Data Aggregation & Unified Dashboard Concept
Fragmented data across multiple platforms was slowing decision-making. I designed and prototyped a unified dashboard concept to consolidate data from Airtable, web sources, and spreadsheets into a single coherent view — demonstrating the architectural thinking behind modern data aggregation pipelines.
Data existed in silos: some in Airtable, some scraped from web sources, some maintained in Google Sheets. Pulling a coherent view required manual cross-referencing across multiple tools — slow, error-prone, and not scalable. Every decision was delayed by data assembly rather than data analysis.
Designed the aggregation architecture first: identify all source systems, map their data schemas, define the unified output structure. Then built Python scripts to extract data from each source — using Selenium for web-based sources that lacked APIs. Prototyped a consolidated dashboard to surface the aggregated data in a single, navigable view.
Python · Selenium · Airtable API · Google Sheets integration · Dashboard prototyping
Source Systems → Extraction Layer (Python/Selenium) → Transformation → Unified Data Store → Dashboard Output
Demonstrated strong systems thinking on data architecture. Proved the feasibility of the aggregation approach. Created a reusable template for future data consolidation projects. The concept directly influenced how operational data was later structured for executive reporting.
Fragmented data is not just an inconvenience — it is a decision tax. Every time someone has to manually assemble a view before they can analyse it, that is time and accuracy being lost. Unified data architecture is an investment that pays back on every decision that follows.