Why this decision matters
Algorithm projects need traceable, lawfully usable data that represents target conditions and supports separate training, validation and independent testing.
Preparation is more than volume: document collection conditions, class and exception coverage, labeling rules, quality checks, versions, permissions and test isolation.
Conditions to confirm before development
- Document source, usage rights, sensitive information and retention
- Cover normal, difficult, rare and failure conditions
- Define labels, conflict resolution and sampling checks
- Separate training, validation and test sets with versions and deduplication
Implementation and delivery approach
Use an inventory and sample review to find gaps before deciding on collection, cleaning, labeling or target adjustment.
Retain data inventory, field or class definitions, sample statistics, labeling guidance, quality checks and version hashes or identifiers.
Acceptance boundary
Large volume does not guarantee representative coverage. The client confirms lawful use and business meaning; the technical team follows the agreed handling controls.