose the institution to compliance risk. Accreditation reviewers who encounter conflicting numbers raise questions about institutional effectiveness.
Unknown Consumption Points
Decentralized units building their own BI, shadow databases, ad-hoc extracts, and department-specific dashboards — hundreds of ungoverned consumption points producing conflicting versions of the same metric with no shared standards.
Elevating Digital Literacy Across Three Strategic Pillars
Advancing digital literacy is not a single initiative — it is the convergence of Partnership, Process, and Technology working in unison. Each pillar reinforces the others: collaborative partnerships build institutional alignment, reproducible processes ensure data integrity, and integrated technology eliminates silos. When all three move together, data becomes the shared language of the institution.
🤝 Partnership
Cultivating a campuswide culture of shared responsibility and data-informed decision-making by building collaborative partnerships, enhancing transparency, and advancing institutional alignment through strategic training, effective communication, and the adoption of relevant data as our shared organizational language.
⚙️ Process
Employing a creative, leading-edge approach to design reproducible, transparent, and automated processes that uphold data integrity in the collection, analysis, and management of spatial information.
💻 Technology
Implementing a state-of-the-art technology framework that seamlessly integrates with leading market solutions and university-approved systems, platforms, and data sources — ensuring smooth data exchange, reducing silos, and enabling high-integrity spatial data management to drive transparency and holistic decision-making.
Ready to Elevate Your Institution?
Data Governance for Modern Institutions
Building an institutional foundation for data-informed decision-making — treating data not as a byproduct of operations, but as an institutional asset requiring deliberate stewardship.
The Institutional Imperative
Effective data governance is not an IT initiative — it is an institutional imperative. Governance establishes the policies, roles, standards, and processes that ensure data is managed with the same rigor applied to financial assets, human capital, and physical infrastructure.
Data Governance Drivers in Higher Education
Six strategic imperatives accelerating the need for enterprise data governance across R1 universities and academic medical centers
Guiding Principles
Six foundational principles that anchor every governance decision
Data as an Institutional Asset
Data belongs to the institution, not any department. Stewardship replaces ownership.
Transparency & Accountability
Every governance decision is documented, traceable, and auditable.
Regulatory Compliance First
FERPA, GLBA, and accreditation compliance is the non-negotiable baseline.
Cross-Unit Collaboration
Shared definitions and certified Sources of Truth break down data silos.
Sustainability Over Speed
Each phase builds institutional capacity that endures beyond implementation.
Digital Literacy & Decision Transparency
Empowering people to understand, trust, and use data effectively.
Governance Structure & Roles
Clear accountability from executive leadership through operational users
Executive Sponsor
Cabinet-level champion providing authority and institutional buy-in
Data Governance Lead
Day-to-day program manager coordinating all governance activities
Data Stewards
Functional experts responsible for quality, definitions, and compliance within their domains
Data Custodians
Technical staff managing systems, infrastructure, and security controls
Data Consumers
End users who access governed data for reporting, planning, and operations
Data Governance Steering Committee
VPs, Provost, CIO, CISO, Registrar — meets monthly in early phases, quarterly after maturity is established.
Data Management Working Group
Data custodians, system admins, functional leads — meets biweekly to coordinate operational governance tasks.
Phased Implementation Roadmap
Six overlapping phases designed for sustainable institutional adoption
Phase 1: Discovery & Current-State Assessment (Months 1–3)
Stakeholder interviews, data landscape mapping, maturity assessment, and gap analysis across all institutional systems.
Phase 2: Governance Framework Design (Months 3–5)
Draft governance policies, define data standards, establish committee charters, and design metadata architecture.
Phase 3: Data Prioritization & Classification (Months 4–7)
Apply weighted scoring matrix to identify Priority 1 data domains. Classify data by regulatory exposure, strategic value, and cross-unit dependency.
Phase 4: Operationalization & Tooling (Months 6–10)
Deploy data quality dashboards, implement stewardship workflows, and integrate governance into existing business processes. Including enterprise decision support tooling — standardized analytical frameworks and governance-aligned reporting that ensures consistency across all institutional analyses.
Phase 5: Training, Adoption & Culture Change (Months 9–14)
Institution-wide training programs, digital literacy initiatives, and change management to embed governance into campus culture.
Phase 6: Continuous Improvement & Maturity Scaling (Months 12–18+)
Establish continuous improvement cycles, advance maturity model levels, and scale governance to support strategic institutional initiatives.
Note: Phases overlap intentionally to maintain momentum and allow parallel workstreams.
Phase 2 Expanded List for Full Metadata Architecture Table — Room FICM Code as Sample Data Element
| Metadata Component | What “Room FICM Code” Looks Like in This Layer |
|---|---|
|
1
Metadata Model (Blueprint)
|
Business: “Room FICM Code” = standardized space classification per FICM (e.g., 110 Classroom, 210 Lab) Technical: Field stored in SPACE.ROOM.FICM_CODEOperational: Updated nightly from FAMIS/ARCHIBUS Regulatory: Used in IPEDS Finance & Space reporting Security: Low sensitivity, read-only for most users |
|
2
Data Standards Framework
|
Naming convention: ROOM_FICM_CODEDefinition: “The official FICM classification assigned to a physical room” Allowed values: 110, 210, 250, 310, 410, etc. Formatting: 3-digit numeric code Taxonomy: Aligns with FICM 2006/2023 standard |
|
3
Source of Truth Metadata Structure
|
Domain: Space & Facilities System of Record: FAMIS or ARCHIBUS Custodian: Facilities Planning Certification Date: 2025-03-01 Quality Score: 4.2/5 Recertification: Annual Lineage: FAMIS → ETL → Space Mart → BI (Operational Source → Movement → Source of Truth → Consumption Layer) Audit Trail: Last updated by Space Management |
|
4
Lineage Framework
|
System → System: FAMIS → Data Warehouse → Power BI Space Dashboard Table → Table: FAMIS.ROOM → DW.SPACE_ROOMField → Field: ROOM.FICM_CODE → SPACE_ROOM.FICM_CODETransformations: None (direct copy) Business rules: Must match FICM taxonomy |
|
5
Metadata Storage Layer
|
Stored in: • Data Dictionary entry: “Room FICM Code” • Metadata repository: Collibra / Alation / Data Cookbook • Cloud metadata: Column comments + tags • API metadata: Exposed as roomClassificationCode
|
|
6
Stewardship & Ownership Metadata
|
Data Steward: Director of Space Planning Data Owner: VP of Facilities Responsibilities: Maintain definitions, ensure accuracy, approve changes, certify annually |
|
7
Regulatory Metadata
|
IPEDS: Required for space classification in Finance & Facilities reporting Accreditation: Supports facilities utilization documentation Clery: Not directly regulated but used for campus geography mapping FERPA/GLBA: Not applicable |
|
8
Quality Metadata
|
Completeness: 100% required for all assignable rooms Accuracy: Must match FICM standard (95%+ target) Timeliness: Updated within 24 hours of room changes Authority: Facilities is authoritative source Accessibility: Read-only for campus; editable only by Facilities |
|
9
Access & Security Metadata
|
Sensitivity: Low Access rules: Read access for IR, Space Planning, Academic Affairs Permissions: Write access restricted to Facilities Audit: All changes logged in FAMIS |
|
10
Integration With Governance Processes
|
Used in: • Prioritization scoring (Space domain) • Source of Truth certification • Stewardship workflows (room recoding) • Data quality dashboards (invalid FICM codes) • Recertification cycle (annual) |
Data Prioritization Framework
A three-tier system with weighted scoring to focus governance where it matters most
Regulatory & Compliance-Driven
FERPA, GLBA, IPEDS, Clery, Sponsored Projects — data with direct regulatory exposure requiring immediate governance
Strategically Critical
Enrollment projections, retention, program financials, space utilization — data driving institutional strategy
Cross-Unit & Operational
Course sections, employee data, facilities data — operational data benefiting from shared standards
Sample Weighted Scoring Matrix
| Criterion | Weight | Sample Score | Weighted Score |
|---|---|---|---|
| Regulatory Exposure | 30% | 2 | 0.60 |
| Strategic Alignment | 25% | 4 | 1.00 |
| Cross-Unit Dependency | 20% | 4 | 0.80 |
| Data Quality Risk | 15% | 3 | 0.45 |
| Remediation Feasibility | 10% | 3 | 0.30 |
| Total | — | 3.15 | |
Score of 3.15 falls in the 2.5–3.4 range → Tier 2 (Phases 4–5)
Note: Scores for each criterion range from 1 (lowest) through 5 (highest).
Sources of Truth Registry & Methodology
A Source of Truth is the single, authoritative, curated, and institutionally recognized system or dataset for a given data domain. It is the system that produces the "official number" — the figure that appears in board reports, accreditation submissions, federal filings, and cabinet presentations. A Source of Truth is not simply the system that happens to contain the most data — it is the system that has been formally evaluated, validated, and certified as meeting the standards required for institutional reliance.
Four Defining Characteristics
Singular
One and only one Source of Truth per data domain, eliminating ambiguity when multiple systems claim authority
Institutionally Recognized
Formally designated by governance bodies with documented authority and accountability
Universally Used
All units use the Source of Truth for official reporting — no parallel data stores or shadow systems
Documented & Transparent
Lineage, quality standards, and certification status are documented and available for review by all stakeholders
Certification Methodology
Nomination
A system or dataset is nominated as the candidate Source of Truth for a specific data domain.
Validation
The candidate is evaluated against five criteria: Completeness, Accuracy (95%+ target), Timeliness, Authority, and Accessibility.
Conflict Resolution
When multiple systems claim authority for the same domain, the Working Group facilitates resolution.
Certification
The Data Governance Steering Committee formally certifies the Source of Truth.
Recertification
Sources of Truth are recertified annually or when triggered by system migration or quality degradation.
Sample Registry Template
| Data Domain | Source of Truth System | Data Custodian | Certification Date | Quality Score | Recertification Schedule |
|---|---|---|---|---|---|
| Student Enrollment | Banner SIS | Registrar | 2026-11-15 | 97% | Annual — November |
| Financial Aid | PowerFAIDS | Director of Financial Aid | 2026-12-01 | 95% | Annual — December |
| FICM/Space | ARCHIBUS | AVP Facilities | 2027-01-15 | 93% | Annual — January |
Registry entries include data lineage documentation, quality standards, and audit trail. First certification cohort targets 3–5 critical data domains.
Sample Success Metrics
Measurable targets that demonstrate governance value
Data quality accuracy target across Priority 1 domains within 18 months
Institutional reports certified with governed sources and definitions within 18 months
Reduction in data-related escalations within 12 months
Stakeholder confidence rating / data trust within 12 months
Ready to Build Your Data Foundation?
Every institution sits on a wealth of underutilized data. A structured governance program turns fragmented information into a strategic advantage.
Location Intelligent Campus
Location & the Science of Where
Space is the universal key that unifies data.
By exposing the location element in data, we integrate disparate datasets, reveal hidden relationships, and surface insights that would otherwise remain invisible. This is the power of the Science of Where — capitalizing on the inherent value of location to drive holistic institutional understanding and enable a new generation of spatially-informed strategic insight.
A Location Intelligent Campus
SPACE is the unifying platform through which teaching, research, student life, and institutional strategy are delivered. When space is treated as a strategic asset — measured, governed, and integrated into institutional operations — it becomes the consolidating lens for cross-functional alignment, tangible strategic planning, fiscal responsibility, and holistic business operations.
Applying Intelligence to Infrastructure
Enabling Cross‑Functional, Strategic, and Responsible University Operations
Integrating geospatial capabilities and interconnecting disparate data from across the university creates live, trusted information that powers timely decisions, aligns cross‑functional teams, and generates new, actionable insights for planning and operations.
Data Sources with Inherent Location
Interconnecting disparate data for holistic institutional intelligence
Analyses & Operational Value
Transforming campus data into actionable spatial intelligence
Analytics Capabilities
- Class Utilization (semester-over-semester comparison)
- Class Contact Hours Analysis
- Department Space Allocations
- ASF by Position Type (HR-linked)
- Lab Productivity (Sponsored Projects + F&A sourced)
- Housekeeping Scheduling Optimization
- Occupant Management (HR-linked, read/write)
- IDC Reporting (MTDC)
- Space Audit & Data Collection (geographically driven, centrally captured, automated)
- Enterprise Decision Support (business case development for capital projects and space investments)
Institutional Stakeholders Served
Strategic Framework
15 principles that define a location intelligent institutional strategy
Advancing Institutional Goals
Connecting spatial intelligence to academic, research, and operational outcomes
Academic Goals
- Student success through enrollment, retention, and space utilization data to identify bottlenecks
- Research excellence by correlating space and funding allocations with grant productivity
- Inclusive excellence via equitable resource distribution across departments and demographics
Research Goals
- Lab productivity metrics sourced from Sponsored Projects and F&A
- PI-level space attribution for defensible indirect cost recovery
- Space and funding correlation with faculty output
Operational Goals
- Space planning: requests, vacancy tracking, swing space
- Cost allocation and billing
- Annual survey and space audit management
- Wayfinding and emergency management
- Data reconciliation and institutional reporting
- Capital Planning
Reimagine Your Campus Infrastructure
Space is more than square footage — it is the connective tissue of institutional strategy.
Annual Space Utilization Survey
Modernize a manual, high-burden business process by deploying a novel geospatial survey interface that reduces substantial effort, and delivers 98%+ data accuracy through smart controls, governed validation, and integrated HR and facilities data.
The Solution
A modern, live data entry interface with:
- Common Definitions (i.e. FICM Codes)
- Sign-off & Accountability
- Data Collection Values Centrally Stored
- Easy-to-Follow User Interface
- Floorplan Navigation
- HR Room Occupant Values
- Embedded Smart Controls & Validation
- Image Capture Capability
Why This Framework Works
Six capabilities that distinguish Christina's practitioner-grade approach
Research Space Compliance
PI certification as the gold standard for defensibility in labs and mixed-use environments — due to PI data access and visibility into the Sources of Truth related to Space.
F&A Defensibility
Audit-ready data that directly supports federal indirect cost rate proposals
Cross-Functional Governance
Aligned stakeholders from Facilities through Finance through Research Administration
IWMS/CMMS Integration
Seamless data flow between space management and institutional systems
Institutional Change Management
Structured engagement that builds buy-in across complex organizations
Multi-Unit Process Leadership
Coordinating survey cycles across schools, departments, and research centers
Modernize Your Annual Space Utilization Survey
Structured QA transforms survey data into something defensible for federal auditors.
Higher-Education Metrics That Drive Strategy
From Course Contact Hours to PI Indirect Cost Recovery — building the measurement infrastructure that turns institutional data into decisive action.
Why Metrics Matter in Higher Education
Higher education faces an era of unprecedented accountability. Accreditation bodies demand evidence of institutional effectiveness, boards require data-driven budget justifications, and prospective students compare outcomes across institutions. A modern metrics infrastructure delivers real-time visibility into the factors that drive institutional success — enabling leaders to make confident, timely decisions.
Novel Utilization Metrics & Dashboards
Purpose-built metrics and live spatial dashboards that elevated digital literacy and accelerated time to insight for executive decision-making
Develop novel utilization metrics and build live visual and spatial dashboards and self-service tools that elevated digital literacy and accelerated time to insight for executive decision-making. These metrics go beyond standard reporting — they connect space, people, finance, and research into a unified analytical framework.
Research Space
- PI & Function Code Percent Occupancy — research space attribution at the principal investigator level, linked to functional use codes
- ICR Recovery — indirect cost recovery metrics tied directly to space allocation and sponsored project activity
Classroom Space
- Average Fill Rate — actual enrollment vs. seat capacity per section, per room
- Contact Hours — total instructional hours per room, per department, per semester
- Semester Utilization — scheduled hours as a percentage of available hours across the full academic term
Administrative Space
- Sq Ft by Position Type — space allocation normalized by HR position classification (faculty, staff, admin, research)
- Sq Ft by Room FICM Utilization Code — space distribution analyzed by standardized facility inventory classification codes
Live Dashboards & Self-Service Tools
All metrics are delivered through live visual and spatial dashboards — interactive, self-service tools built on ESRI GIS and integrated with institutional data sources. These dashboards are designed for broad accessibility, enabling leaders from Provosts to department chairs to explore data independently and arrive at insight without waiting for report requests.
Key Operating & Design Principles
Ten foundational principles that guide every aspect of the location intelligent campus
Location-Centric Data Model
Every data point is tied to a physical space — whether a room, building, or campus zone — enabling precise context and relevance in all applications.
Interoperability by Design
Seamlessly integrates with other systems, platforms, or data sources, ensuring smooth data exchange and minimizing silos.
Live & Automated
Real-time data flows and automations are embedded throughout the system, reducing manual effort and enabling proactive insights.
Single Source of Truth (SSOT)
All systems tap into verified, authoritative data sources, eliminating duplication and ensuring consistency across departments and tools.
Metadata-Rich Spatial Data
Images and spatial representations carry rich metadata, enabling advanced analytics, visualization, and traceability.
Contract-Free Flexibility
Not bound by proprietary contracts, allowing for agile evolution and vendor independence.
Scalable & Growth-Oriented
Designed to grow with the university's needs, supporting expansion in both data volume and functional scope.
Disparate Business Data Integration
Harnessing the location element to interconnect disparate business data — enabling holistic analytics for deeper insights and smarter, more aligned decision-making.
Low Cost & High Reliability
Leveraging open standards and efficient infrastructure, the system delivers robust performance at a sustainable cost.
Accessible & Self-Service
Tools and dashboards are designed for broad accessibility, empowering users with self-service reporting and intuitive interfaces.
Partnership Pillars
Advancing Space as a Strategic Institutional Asset — Elevating Digital Literacy
Employ a creative, leading-edge approach to design reproducible, transparent, and automated processes that uphold data integrity in the collection, analysis, and management of spatial information.
Implement a state-of-the-art technology framework that seamlessly integrates with leading market solutions and institution-approved systems, platforms, and data sources.
Foster a culture of shared responsibility and data management awareness by soliciting partners, promoting transparency, and building institutional alignment.
Unlock Your Institution's Data Potential
The gap between data-rich and data-driven is a measurement strategy. Let's build the metrics infrastructure your institution needs to lead.
AAU Ambitions
The Association of American Universities represents the pinnacle of research university distinction. Achieving — or maintaining — AAU membership requires deliberate, metrics-driven institutional strategy. Build the assessment frameworks and monitoring infrastructure to measure, track, and close the gap.
From Aspiration to Invitation
Why AAU Membership Matters
AAU membership signals top-tier research competitiveness, high faculty distinction, strong doctoral and postdoctoral training, robust federal funding success, and national visibility in policy and higher education leadership. These metrics often become internal strategic targets — presidents, provosts, and VPRs track them closely because they shape institutional reputation and federal funding competitiveness.
69 U.S. institutions and 2 Canadian institutions. Membership is by invitation only and requires an affirmative vote of three-quarters of current members.
AAU membership is by invitation only. The Membership Committee periodically evaluates both non-member universities for possible membership and current members for continued membership.
Phase I Indicators
The core quantitative measures AAU weighs most heavily
Competitively Funded Federal Research Support
Total research expenditures from NIH, NSF, DoD, DOE, and NASA — the primary measure of an institution's ability to compete for and secure federal research dollars.
Normalized per tenured/tenure-track facultyPrestigious Faculty Awards, Fellowships & Memberships
National Academies memberships (NAS, NAE, NAM), AAAS Fellows, Guggenheim Fellows, MacArthur Fellows, Sloan Fellows, and other top-tier distinctions that signal faculty eminence.
Total count + normalized per facultyCitation Impact of Faculty Research
Total citations normalized per faculty, typically measured using Clarivate Web of Science data — reflecting the reach and influence of institutional scholarship.
Normalized per tenured/tenure-track facultyBooks Published
Reflects scholarly productivity in humanities and social sciences beyond journal articles — a critical measure for institutions with comprehensive programs across disciplines.
Total count + normalized per facultyPhase II Indicators
Providing a broader picture of institutional research and graduate education strength
USDA, State & Industry Research Funding
Research funding beyond federal competitive grants — capturing agricultural, state-funded, and industry-sponsored research activity. Normalized per faculty to account for institutional size.
Doctoral Education Strength
Number of academic and professional doctorates awarded, normalized per faculty. Measures the institution's capacity to produce the next generation of scholars and researchers.
Postdoctoral Appointments
A proxy for research intensity and training environment quality. High postdoc counts indicate active, externally funded research programs with robust mentoring infrastructure.
These indicators help AAU calibrate performance across institutions of different sizes and research profiles.
How AAU Evaluates These Metrics
AAU normalizes nearly all quantitative indicators by tenured/tenure-track faculty count to ensure fair comparison across institutions of different scale. The normalization denominator includes full-time employees (excluding medical schools) with faculty status who are tenured or on the tenure track — instructional staff, research faculty, and management. For institutions with a medical school, basic science medical school faculty counts are included.
Benchmarking Non-Members
Compare non-member universities against current AAU members to identify institutions whose research profile exceeds that of existing members
Identifying Rising Institutions
Surface institutions whose trajectory on Phase I and Phase II indicators positions them for membership consideration
Reviewing Current Members
Assess current members whose performance has declined relative to peer institutions or below the criteria for admission of new members
Proposed AAU Assessment Framework
Assessing readiness and monitoring movement toward AAU status
Baseline Assessment & Peer Benchmarking
Conduct a comprehensive baseline assessment across all Phase I and Phase II indicators, benchmarking your institution against current AAU members and aspirant peers. This includes raw and normalized metrics, gap analysis by indicator, and identification of your strongest and weakest dimensions.
Data Infrastructure & Governance Alignment
AAU metrics depend on clean, governed, certified data from research administration, HR, finance, the registrar, and sponsored programs. Assess your data infrastructure against AAU reporting requirements — identify Sources of Truth, resolve inconsistencies, and establish the governance framework needed for defensible, reproducible metrics.
Strategic Metric Dashboard Design
Design and implement a leadership-facing AAU readiness dashboard that tracks all Phase I and Phase II indicators in real time — with trend lines, peer comparisons, and normalization adjustments. This becomes the provost's and VPR's primary instrument for tracking institutional trajectory. This dashboard follows enterprise decision support standards — consistent assumptions, transparent methodology, and scenario modeling that enables leadership to evaluate investment trade-offs across faculty hiring, research infrastructure, and doctoral program expansion.
Continuous Monitoring & Strategic Advising
AAU evaluation is not a single point in time — it is a trajectory. Establish continuous monitoring protocols, annual benchmarking cycles, and strategic advisory sessions that help leadership prioritize investments in faculty hiring, research infrastructure, doctoral programs, and postdoctoral support to move indicators in the right direction.
Adoptable Metrics for AAU Tracking
Metrics your institution can adopt to assess and monitor movement toward AAU status
| Category | Metric | Data Source | Normalization | Target Benchmark |
|---|---|---|---|---|
| Research Funding | Total federal R&D expenditures | NSF HERD Survey, Sponsored Programs | Per T/TT faculty | AAU member median |
| Research Funding | NIH funding | NIH RePORTER | Per T/TT faculty | AAU member median |
| Research Funding | NSF funding | NSF Award Search | Per T/TT faculty | AAU member median |
| Research Funding | USDA/state/industry funding | HERD Survey | Per T/TT faculty | AAU member median |
| Faculty Distinction | National Academy members (NAS, NAE, NAM) | Academy rosters, Provost records | Total + per T/TT faculty | AAU member median |
| Faculty Distinction | Major awards & fellowships | Provost/Dean records | Total + per T/TT faculty | Top quartile AAU |
| Research Impact | Total citations (Web of Science) | Clarivate InCites | Per T/TT faculty | AAU member median |
| Research Impact | Books published | Faculty activity reporting | Per T/TT faculty | AAU member median |
| Doctoral Education | PhDs awarded (academic) | Registrar, IPEDS | Per T/TT faculty | AAU member median |
| Doctoral Education | Professional doctorates awarded | Registrar, IPEDS | Per T/TT faculty | AAU member median |
| Research Workforce | Postdoctoral appointments | HR, Research Administration | Per T/TT faculty | AAU member median |
| Faculty Base | T/TT faculty count (excl. medical) | HR/IPEDS | Denominator metric | Stable or strategic growth |
| Faculty Base | Basic science medical faculty | Medical school HR | Included in denominator | Per AAU methodology |
All metrics follow AAU's normalization methodology: denominator is tenured/tenure-track faculty excluding medical schools, with basic science medical faculty included for institutions with medical schools.
AAU Membership Indicators — Quick Reference
| Category | Metric | Phase |
|---|---|---|
| Research Funding | Competitively funded federal research (NIH, NSF, DoD, DOE, NASA) | Phase I |
| Faculty Distinction | National Academies memberships + major awards & fellowships | Phase I |
| Research Output | Citations (Clarivate Web of Science) | Phase I |
| Research Output | Books published (humanities/social sciences) | Phase I |
| Research Breadth | USDA, state, and industry research funding | Phase II |
| Graduate Education | Academic and professional doctorates awarded | Phase II |
| Research Workforce | Postdoctoral appointments | Phase II |
All indicators evaluated in raw totals and normalized per tenured/tenure-track faculty.
Is Your Institution Ready for AAU?
The path to AAU membership is a multi-year strategic commitment. It starts with knowing exactly where you stand today — across every indicator, against every peer. Build the assessment, the infrastructure, and the monitoring framework to turn aspiration into trajectory.