Start with the decision question.
A good analysis begins with what needs to be understood, proven, falsified, or decided.
Business analyst · Civic technology · NYC public systems
Data forpublicsystemsthat work.
I analyze public data, build dashboards, and surface the operational patterns behind city services. My work turns missing context into clearer evidence, better questions, and decisions people can actually use.
Analyze → Diagnose → Visualize → ImproveScroll to explore ↓
01 / WORLDVIEW
Public systems are data systems.
Budgets. Utilities. Housing. Family services. Operations. Every public system produces signals about what is working, what is breaking, and who is carrying the cost.
Information is abundant. Understanding is not. The work is turning messy administrative data into clear evidence that helps agencies see patterns, ask better questions, and make better decisions.
City systems are not abstract. They shape daily life, neighborhood stability, and whether public service feels navigable or opaque. Good analysis restores the context people need to act with confidence.
01Clean the data
02Find patterns
03Explain impact
04Build dashboards
05Improve decisions
02 / ABOUT
Data analysis for civic operations.
I’m Jonathan Martinez, a Native New Yorker whose work sits at the intersection of public systems, data analysis, dashboards, and systems thinking.
I’m especially interested in NYC government because city agencies hold the data, constraints, and responsibility of real-world operations. I want to help turn that data into analysis that is clear, usable, and connected to decisions.
03 / PRINCIPLES
How I think.
Context precedes answers.
Before solving a problem, understand the agency, population, process, and constraint environment that produced it.
Evidence beats output.
Dashboards and notebooks should make conclusions defensible, not just visually impressive.
Validate before interpreting.
Row counts, filters, missingness, and caveats are part of the argument—not cleanup chores.
AI should structure judgment.
My AI workflow helps plan, review, and pressure-test analysis while keeping human responsibility at the center.
Make the work reusable.
The best analysis becomes a method others can rerun, audit, adapt, and trust.
04 / METHOD
The system behind the analysis.
My dashboards and notebooks are produced through a repeatable AI-assisted analyst workflow: plan the argument, structure the notebook, handle large data safely, convert evidence into memos, and review the work before publishing.
Analyst operating system
Thesis-First Planning
Every notebook starts with a claim, decision question, required data, metrics, validation checks, and risks before code begins to sprawl.
PlanningMemory-Safe Data Work
Large local files are handled through scoped columns, chunked reads, early aggregation, deterministic samples, and visible scan validation.
ExecutionEvidence Memos
Raw notebooks become concise artifacts that show the conclusion, why it is credible, how it was produced, and where the limits are.
CommunicationNotebook Review
Before sharing, I pressure-test execution order, filters, evidence, charts, caveats, reproducibility, and whether the conclusion is supported.
Quality controlIndependent Research
Outside of my professional work, I maintain Studio 16 as a small research practice for AI, systems thinking, and organizational decision-making.
Related work ↗05 / EVIDENCE
Proof through artifacts.
These artifacts show how I think with data: dashboards, analytical notebooks, evidence memos, and public-data studies built to make complex systems easier to understand.
06 / WORK
Evidence of practice.
Selected dashboards and public-data analyses that demonstrate analytical judgment, civic context, and the ability to communicate findings clearly.
Business operations · JM The Creative
Revenue & Invoice KPIs
A proof-of-work dashboard for tracking client revenue, invoice status, and business performance signals.
Public systems · NYCHA
The Invisible Bill
A diagnostic study of utility spending, resident opportunity cost, and hidden public-housing burden.
Public systems · ACS
The Aging-Out Effect
NYC youth-system data examined across intake, retention, and the consequences of institutional boundaries.
07 / CONNECT
Business analysis for public systems that need clearer context.
I bring data analysis, dashboard development, requirements thinking, and a systems lens to civic technology and public sector work—especially where complex operations need evidence people can trust.