Air base data is often flawed because mechanics are rushed and use slang and abbreviations when logging maintenance actions, which lead to errors in ~60% of Air Force Maintenance Data Logs. The DoD only corrects logs down to the system level, and much of the process is manual and time intensive. This leads to errors and incorrect aircraft system fixes, which costs lives and money and decreases mission readiness. Incorrect coding of maintenance data leads to millions of dollars in unnecessary spend and a reduction of aircraft sorties and mission readiness. The current DoD data fidelity processes are either non-existent or entirely manual; automating data fidelity will greatly streamline all Air Force maintenance processes. The USAF requires more rapid, automated data processing supported by machine learning and big-data analytics to improve data integrity to support better maintenance intel. Aermetric simplifies, automates, and streamlines data fidelity processes through its proprietary data modeling techniques. Aermertric seeks to expand on its pilot of 870,000 data logs to understand how to improve its data fidelity algorithms to best clean Air Force maintenance logs and deploy future data-fidelity processes.
