Special Forces Selection 

Identification of performance 

To focus efforts and keep costs down, military organisations need to identify high and low-performing candidates at an early stage, however, traditional methods make this challenging. 

Ensuring fairness and reducing bias 

A fair and consistent process is essential to ensure candidates are treated equally and the best performers are selected, however, manual processes are susceptible to bias and subjectivity and subjective decisions over time can introduce ‘drift’ in selection decisions. 

Data collection and analysis 

Data from different stages of the selection process is often siloed in different systems making it difficult to work with common, updated data, track candidate’s progress effectively, weigh up different results, or perform trend analysis. 

Resource-intensive preparation 

The preparation, management, and analysis of selection courses is resource-heavy, involving extensive paperwork, manual scoring, and data input, which is not easily accessible by all stakeholders at any given time. 

Capturing performance offline 

Selection often occurs in environments where network connectivity is unreliable, relying on observers to log performance without bias post event, which greatly complicates the data capture process.