Engineering-first data validation and structuring baselines for integrity and risk-based inspection decision support.
This repository focuses on the part most teams underestimate: making engineering data consistent, traceable, and defensible before adding advanced analytics or machine learning. The goal is not to produce impressive outputs, but to build a reliable foundation where assumptions are explicit and failure modes are visible.
A set of lightweight Python baselines to:
- validate and normalize integrity datasets
- enforce engineering-aware data contracts
- expose inconsistencies early (instead of hiding them)
- produce structured outputs ready for downstream decision workflows
This is not a production product, not a client deliverable, and not a benchmark repository. No client data is included.
In integrity programs, poor data does not only reduce accuracy. It changes decisions. A clean, structured, and auditable dataset is often the largest lever for ROI because it reduces rework, shortens decision cycles, and increases trust in outputs.
This repo is designed around:
- explicit assumptions
- transparent validation logic
- deterministic outputs
- security-aware handling of data artifacts
Create and activate a virtual environment, install dependencies, and run the example pipeline on sample data.
Windows: python -m venv .venv . .venv\Scripts\activate pip install -r requirements.txt python -m integrity_data_foundation.pipeline --input data/sample --output out
macOS/Linux: python -m venv .venv source .venv/bin/activate pip install -r requirements.txt python -m integrity_data_foundation.pipeline --input data/sample --output out
The pipeline produces:
- normalized datasets
- validation reports (what failed, why, where)
- a structured export for downstream decision workflows
Treat engineering data as decision-critical. This repository includes guidance to avoid leaking sensitive information and to keep data lineage and integrity checks visible.
MIT