31. März 2026

Many preclinical studies fail not because of flawed biology, but because of compromised data quality. Even well-designed experiments lose regulatory value if data cannot be verified, traced, or audited.

Regulatory decisions depend on data that is not only accurate—but reliable.

Data integrity ensures that results are complete, consistent, and attributable. This requires clear documentation of how data is generated, processed, and stored, allowing every step to be traced and verified.

Without this foundation, results cannot be independently validated.

Failures often arise when data is handled manually, increasing the risk of transcription errors. Missing audit trails make it impossible to track how results were generated or modified. Inconsistent data processing methods can introduce variability that is unrelated to biology, while the absence of version control creates uncertainty about which dataset is final.

These issues create doubt—even when the experimental work itself is sound.

Regulatory frameworks such as GLP and ALCOA+ require that data records are traceable, time-stamped, and attributable to specific actions. Workflows must be controlled and standardized, and all data handling steps must remain fully transparent.

Data that cannot be audited is typically considered unreliable.

Valid experiments are not enough.

Only valid data creates evidence.

Without data integrity, results lose regulatory relevance.