Step 1 · Assess
Data & AI Readiness Assessment
Know exactly what to fix first, and what AI your data can safely support.
Discovery follows five questions: what is the mission, who is the customer, what does the customer value, what are the results, and what is the plan. We put IT, institutional research, the operational offices, and leadership at the same table, then look through two lenses at once: the state of the data (sources, pipelines, definitions, quality) and the capacity to govern it (ownership, decision rights, skills). You leave with a scored readout, agreed result indicators, and a roadmap your cabinet can act on.
Two lenses, scored together
Governance capacity →
Good rules, little to govern
Ownership exists, but the data platform cannot answer the questions yet. Build the foundation.
Ready to scale
Trusted data and the capacity to keep it trusted. Add metrics and governed AI.
Start with the basics
Neither the platform nor the ownership is there yet. Diagnose, then build both together.
Fragile platform
Good data without owners decays. Put definitions, stewards, and decision rights in place.
Data state →
What you get
- Mission and public-value alignment for the data program
- Stakeholder map and 10–15 interviews across IT, IR, operational offices, and leadership
- User-values survey and readout
- Current-state architecture review and data-flow map
- Two-lens readiness score: data state × governance capacity
- Results framework with indicators, agreed before anything is built
- AI-readiness assessment and prioritized 18-month roadmap with costs and sequencing
How you'll know it worked
- IT, research, and operational leaders agree on what to fix first
- Leadership can fund the roadmap with a clear line to mission results
Next on the path: Modern Data Platform Build
Start with a 30-minute readiness call
Tell us what you are trying to fix or build. You will talk with the architect who does the work.
Led by Victor Manchik, founder and principal data architect