Automated Data Intelligence Platform (ADIP) — Intelligence Layer
The ADIP Intelligence Lab is the core intelligence engine of the Automated Data Intelligence Portal (ADIP).
This repository is responsible for transforming clean, ingested data into actionable intelligence — insights, forecasts, and recommendations — through a modular, automated, and production-oriented analytics system. While the ingestion layer focuses on collecting and standardizing data, the Intelligence Lab focuses on understanding it.
This project is intentionally built as a learning-first, systems-engineering lab alongside real-world implementation.
The ADIP Intelligence Lab:
- Converts ingestion outputs into analytics-ready data models
- Engineers meaningful features from raw signals
- Detects trends, anomalies, and behavioral shifts automatically
- Forecasts future patterns using time-series models
- Generates human-readable insights and recommendations
- Runs autonomously through scheduled and CI/CD-driven workflows
In short:
Raw data → Structured understanding → Predictive insight → Automated narrative
The system is composed of six tightly integrated modules:
Standardizes schemas, enforces typing, normalizes time, and prepares analytics-ready tables.
Extracts signal from noise using rolling statistics, lag features, ratios, and domain-aware metrics.
Automatically detects trends, anomalies, correlations, and performance shifts.
Predicts future behavior using time-series models such as Prophet and ARIMA.
Translates analytics into human-readable summaries, alerts, and decision guidance.
Ensures the intelligence system runs autonomously via scheduled jobs and CI/CD workflows.
adip-intelligence-lab/
├── dis/
│ ├── models/ # Data schemas and analytical models
│ ├── validators/ # Schema enforcement and data validation
│ ├── features/ # Feature engineering logic
│ ├── insights/ # Pattern detection and analytics
│ ├── forecasts/ # Time-series forecasting models
│ └── narratives/ # Insight summaries and recommendations
├── tests/ # Unit and integration tests
├── docs/ # Design notes and learning documentation
├── workflows/ # CI/CD and automation logic
└── README.md
This repository is not only a production system but also a technical growth artifact.
It is designed to:
- Build deep intuition in data modeling and analytics
- Develop systems-level thinking for intelligence pipelines
- Demonstrate end-to-end automation capability
- Serve as a portfolio-grade example of intelligent data infrastructure
🚧 Phase 2 — Data Intelligence Service (Active Development)
Modules are implemented iteratively and integrated continuously with the ADIP ingestion layer.
Charles Onokohwomo
Built and maintained as part of the Automated Data Intelligence Portal (ADIP) initiative.