Practical Considerations for Enterprise Level Analysis of Training Data
(Room S310AB)
28 Nov 18
10:30 AM
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12:00 PM
Speaker(s):
Liz Gehr, Chief Learning Scientist, Training & Professional Services, The Boeing Company; Hank Phillips, Operational Psychology Department Head, Naval Aerospace Medical Institute; Matthew Proetsch, Lead Data Scientist, Naval Enterprise Research Data Science Team (NERDS.ai), Naval Air Warfare Center Training Systems Division; Don Salamon, Deputy Assistant Program Manager for Systems Engineering, Naval Air Systems Command PMA-265; Trisha Shields, Data Science and Analytics Community of Practice Lead, Naval Surface Warfare Center, Carderock Division; Joe Incorvia, Director of Command Operations, Naval Air Systems Command AIR-00X
Enterprise Data Analytics (EDA) has revolutionized the way industry stores, accesses, examines, and maintains its data, as well as the level of insights we routinely expect those data to yield, and the time required to refresh the data and information displayed. EDA has changed how we build and resource our analytic teams, including skillsets and level of collaboration required. Industries across the spectrum now rely on frequently open-source tools to manage and analyze data stacks the size of which would have been unheard of only a few years ago. In keeping with this year’s theme, Launching Innovation in Learning: Ready, Set, Disrupt, this panel will provide an examination of practical considerations and important lessons in the use of EDA to enable insight relevant to training. The panel will feature speakers with experience in solving applied problems for the Naval Aviation Enterprise, addressing topics including:
• Building, staffing, managing, and resourcing EDA teams to ensure all capability requirements are met
• Managing program management level requirements, expectations, and understanding about what information can be gleaned from data stacks
• Using development languages and data analytic and visualization tools to conduct meaningful analyses at the enterprise level
• Data engineering management (aka data cleaning) for data stacks and lakes, supporting real time currency for data updates
• Defining requirements for EDA training applications, including:
» Understanding what information is present in available data stacks
» Defining what successful training performance looks like
» Determining how data can be restructured and tagged to yield meaningful metrics
» Determining how to generate or access data on platforms, training devices, or classrooms that can now yield insight that it could not before the proliferation of EDA
Panelists will draw on their own experience and expertise in managing broad EDA applications and offer insight into how the