What does a university's alumni data actually say about graduate outcomes? Most institutions cannot answer this question today. This proposal explores how to close that gap.
Every university with a large alumni base faces a similar challenge: the registry holds thousands of names, but what graduates actually do after leaving the institution remains largely unknown. The questions below frame the scope worth exploring.
These questions are not new. Most institutions already have them in some form — embedded in accreditation requirements, ranking submissions, or internal reviews. What is missing is the data infrastructure to produce answers that are systematic, verifiable, and repeatable year over year.
Current approaches typically rely on voluntary alumni surveys (low response rates, self-selection bias) or manual lookups (unscalable, inconsistent). The registry itself contains graduation records, but career data, professional affiliations, publications, and geographic distribution exist outside the institution's walls.
Of an estimated 41,500 registered alumni, fewer than 600 have career data that can be verified through public sources. The remaining estimated 40,000 records hold graduation details but no outcome signal. Comprehensive insight into graduate outcomes remains out of reach — not because the data does not exist, but because it has not been systematically connected back to the registry.
A methodology for connecting registry data to publicly available professional information — systematically, at scale, with verifiable confidence in every data point.
A systematic process for locating publicly available professional information associated with each registry record. The approach combines structured queries across multiple public data sources — professional networks, corporate registries, academic databases, and publication indices. Each source is queried using identity signals (name, graduation year, programme) to locate matching profiles. Records that do not resolve on the first pass are retried with expanded search strategies.
Every piece of information carries a confidence score based on the strength of the evidence supporting it. A LinkedIn profile that matches name, institution, and graduation year receives a higher confidence assignment than a partial match from a single source. Source attribution is maintained for every data point — enabling downstream verification of claims. The framework defines what level of evidence is required for different use cases: accreditation submissions might require high-confidence data, while internal planning can work with moderate confidence signals.
Individual profiles are the foundation. The output is aggregate insight: employment rates by programme and cohort, industry distribution across faculties, geographic dispersion of graduates, and employer concentration patterns. These aggregates are what accreditation bodies, rankings agencies, and institutional leadership need to see. The methodology ensures that every aggregate number is traceable back to the individual records and confidence scores that produced it.
Evidence Gap Assessment. Before building anything, the first step is to understand the current state: what the registry contains, where the gaps are, what confidence levels are achievable with existing data, and what sources are most productive for the specific cohort profiles. This ADA assessment produces a map of the data landscape — not a system, but a shared understanding of what is possible and where the effort should go.
The transformation from a registry record to a multi-dimensional profile. Below is a representative example based on actual data patterns, anonymised.
Individual profiles roll up into institutional-level insight. These numbers are simulated based on actual data patterns.
Data note: The profile above is a composite based on patterns observed across multiple records. All identifiers have been anonymised. Aggregate figures are simulated from actual data distributions and are indicative only. In production, every data point would carry source attribution and a confidence score.
The ADA Discovery Pipeline, Confidence Framework, and Institutional Intelligence outputs described in Parts 1-3 are delivered as a managed service. We operate the infrastructure, run the discovery campaigns, and maintain the evidence base. The institution provides registry data, reviews discovered profiles, and consumes the resulting intelligence. This model places operational responsibility with the party best positioned to carry it — and keeps the institution's commitment focused on outcomes, not infrastructure.
| Milestone | Amount |
|---|---|
| NDA execution and engagement letter | 25% of setup — RM 12,500 |
| Registry imported, pilot operational | 75% of setup — RM 37,500 |
| Monthly subscription | RM 8,000 / month from Month 1 |