Data

Table of contents

• Purpose
• Data contracts
• Schema and validation
• Artifacts
• Lineage
• Versioning
• Privacy and governance
• Related guides

Purpose

The data layer provides reproducible, validated inputs for retention analysis, modeling, monitoring, and dashboard evidence.

Data contracts

Each dataset should document:

source
grain
primary identifiers
target definition
feature window
observation window
allowed nullability
expected types
refresh behavior

Schema and validation

Validation should cover:

required columns
types
ranges
category domains
duplicate keys
missingness
temporal consistency
target leakage

Artifacts

Versioned analytical artifacts should be reproducible from code and source data.

Avoid committing sensitive raw data.

Lineage

Every dashboard result should be traceable to:

dataset version
transformation
model version
evaluation version
explanation evidence

Versioning

Use immutable dataset and artifact identifiers whenever practical.

Monitoring and retraining workflows must know which data version produced a model or report.

Privacy and governance

Retention data may involve high-impact employment context. Apply data minimization, access control, appropriate retention periods, and human review.

Related guides

• EDA
• Modeling
• MLOps
• Documentation index