Global Garment Data Lake, Warehouse & Analytics Engine (GG-Data)
The data heart of NOSK Life — collecting, storing, structuring, and powering all factory data, intelligence outputs, predictions, KPIs, logs, and dashboards across the entire ecosystem.
Purpose
Goal
Create a global garment data infrastructure that collects, stores, structures, cleans, normalizes, and indexes all data — powering every intelligence engine, dashboard, and prediction across NOSK Life.
Capabilities
GG-Data Architecture
A. Global Data Lake
Stores all raw factory and ecosystem data.
- Raw production data
- Raw quality data
- Raw IE data
- Raw cutting data
- Raw washing data
- Raw finishing data
- Raw packing data
- Raw testing data
- Raw compliance data
- Raw sustainability data
- Raw merchandising data
- Raw supply chain data
- Raw digital twin data
- Raw buyer requirement data
- Raw operator/machine data
B. Global Data Warehouse
Stores cleaned, structured, and aggregated data.
- Cleaned data
- Structured data
- Indexed data
- Aggregated data
- Historical data
- KPI data
- Prediction data
- Risk data
- Intelligence outputs
C. Global Data Mart Layer
Provides department-specific data marts.
- Production data mart
- Quality data mart
- IE data mart
- Cutting data mart
- Washing data mart
- Finishing data mart
- Packing data mart
- Testing data mart
- Compliance data mart
- Sustainability data mart
- Merchandising data mart
- Supply chain data mart
- Digital twin data mart
- Buyer requirement data mart
D. Global Analytics Engine
Provides dashboards, reports, KPIs, and insights.
- Dashboards
- Reports
- KPIs
- Trends
- Patterns
- Insights
GG-Data Inputs
Factory Inputs
- All operational data
- All quality data
- All IE data
- All supply chain data
- All compliance data
- All sustainability data
- All buyer data
NOSK Life Inputs
- GGKI nodes
- Intelligence outputs
- Prediction outputs
- Risk outputs
- Semantic graph outputs
GG-Data Intelligence Layers
Layer 1 — Data Ingestion Engine
Ingests real-time, batch, API, and file data.
Layer 2 — Data Cleaning Engine
Cleans missing, incorrect, and duplicate values.
Layer 3 — Data Normalization Engine
Normalizes units, formats, and structures.
Layer 4 — Data Indexing Engine
Indexes operations, machines, lines, factories, and suppliers.
Layer 5 — Data Analytics Engine
Analyzes trends, patterns, deviations, and correlations.
Layer 6 — Data Prediction Engine
Predicts efficiency, output, defects, risks, and delivery.
Layer 7 — Data Visualization Engine
Visualizes dashboards, reports, and alerts.
Data Lake Model
Lake Example
Inputs
- Raw operator speed data
Output
- ResultStored in data lake
Data Warehouse Model
Warehouse Example
Inputs
- Cleaned data
- Structured data
Output
- Efficiency tableUpdated hourly
Data Mart Model
Mart Example
Inputs
- Warehouse data
Output
- Quality data martDefects, rework, buyer compliance
Analytics Engine Model
Analytics Example
Inputs
- Data marts
Output
- TrendDefects increasing on Line 7
Prediction Engine Model
Prediction Example
Inputs
- Historical + real-time data
Output
- Predicted output tomorrow+3%
Visualization Engine Model
Visualization Example
Inputs
- Analytics
- Predictions
Output
- AlertEfficiency drop predicted
GG-Data Dashboards
Dashboard Shows
- Data lake health
- Data warehouse health
- Data ingestion
- Data cleaning
- Data normalization
- Data indexing
- Data analytics
- Data predictions
Users
GG-Data Alerts
Critical Alerts
- Data ingestion failure
- Data corruption
- Data loss
High Alerts
- Missing data
- Incorrect data
Medium Alerts
- Slow ingestion
Low Alerts
- Minor inconsistencies