The Data Lifecycle

Data Collection Strategies

3 min read Data & Business Intelligence

Design the pipeline that feeds your analytics with reliable, consistent data.

Learning objectives

Design consistent collection points
Manage schema changes safely
Validate data at the boundary

Consistent collection

Data is most reliable when it is captured at a single, well-defined point - such as an API or a standard event - rather than scattered through many ad-hoc forms and exports.

Schema evolution

Systems change, and so do their data structures. Version schemas, test migrations, and backfill carefully so historical analysis is not broken by a new column or renamed field.

ALTER TABLE events ADD COLUMN source VARCHAR(50) NOT NULL DEFAULT 'web';

Validate at the boundary

Reject bad data where it enters the pipeline. Validating at the edge - required fields, correct types, bounded values - keeps the warehouse clean and analysis trustworthy.

Key takeaways

Capture data at defined, consistent points.
Treat schema changes like code changes: versioned and tested.
Validate data when it enters, not when it breaks a report.