AI Model & Decision Center
Data quality monitoring across 18 enterprise sources — and how it drives AI confidence
Prototype model metrics — demonstration only.Data-quality scores and issues are synthetic.
Sources monitored
18
17 systems + manual field input
Average data-quality score
96.4/ 100
5 dimensions · weakest site feed
Open issues
6
2 high · 2 medium
Models with degraded inputs
10
confidence adjusted automatically
Data quality heatmap
Completeness · Accuracy · Timeliness · Consistency · Validity (%)
| Source | Completeness | Accuracy | Timeliness | Consistency | Validity | Score |
|---|---|---|---|---|---|---|
| ERP Transactional · 180k/day | 99.4 | 98.9 | 97.8 | 98.6 | 99.2 | 98.8 |
| Plantation Mgmt System Transactional · 62k/day | 96.1 | 95.8 | 86.4 | 94.2 | 96.9 | 93.9 |
| Mill System (MES) Transactional · 24k/day | 98.7 | 97.9 | 98.1 | 97.4 | 98.8 | 98.2 |
| SCADA / PLC Time-series · 41 M/day | 91.2 | 98.2 | 97.6 | 96.9 | 98.4 | 96.5 |
| IoT Sensors Time-series · 9.6 M/day | 95.8 | 96.4 | 98.9 | 95.1 | 97.2 | 96.7 |
| CMMS / EAM Transactional · 3.1k/day | 97.2 | 94.6 | 95.3 | 93.8 | 97.9 | 95.8 |
| GPS / Telematics Streaming · 2.2 M/day | 97.9 | 95.2 | 99.1 | 96.3 | 91.8 | 96.1 |
| Weighbridge Transactional · 84/day | 99.6 | 98.8 | 99.4 | 90.7 | 98.9 | 97.5 |
| Warehouse Transactional · 5.4k/day | 98.4 | 96.7 | 97.2 | 97.9 | 98.6 | 97.8 |
| Procurement Transactional · 1.2k/day | 98.1 | 97.5 | 96.8 | 95.9 | 98.2 | 97.3 |
| HR Transactional · 4.8k/day | 97.6 | 98.4 | 94.1 | 97.2 | 99.0 | 97.3 |
| Finance Transactional · 22k/day | 99.7 | 99.5 | 96.2 | 99.1 | 99.6 | 98.8 |
| Weather API External API · 38k/day | 98.8 | 94.9 | 99.5 | 97.8 | 99.1 | 98.0 |
| Satellite Imagery · 120 tiles/day | 88.6 | 95.7 | 92.4 | 96.8 | 98.2 | 94.3 |
| Drone Imagery · 35 flights/wk/day | 94.2 | 97.3 | 90.8 | 96.1 | 97.7 | 95.2 |
| CCTV Video · 14 streams/day | 96.9 | 93.8 | 99.2 | 97.4 | 98.5 | 97.2 |
| Laboratory Transactional · 640/day | 98.9 | 97.1 | 93.6 | 92.8 | 94.4 | 95.4 |
| Manual field input Mobile forms · 7.9k/day | 92.4 | 89.7 | 88.2 | 90.6 | 92.9 | 90.8 |
≥ 97 good 94–97 watch 90–94 degraded < 90 poor· Scores show the weakest site feed per source · click a row to simulate its impact
Data quality → AI confidence simulator
Move completeness for a source and watch dependent models react
SCADA / PLC completeness (today 91.2%)91.2%
60%100%
Asset Failure Prediction · 45% dependent89% →89%(+0.0)Recommend
OER Prediction & Optimization · 35% dependent87% →87%(+0.0)Recommend
Remaining Useful Life · 30% dependent78% →78%(+0.0)Warn
Impact
Confidence90%Improving SCADA / PLC to 91.2% lifts confidence for 3 models — Asset Failure Prediction gains the most.
Open data-quality issues
Owner-assigned, linked to the models they affect
Model × source dependency
Share of each model's feature importance coming from each source (%). Degraded sources propagate to these models' confidence.
ERP | Plantation Mgmt System | Mill System (MES) | SCADA / PLC | IoT Sensors | CMMS / EAM | GPS / Telematics | Weighbridge | Warehouse | Procurement | HR | Weather API | Satellite | Drone | CCTV | Laboratory | Manual field input | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OER Prediction & Optimization | 20 | 35 | 10 | 15 | 20 | ||||||||||||
| Asset Failure Prediction | 45 | 40 | 15 | ||||||||||||||
| Remaining Useful Life | 30 | 35 | 35 | ||||||||||||||
| Yield Prediction | 15 | 35 | 20 | 15 | 15 | ||||||||||||
| Harvest Forecasting & Scheduling | 45 | 20 | 15 | 20 | |||||||||||||
| Disease & Pest Risk | 15 | 25 | 30 | 30 | |||||||||||||
| Fire Risk | 25 | 35 | 40 | ||||||||||||||
| Weighbridge Anomaly Detection | 25 | 55 | 20 | ||||||||||||||
| Fuel Anomaly Detection | 25 | 60 | 15 | ||||||||||||||
| Harvest-to-Mill Dispatch Optimizer | 30 | 20 | 50 | ||||||||||||||
| Inventory Demand Forecast | 30 | 20 | 50 | ||||||||||||||
| Spare Parts Prediction | 35 | 45 | 20 | ||||||||||||||
| Safety Vision (PPE / Proximity) | 100 | ||||||||||||||||
| FFB Quality Grading (Vision) | 20 | 80 |
How data quality flows into decisions
Controls applied automatically when input quality degrades
| Confidence band | What the platform does | Example today |
|---|---|---|
| ≥ 0.80 | Recommendation raised with full explanation; normal approval workflow | ST-04 failure prediction (0.89) |
| 0.70 – 0.80 | Recommendation raised with data-quality warning and named data owner | Weighbridge anomaly (0.76) · duplicate records open |
| < 0.70 | No recommendation — routed to analyst for human review only | CPO price scenario (0.62) · scenario planning only |