Analytics Maturity Model
The Student Affairs Analytics Maturity Model is a conversation tool for understanding where your current practices are and identifying the next capability that will create meaningful, sustainable value. It is not a ranking, certification, or judgment of staff effort. Different areas may be at different stages, and progress does not need to happen evenly across every domain.
Use the model to ask: Where are we now? What evidence supports that assessment? Which next move would remove the most important constraint?
Stage 1 | Fragmented — Local and reactive:
- Data live in separate files or systems.
- Requests often depend on individual people.
- Focus: Establish basic visibility, consistency, and ownership before expanding into advanced analytics.
Stage 2 | Organized — Standardizing:
- Definitions, collection practices, inventories, and recurring reports are becoming more consistent.
- Focus: Create repeatable practices, strengthen governance, and clarify how work gets done before expanding integrations.
Stage 3 | Connected — Integrating:
- Divisional data connect with institutional sources.
- Governance, validation, and shared definitions become increasingly important.
- Focus: Build trusted connections, reusable data assets, and analysis-ready data. Integration alone does not make data ready for analysis.
Stage 4 | Applied — Decision-focused:
- Advanced and mixed methods support program, resource, and strategy decisions.
- Focus: Strengthen analytical depth, manage demand and capacity, and connect evidence to action without outpacing maintenance.
Stage 5 | Sustained — Embedded and learning:
- Evidence use, maintenance, talent, review, and continuous improvement are institutionalized.
- Focus: Sustain, learn, adapt, and improve. Maturity is not completion; the system should continue to evolve.
Maturity by Domain: Find Your Next Capability
Use the domains below to see how practice typically develops from Stage 1 through Stage 5, and use the model as a structured conversation about where you are today, what evidence supports that assessment, and what capability would create the most value next. Bring together the people who understand the data, the work, the decisions, and the institutional context; identify the capability that is most limiting progress; and choose one practical next step that can be built, adopted, and maintained within the next planning period. Revisit the model after implementation to consider whether the change improved the quality, speed, reach, or sustainability of the work.
Stage 1 | Requests drive work
Work is primarily shaped by incoming requests and immediate needs.
Stage 2 | Priorities inform planning
Divisional or institutional priorities begin to influence what work gets planned.
Stage 3 | Questions align with strategy
Analytical questions are intentionally connected to strategic priorities and decisions.
Stage 4 | Portfolio supports decisions
A coordinated portfolio of analytical work supports program, resource, and strategic decisions.
Stage 5 | Evidence routinely shapes strategy
Evidence is routinely incorporated into strategic planning, decision-making, and continuous improvement.
Stage 1 | Files and silos
Data are distributed across individual files, systems, and program areas.
Stage 2 | Inventories and standards
Data sources are inventoried, definitions become more consistent, and basic standards emerge.
Stage 3 | Integrated, governed sources
Data sources are connected and governed with greater attention to quality, meaning, and appropriate use.
Stage 4 | Reusable analytic assets
Validated datasets and other reusable assets support multiple analytical questions and reduce redundant work.
Stage 5 | Monitored, documented ecosystem
Data assets are continuously monitored, documented, maintained, and improved as part of a sustainable ecosystem.
Stage 1 | Informal permissions
Access and use decisions depend largely on informal practices and individual knowledge.
Stage 2 | Basic roles
Ownership, access, and stewardship responsibilities begin to be clarified.
Stage 3 | Project-based access and stewardship
Roles, permissions, and stewardship practices are established for specific analytical work.
Stage 4 | Consistent review and lifecycle
Access, review, documentation, and data lifecycle practices are applied consistently.
Stage 5 | Embedded, auditable practice
Governance is embedded in routine operations, with clear documentation and practices that can be reviewed and sustained.
Stage 1 | Transactional requests
Relationships are primarily based on individual data requests and handoffs.
Stage 2 | Recurring contacts
Regular points of contact and communication begin to replace one-time interactions.
Stage 3 | Shared roles and handoffs
Partners establish clearer responsibilities across Student Affairs, IT, institutional research, and data stewardship.
Stage 4 | Co-designed portfolio
Partners jointly shape analytical priorities, workflows, and opportunities.
Stage 5 | Durable cross-functional ownership
Responsibility for the data ecosystem is shared across functions and sustained beyond individual projects or people
Stage 1 | Descriptive counts
Analysis primarily describes participation, activity, and other basic measures.
Stage 2 | Consistent comparisons
Teams establish more consistent approaches to comparisons, benchmarks, and basic analytical questions.
Stage 3 | Matched and multivariable analysis
Methods expand to account for multiple factors and strengthen comparisons between groups.
Stage 4 | Longitudinal and mixed methods
More sophisticated approaches examine change over time and combine quantitative and qualitative evidence.
Stage 5 | Method choice embedded in decision design
Analytical methods are selected intentionally based on the decision, evidence needed, limitations, and appropriate level of rigor.
Stage 1 | Key-person dependence
Critical knowledge and skills are concentrated among a small number of individuals.
Stage 2 | Basic role clarity
Responsibilities become clearer, reducing ambiguity about who does what.
Stage 3 | Dedicated expertise
Specialized analytical, technical, and data stewardship expertise is available to support the work.
Stage 4 | Distributed depth and backup
Expertise is distributed across people and functions, with greater redundancy and backup capacity.
Stage 5 | Workforce strategy and learning pathways
Talent development, succession, professional learning, and capacity planning are intentionally built into the model.
Stage 1 | Reports delivered
The primary goal is producing requested reports and information.
Stage 2 | Findings discussed
Findings are shared with stakeholders and become part of conversations about programs and priorities.
Stage 3 | Actions documented
Teams begin documenting what they will do in response to evidence.
Stage 4 | Follow-up evidence planned
Teams intentionally return to questions to examine whether actions produced the intended results.
Stage 5 | Learning cycles routinely completed
Evidence becomes part of an ongoing cycle of inquiry, action, reflection, and improvement.
Stage 1 | Project by project
Work is organized around individual projects and immediate needs.
Stage 2 | Some recurring processes
Certain activities become repeatable and are performed on a regular basis.
Stage 3 | Maintenance recognized
Teams recognize that data, documentation, integrations, and analytical assets require ongoing maintenance.
Stage 4 | Capacity and demand managed
Leaders actively balance demand, available expertise, maintenance, and new analytical work.
Stage 5 | Continuity, refresh, and adoption planned
The work includes intentional plans for continuity, refresh, adoption, learning, and long-term value.
You do not need to be at the same stage in every domain. Use the model to identify where your current capabilities are strongest, where important constraints exist, and which next capability would create the greatest practical value.