Maintaining data integrity.

The idea

Data integrity means keeping performance data accurate, complete and unaltered from the moment it is captured to the moment it is used. In sport, this covers GPS vests recording distance, heart-rate monitors logging intensity, and software storing event data. Maintaining integrity requires controlled collection, secure storage, restricted access and regular checks for missing or corrupted entries. If a coach uses faulty data, training decisions become unreliable. For example, a GPS unit that fails to record a session may understate a player's weekly load, causing the coach to over-train them. Integrity is strictly about the reliability and correctness of the data, ensuring that technological analysis leads to valid performance interventions.

What earns the marks

• Defines data integrity as the accuracy, completeness and consistency of performance data over its entire lifecycle.
• Identifies collection-stage threats to integrity, such as faulty sensors, calibration errors or manual entry mistakes.
• Explains storage and access controls, including secure servers and restricted permissions, to prevent unauthorised alteration of data.
• Describes verification methods such as cross-checking devices, reviewing logs and cleaning data before conducting performance analysis.
• Links integrity failures to negative consequences for coaching decisions, such as over-training an athlete due to incomplete load data.

Common mistakes and how to put them right

• Conflating data integrity with data privacy.

  Put it right: Integrity is about accuracy and completeness, whereas privacy and confidentiality are about who is allowed to see the data.

• Assuming all recorded data are automatically correct.

  Put it right: Sensors can fail or lose signal, so regular calibration and data cleaning are essential to maintain integrity.

• Ignoring the effect of missing data on analysis.

  Put it right: Incomplete data can distort load monitoring and selection decisions, leading to poor coaching interventions.

Exam tips

• In multiple-choice questions, eliminate options that confuse integrity with confidentiality, consent or data quantity.
• Look for the option that names a specific control for accuracy, such as calibration, secure storage or verification, rather than a vague benefit.
• Check whether the scenario describes a collection, storage or analysis problem, because each points to a different measure for maintaining data integrity.