ARMY AL&T
encryption protocols and the availabil- ity of secure communication methods.
• Information specific to the mission at hand, such as target information, mission objectives, rules of engage- ment and operational plans. This data is crucial for mission planning and execution.
Data may need to take more than one path (more on this in the third article on data transport)—the path may be dictated by the demand. For example, there is a need for the most recent, authoritative data to be available in a data platform to be consumed by other systems. However, there may also be a need for raw histor- ical data to train new or retrain existing data models. Finally, data may also need to go directly from system to system for time-or safety-critical uses. In many cases, data will be needed not just for one of these uses, but two or three uses, requir- ing simultaneous transport based on need.
DATA MESH A data fabric—a centralized, federated collection of common data provided and consumed by multiple systems—is a useful platform for a particular domain, organization or geographic area, but becomes unwieldy at larger scales. Te Unified Data Reference Architecture (UDRA), promulgated by the deputy assistant secretary of the Army for data, engineering and software (DASA(DES)), is based on a concept called “data mesh,” where the data is decentralized, but infor- mation about data products is cataloged centrally so that users can discover data across the enterprise. Te Department of Defense is also considering a data mesh that will expand that discoverability across the services.
When a user finds the data product they need in the catalog, they can request or subscribe to that data product from its
source system or domain. Te demand is thus shifted from a “push” model, where data is synchronized across platforms for maximum availability, to a “pull” model, where data is consumed on demand.
SCALING TO DEMAND Demand for data and information fluctu- ates depending on the level of activity, the rate of change, or the potential for change on the battlefield. Tis variable demand requires dynamic resources that can scale up and down accordingly. For example, compute resources for creating or updating data models to support decision-making, or decision aids that kick in to help a user when physiological sensors indicate high cognitive load.
The ability to dynamically allocate resources based on the fluctuating demand for tactical data and information is essen- tial in ensuring that decision-makers and operators have access to the neces- sary support when they need it most. By leveraging scalable compute resources and implementing intelligent decision aids, military organizations can optimize the utilization of data and enhance the over- all effectiveness and efficiency of their operations.
However, it is important to note that implementing dynamic resource allocation in data logistics comes with its own set of challenges. It requires the establishment of flexible infrastructure, robust networking capabilities and advanced algorithms for resource management. Moreover, consid- erations such as data security, privacy and interoperability must be carefully addressed to ensure the seamless integra- tion and exchange of information across different systems and platforms.
CONCLUSION Te demand signal in logistics provides key information about consumption and
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replacement rates. Data may not be consid- ered a consumable item; however, data is perishable, and the demand signal could provide clues about how frequently data may need to be collected to meet usage needs. Like ammunition, when the oper- ations tempo is high, the need for data to inform a quickened pace of decisions will also increase.
Adopting logistics principles for data management is crucial in the modern battlefield. Te demand signal drives the timely delivery of information, enabling informed decision-making and adapt- ability. Te DIKW hierarchy provides a framework for transforming raw data into actionable insights. Embracing concepts like a data mesh can further enhance the efficiency and availability of data across the enterprise. By recognizing the importance of data logistics, military organizations can optimize their operations and gain a competitive edge in the digital age of warfare.
In the next article, we’ll discuss another aspect of relating data to logistics—inven- tory and warehousing.
For more information, contact Thom Hawkins at
jeffrey.t.hawkins10.civ@
army.mil.
THOM HAWKINS is a project officer for artificial intelligence and data strategy with Project Manager Mission Command, assigned to the Program Executive Office for Command, Control and Communications – Tactical, at Aberdeen Proving Ground, Maryland. He holds an M.S. in library and information science from Drexel University and a B.A. in English from Washington College.
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