In order to ensure that our Warfighters are appropriately trained for the next fight, an effective data management strategy built around identifying, integrating, analyzing, and understanding relevant data is critical to success. There is a growing collection of real time and historical information that is relevant to understand how we’re training to fight, including data generated during training events, empirical data that helps inform and structure training scenarios, data pertaining to individual soldiers, data from training equipment and systems that track soldiers, and many others.
Our proposed study will incorporate experience from a variety of Government and commercial customer spaces in how advances in commercial industry around data management technology can be leveraged, including efficient data fusion, Artificial Intelligence (AI) and Machine Learning (ML) strategies to detect and identify patterns, and algorithmic approaches to optimize the prediction of future performance. Our study will also explore common challenges in utilizing such tools in developing appropriate data management strategies, including:
- Integrating data of different types and formats from a variety of disparate sources
- Applying novel AI/ML, and algorithmic techniques to data analysis and the execution of operational workflows
- Providing accessible tools and user interfaces that allow non-technical users to benefit from advanced analytics
- Enabling collaboration across security, organizational, and even national boundaries
The results of these case studies, to be presented in our paper, will provide courses of action for implementing data management technology. Such capabilities will enable stakeholders from across the training enterprise – from senior decision makers to individual soldiers – to better evaluate unit and soldier readiness and the effectiveness of ongoing training.