Free resource
Data & AI Readiness Checklist for Colleges
Tick every statement that is true at your college today; each unticked item is a place to start, not a failure. It takes about 15 minutes and is written for CIOs, institutional research directors, and cabinets or AI task forces planning a data or AI investment.
Download the PDFOr tick the boxes on this page. No sign-up needed.
Answer it together if you can: one person from IT, one from institutional research, and one from an operational office such as financial aid or the registrar. Where your answers differ, that difference is itself a finding.
This is a self-check, not a score. There are no points to add up and no pass mark.
1Purpose and results
A data program pays off when it is tied to what the college exists to do and to results someone outside IT would notice.
2People and governance
Trusted data is a team result: IT holds the systems, institutional research holds the meaning, and operational offices hold the business rules.
3Definitions and metrics
Most reporting conflicts are definition conflicts: every office is right by its own rules.
4Data platform and reporting
A governed platform lets every report and every AI tool read the same numbers, and keeps working when systems change.
5Privacy, security, and AI readiness
AI is only as trustworthy as the definitions and controls behind it.
6Skills and sustainability
A platform is only an asset if your own staff can run and extend it.
What your answers mean
Where your unticked items cluster tells you where to start. Most colleges begin with sections 1 to 3, because a platform built before purpose and definitions are settled tends to repeat old conflicts faster.
1. Purpose and results, or 2. People and governance
Agree the mission, owners, and results first. A short, structured assessment with IT, research, and operations at one table usually settles them in weeks.
3. Definitions and metrics
Agree and document the handful of measures leaders argue about, then encode them once in a shared model.
4. Data platform and reporting
Plan a phased platform build that starts with the subject area where value is highest and keeps daily reporting running.
5. Privacy, security, and AI readiness
Put an AI use policy and data guardrails in place before any pilot, and pilot only on data you already trust.
6. Skills and sustainability
Build alongside your staff, with documentation and training, so the work stays yours.
Want a second opinion on your answers?
Book a free 30-minute readiness call with the architect who does the work. You will leave with a clear next step, whether or not we work together.
Led by Victor Manchik, founder and principal data architect
Prefer email? info@nvlynx.com · Choose a time