On Sunday, 26 October, the Defense Entrepreneurs Forum hosted an innovation competition sponsored by the United States Naval Institute. $5,000 in prizes were awarded after the eight contestants made their pitches. This is the third prize winner posted originally at the DEF Whiteboard.
THIRD PRIZE WINNER
Contestant: Dave Blair, US Air Force Officer
MoneyJet: Harnessing Big Data to Build Better Pilots
BLUF: ‘Moneyball’ for flying. Track flight recorder and simulator ‘Big Data’ throughout an aviator’s flying career. Structure and store these data so that aviators can continually improve their performance and maximize training efficiency for their students.
Problem:
High-fidelity data exists for flights and simulators in an aviator’s career. However, these data are not structured as ‘big data’ for training and proficiency – we track these statistics by airframe, and not aircrew, unless there is an incident. Therefore, we rely on flawed heuristics and self-fulfilling prophecies about ‘fit’ when we could be using rich data. Solution. Simple changes in data retrieval and storage make a ‘big data’ solution feasible. By making these datasets available to aircrew, individuals can observe their own trends and how they compare to their own and other flying populations. Instructors can tailor flights to student-specific needs. Commanders can identify ‘diamonds in the rough’ (good flyers with one or two key problems) who might otherwise be dismissed, and ‘hidden treasure’ (quiet flyers with excellent skills) who might otherwise be overlooked. Like in ‘Moneyball,’ the ability to build a winning team at minimum cost using stats is needed in this time of fiscal austerity.
Benefits:
Rich Data environment for objective assessments.
o Self-Improvement, Squadron Competitions, Counterbalance Halo/Horns effect
o Whole-force shaping, Global trend assessments, Optimize training syllabi
o Maximize by giving aircrew autonomy in configuring metrics.
Costs: Contingent on aircrew seeing program as a benefit or a burden.
o Logistics: Low implementation cost, data already exist, just need to re-structure.
o Culture: Potential high resistance if seen as ‘big brother’ rather than a tool.
o Minimize by treating as non-punitive ‘safety data’ not ‘checkride data’
Opportunities:
Partial foundation for training/ops/tactics rich data ecosystem.
o Build culture of ‘Tactical Sabermetrics’ – stats-smart organizational learning
o Amplify thru Weapons School use of force stats, large-n sim experiments
Risks
Over-reliance on statistics to the expense of traditional aircrew judgment
o If used for promotion, rankings, could lead to gaming & stats obsession
o Mitigate by ensuring good stats only replace bad stats, not judgment Implementation. First, we build a secure repository for all flight-performance-relevant data.
All data is structured by aviators, not airframes. This data is stored at the FOUO level for accessibility (w/secure annex for wartime data.) Second, we incorporate data retrieval and downloading into post-flight/sim maintenance checklists. Finally, we present data in an intuitive form, with metrics optimized to mission set. For individuals, we provide stats and percentiles for events such as touchdown point/speed, fuel burn, and WEZ positioning. For groups, we provide trend data and cross-unit comparison with anonymized names.