ScentMetrics

ChimStat • Data & BI Audit

DATA CONSOLIDATION • MACHINE LEARNING PREDICTION

REFERENCE : DATA-2026-ST-901

I. Critical Data Flow Diagnosis

Integrity

62%

Latency

4.2h

Duplicates

14%

IoT Quality

92%

The diagnosis reveals major fragmentation between sensor data and manual Excel reports (source of 85% of integrity errors).

II. Structuring Architecture (Pipeline)

// Deep-Clean Processing Steps

1. INGEST_RAW(Sources) -> Data Lake [S3]

2. CLEAN_OUTLIERS(Z-Score > 3.0) -> Deduplication

3. NORMALIZE_UNITS(PPM to mg/m3) -> TimeSync[UTC]

4. IMPUTE_MISSING(Random Forest) -> Curated_Zone

// Result: ML-ready data

III. Transformation Roadmap (6 to 12 Months)

1

Months 1-4 : Foundation Consolidation

Setup of unified Data Warehouse and elimination of Excel silos. API security audit.

2

Months 4-8 : Automation & Mobile BI

Deployment of PowerBI/Tableau dashboards with real-time alerts for field teams.

3

Months 8-12 : Predictive Intelligence

Launch of Machine Learning models for predictive maintenance and energy cost optimization.