Proof point → My first government engagement started the same way every good system starts: by modeling the citizen correctly.

🩻 Problem

Social-services workflows like adoption run on paper files and tribal knowledge. Digitizing them starts with a deceptively hard question: what, exactly, is a citizen record in a Nigerian government context - and what must be verified before the state acts on it?

🔨 Solution

In June 2021, working in the context of the Lagos State Government Ministry of Youth & Social Development, I produced the initial UML domain model for an adoption-services system:

  • A Nigeria-contextual citizen entity - surname/firstname/othernames, sex, date of birth, place of birth (city/town/state), tribe, religion, marital status, nationality, role - with email_verified and phone_verified flags designed in from the start: a digital-identity-aware model, not a generic web-app user table.
  • A home-address entity with proper association multiplicities (a citizen lives at 0..1 registered address).
  • Role attributes separating applicants from case officers - the seed of a workflow permission model.

📜 Philosophy

Model the domain before writing the code - especially in government, where the data model is the policy. Verification flags belong in the schema because in public services, unverified data is a liability, not a record.

🎓 Key learnings

  • Public-sector requirements gathering: sensitive domains (adoption) demand localized demographic fields and explicit verification states that consumer apps never think about.
  • Government digitization is won or lost at the data-model stage, long before any portal is built.

📈 Output & impact

  • An initial citizen + address UML data model for adoption-services digitization, authored June 2021, preserved in version control.
  • Honest scope: this evidences early-stage design contribution toward a ministry workflow - not a delivered system. It marks the start of a public-sector thread that continues through the security and identity work that followed.

🌍 Why this matters

Platforms & Registries · Trust, Security & Compliance. Registries of people - by governments, fintechs, or marketplaces - succeed or fail on whether the record of a person is modeled right: verified contact, localized fields, role separation. I have been doing that modeling since 2021, and every system since (Lura Identity, The Investor Directory) has deepened the practice.

🚀 Hire me

Digitizing a workflow that touches real people’s records? Let’s talk → · See also: Lura Identity · The thesis