Data Governance & BI
for regulated industries.

What I do, in one line

I build audit-defensible data systems for compliance-heavy organizations — pharma, healthcare, supply chain — by translating between SQL, governance frameworks, and the boardroom.

The longer version

I work at the intersection where data integrity meets human communication. By training, I'm a data and BI analyst. By instinct, I'm a translator — between engineers and executives, between SQL schemas and business stakes, between English and Chinese, between what the numbers technically say and what people actually need to decide.

My path is unusual. A Bachelor's in English literature in China, then a Master's in Information Science at UW–Madison. Most people read those as opposites. I read them as the same craft: the discipline of saying exactly what is true, with care for how it lands.

That's the through-line in my work. SQL audit trails. Segregation of Duties frameworks. ERP metadata governance. Executive-facing Tableau dashboards. These look like different things — they're really one question viewed from different angles: how do you build a system whose claims you can actually defend?

I've practiced this inside IBM's enterprise ERP consulting practice, inside a 5-stakeholder logistics operation in New Jersey, and inside my own company — Lunarix Technologies LLC — where I build analytics platforms for healthcare, pharma, and supply chain clients.

How I work

I think of my craft as having two modes. One that listens. One that builds. They're not separate hats — they're two ends of the same hand.

The listening mode is where I spend more time than most data people do. The questions a CFO asks at 9pm that no dashboard answers. The thing a process owner is too tired to articulate. The gap between what a stakeholder requests and what they're actually protecting. Most data work fails here, not in the SQL.

The building mode is where I refuse shortcuts. Clean schemas. Traceable logic. Audit-ready documentation. The kind of system where six months later, when someone asks "where did this number come from?", the answer is one click away. I'm allergic to data work that can't survive scrutiny.

The first mode is why stakeholders trust me. The second is why the auditor signs off.

Right now

I'm focused on the data challenges facing New Jersey's pharma corridor — J&J, Merck, BMS, Sanofi. ESG performance tracking. Health equity gap analysis. GRC control frameworks. Problems where the data has to be right because the stakes are real — patient outcomes, regulatory exposure, board-level decisions.

In parallel, I'm preparing for the CISA certification (Certified Information Systems Auditor), and watching closely as AI systems enter the enterprise without the audit trails and controls we spent decades building for traditional software. That gap is going to define the next wave of enterprise risk. I want to be useful when it arrives — which is why I built TPRM Copilot, an AI-augmented third-party risk and controls testing engine that turns the audit-defensible patterns I've practiced into an agent pipeline. It lives at the intersection of the two things I care about: governance frameworks that survive scrutiny, and AI tooling that earns trust by showing its work.

Beyond the work

I read across systems I'm not formally trained in — Vedic astrology, energy work, contemplative traditions. Not as escape from rigor, but as a complement to it. Some of the best frameworks for understanding people, timing, and pattern weren't written in PDFs after 1950.

I keep a small, deliberate circle. I write in two languages. I'm building slowly and on purpose, because the work I'm interested in compounds, and short-term wins rarely do.

If we share a sense that clarity is care — that the most respectful thing you can do for someone is tell them the truth in a form they can use — we'll probably get along.