Case Study
Women's Health Data Dashboard
A data-driven analytics platform for exploring trends in public women's health datasets through structured ingestion, SQL queries, and interactive dashboard views.
Role
Solo Engineer
Status
In Progress
Focus
Health Data Systems
Type
Analytics Dashboard
Overview
What this project is
This project explores how public health datasets can be transformed into a structured analytics workflow. It combines ingestion, normalization, SQL querying, and dashboard presentation to make healthcare-related trends easier to inspect.
Problem
Why this project exists
Public datasets often arrive in raw formats that are difficult to query, compare, or interpret directly. The goal was to build a system that could make that data more usable without overstating what the data can actually support.
Goals
Project goals
- • Normalize selected health dataset fields into a structured schema
- • Build a dashboard interface for querying trends
- • Document assumptions and data limitations clearly
- • Keep the workflow reproducible and maintainable
System
System architecture
The project separates ingestion, storage, query logic, and presentation so the workflow remains easier to debug, extend, and reason about.
Raw dataset
↓
Ingestion script
↓
Validation / normalization
↓
PostgreSQL
↓
API routes
↓
Dashboard UI
Stack
Technology used
- • TypeScript
- • Next.js
- • Node.js
- • PostgreSQL
- • SQL
- • Tailwind CSS
Features
Key capabilities
- • Dataset ingestion and transformation
- • Structured relational schema
- • Aggregate SQL queries for trend analysis
- • Filterable dashboard views
- • Documentation of assumptions and limitations
Technical Decisions
Important implementation choices
Why PostgreSQL
PostgreSQL made sense for structured relational queries, aggregation, and extensibility around normalized health records.
Why Next.js
Next.js allowed the dashboard UI and API routes to live in one codebase, keeping the project simpler while it evolves.
Why explicit documentation
Health datasets often contain incomplete or inconsistent fields, so documenting assumptions was necessary to keep the analysis honest and reproducible.
Constraints
Challenges and limitations
A central challenge was deciding how much normalization to perform without over-interpreting ambiguous source fields. To handle this, raw values were preserved where needed and transformation assumptions were documented explicitly.
Outcome
What this project demonstrates
The current version establishes a maintainable ingestion-to-dashboard pattern for analytics projects. It demonstrates structured data modeling, systems thinking, and responsible handling of imperfect datasets.
Next Steps
Future improvements
- • Add comparative filtering across conditions and regions
- • Expand documentation for schema and query design
- • Add more datasets for broader trend exploration
- • Refine dashboard views and exportable summaries