data integrity
The WHO guideline on Good Data and Record Management practices is now final, below you find the link. Particularly interesting is that this guideline talks about quality risk management:
Quality risk management and sound scientific principles. Robust decision making requires appropriate quality and risk management systems, and adherence to sound scientific and statistical principles, which must be based upon reliable data. For example, the scientific principle of being an objective, unbiased observer regarding the outcome of a sample analysis requires that suspect results be investigated and rejected from the reported results only if they are clearly attributable to an identified cause. Adhering to good data and record-keeping principles requires that any rejected results be recorded, together with a documented justification for their rejection, and that this documentation is subject to review and retention.
And later it advises to train employees what is good data and data integrity:

8. Training in good data and record management
8.1 Personnel should be trained in data integrity policies and agree to abide by them. Management should ensure that personnel are trained to understand and distinguish between proper and improper conduct, including deliberate falsification, and should be made aware of the potential consequences.
In order to have a quality risk management system and be able to train employees, it is, obviously, vital to integrate a platform, which evaluates, controls and reports risks & data integrity.