CONCEPT OF OPERATIONS: The Army is developing a concept of operations for decision-driven data to describe the transition path where systems and networks become secondary to the data they transmit and contain.
The demand signal for data: Getting battlefield data to the right place at the right time.
by Thom Hawkins
Part One of a three-part series.
If data is the new ammunition, as our leaders are fond of saying to emphasize the crucial role that data plays in modern warfare, then the concept of logistics should apply equally to both ammunition and data. The ability to collect, analyze and disseminate real-time information is crucial for military operations. Just as logistics ensures that physical resources such as ammunition are delivered efficiently, a similar approach must be taken for the movement of battlefield data.
Key concepts in logistics include measuring demand signal, inventory and warehousing, and packaging and transportation. Each of these concepts has a corollary in the data domain. This article will address the demand signal for data, and future articles will address inventory and warehousing of data, and then packaging and transportation of data.
THE DIKW HIERARCHY
Before delving into these concepts, it is important to understand the data, information, knowledge and wisdom (DIKW) hierarchy, and in particular the distinction between data and information. At the lowest level of the hierarchy, data refers to raw, unprocessed facts and figures collected from different sources. This can include sensor readings, images, videos, audio recordings and other forms of data. Information is derived from the analysis, processing and organization of raw data. It provides context, relevance and meaning to the data. Through data fusion techniques, such as correlation, aggregation and filtering, the information is synthesized to create a more coherent and meaningful representation of the operational environment. Knowledge is the result of synthesizing relevant information, identifying patterns, relationships and trends, and extracting actionable insights. Wisdom represents the highest level of the DIKW hierarchy. It goes beyond the immediate situational understanding and incorporates broader context, strategic thinking and long-term implications.
By linking and combining related elements, raw data is transformed into higher levels in the DIKW hierarchy, enabling commanders and operators to gain a holistic understanding of the battlefield, make well-informed decisions and take appropriate actions. Army Techniques Publication (ATP) 6-01.1, Techniques for Effective Knowledge Management, breaks this down as follows: “Staffs use processes to produce information from data and analyze and evaluate that information to produce knowledge. Staffs provide collective knowledge to commanders who apply experience and judgment to transform that knowledge into understanding.” (The Army uses “understanding” in place of the more nebulous “wisdom.”)
The Army is developing a concept of operations for decision-driven data to describe the transition path to a “data-centric Army,” where systems and networks become secondary to the data they transmit and contain.
PROCESSING THE INFORMATION: Information is derived from the analysis, processing and organization of raw data.
DEMAND SIGNAL
Identifying the demand for resources is key to the flow of logistics. Where are these resources needed, how many and how frequently? In the logistics of those resources, like ammunition and fuel, the term “just-in-time” refers to a delivery at the time of need, to balance resources against other demands and eliminate storage at the point of use. If ammunition is delivered too early, it can become a storage issue. Units at the tactical edge would have to move excess ammunition from place to place, making them a more vulnerable target.
In the context of battlefield operations, just-in-time data refers to the timely delivery of information to decision-makers and operators to ensure that commanders and personnel have access to the most up-to-date and relevant information, allowing them to adapt quickly to dynamic battlefield situations. Receiving information too late to make the decision to which the information is relevant is an obvious problem. If the information arrives too early, it can be outdated and lose its relevance as well. The frequency with which data is collected by sensors or other inputs is driven by the demand signal for information.
The decision point provides the demand signal for data. Do I attack or stand ground? Do I move this unit first or this one? Do I fire mortars or call for air support? Questions are answered with information, which is derived from data. Anticipating which questions will be asked helps to ensure that data is being collected and the processes are in place to refine that data into information—or answers.
This data or information may include:
- Real-time updates on enemy positions, friendly force locations, terrain conditions, weather data and other relevant information that helps build a comprehensive understanding of the operational environment.
- Tactical intelligence regarding enemy capabilities, intentions, vulnerabilities and potential threats. This includes information gathered from various sources, such as human intelligence, signals intelligence and imagery intelligence.
- Data related to logistics and supply chain management, including the availability of ammunition, fuel, medical supplies and other critical resources. This information ensures that the necessary resources are allocated efficiently to support ongoing operations.
- Information about communication networks and infrastructure, including the status of communication channels, encryption protocols and the availability of secure communication methods.
- Information specific to the mission at hand, such as target information, mission objectives, rules of engagement 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 historical 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, requiring 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. The 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 information about data products is cataloged centrally so that users can discover data across the enterprise. The Department of Defense is also considering a data mesh which 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. The 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.
DATA MESH: “Data mesh” is based on a concept where the data is decentralized, but information about data products is catalogued centrally so that users can discover data across the enterprise.
SCALING TO DEMAND
Demand for data and information fluctuates depending on the level of activity, the rate of change, or the potential for change on the battlefield. This 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 essential in ensuring that decision-makers and operators have access to the necessary 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 overall 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, considerations such as data security, privacy and interoperability must be carefully addressed to ensure the seamless integration and exchange of information across different systems and platforms.
CONCLUSION
The demand signal in logistics provides key information about consumption and replacement rates. Data may not be considered 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 operations 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. The demand signal drives the timely delivery of information, enabling informed decision-making and adaptability. The 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—inventory 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.
