LOGISTICS FOR DATA
Once the network is restored in a battle- field environment, establishing priorities for data updates becomes crucial. Not all data must be synchronized simultaneously, as certain information is more time-sensi- tive or critical than others. Clear priorities ensure that the most important data is updated first, enabling effective decision- making and operational coordination. For example, critical data might include data directly relevant to ongoing opera- tions, situational awareness and imminent threats. Tactical intelligence is important but not as immediate as imminent threats. Finally, less time-sensitive historical data, such as archives, past mission records and lessons learned, can be updated after synchronizing more critical data.
By establishing clear priorities for data updates, battlefield commanders can ensure that the most critical information is available right after network restoration.
Tis facilitates swift decision-making and effective response in dynamic and rapidly changing environments.
PROVENANCE AND MAINTENANCE As new data sources become available, so must methods to track data from those sources. Machine learning uses known data points to build a model that predicts data within the model’s parameters. For example, knowing where munitions fell concerning their target under certain conditions can help predict where muni- tions will fall under similar conditions, and can be corrected to hit the target more precisely. Because data is only as trustworthy as its source, data provenance is necessary to ensure information secu- rity. Tis has always been important, but with the growth of data and increasing complexity of models, trust now borders on faith that the output is correct.
An enemy could subtly inject false data that evades notice but impacts the result, such as targeting coordinates. Attackers inherently have the advantage of need- ing only a single vulnerability and a few moments to breach a system that defend- ers must always be ready to protect.
Te level of certainty in data, derived from its source, may not appear in the output but is a consideration for how we use that output. Just as we would likely avoid a medication that causes death in 30% of the people who take it, we should simi- larly reject a targeting system with only 70% accuracy. Fusing data with varying degrees of certainty can be a complicated business. However, confirming data with a second independent source can increase the certainty if it confirms the data from the original source.
Data is also often subject to time
constraints. You would avoid using a six- month-old weather report to decide what to wear outside because time-variable data eventually expires, so it must be regu- larly updated. Data also may need to be updated based on other parameters; for example, machine learning models are subject to drift over time and must be retrained when the drift reaches a partic- ular threshold. As with fielded hardware, there must be a plan to maintain data to ensure its accuracy and quality.
DOUBLING DOWN ON DATA
A combination of technological solutions and strategic data-handling practices are necessary to ensure efficient, secure and reliable data flow. (Photo by Brett Sayles, Pexels)
CONCLUSION Tis series has demonstrated that concepts underpinning logistics for a hardware system—such as demand, storage and transportation—are also relevant to data. Not only should we think about data in these terms, but we should also plan for it. As data models increasingly serve as battle- field tools, there is an unanswered question about whether logistics for data would fall under sustainment or operations and maintenance from a funding perspective
70 Army AL&T Magazine Spring 2024
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