Home▸AI & Machine Learning▸Building the Data Foundation for Machine Learning: A Data Quality Score Framework

📊 Building the Data Foundation for Machine Learning

A 3D data-quality pipeline: raw records flow through five quality gates — completeness, accuracy, consistency, timeliness and uniqueness — and settle into bronze, silver or gold storage tiers based on their composite score.

AI & Machine Learning3DAdvanced60 FPS

Raw records leave a data lake and pass through five live quality gates — completeness, accuracy, consistency, timeliness and uniqueness — before settling into bronze, silver or gold storage tiers based on their composite Data Quality Score.

🔬 What It Demonstrates

Each record carries five independent quality dimensions that combine into a single Data Quality Score. Gates flash green or red per dimension as records pass, and the final score decides whether a record is curated (gold), merely validated (silver), or quarantined.

🎮 How to Use

Raise the defect rate to simulate messier source systems, adjust the ingestion rate, and move the gold threshold to see how strict quality bars change the split between bronze, silver, gold and quarantine.

💡 Did You Know?

Bronze/silver/gold is a common tiered data-lake pattern: bronze holds raw ingested data, silver holds cleaned and validated records, and gold holds curated, ML-ready datasets — exactly the pipeline this scene visualises.

⚙ Under the hood

machine learningdata qualitydata pipelinebronze tiersilver tiergold tierThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install