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The Department of Defense (DOD) and its Service components are investing in advanced technologies to gain and maintain a competitive advantage over adversaries and pacing threats such as China and Russia. From automated sensor-shooter networks, artificial intelligence, and next-generation vehicles, helicopters, and weapons, as well as software factories and innovation centers, the breadth and depth of the DOD undertaking is growing by the day to meet the demands of advanced technological warfare.

Regardless of these advances, the DOD military advantage will come from an ability to understand the situation and then develop and execute courses of action faster than our adversaries. Critical to meeting this challenge is the DOD ability to collect, harness, and utilize data across these advanced platforms and systems. To do this, DOD must do two things. First, it must focus on data by embracing a data-first mindset, an aspect of organizational culture by which its members prioritize the use of data in their day-to-day operations, staffing actions, and decisionmaking. When faced with a question or challenge, organizations that possess a data-first mindset recognize that inventorying and comprehending relevant data lay the foundation for a timely, accurate, and effective solution.

Second, DOD and its Service components must take concrete steps to operationalize this mindset. A natural place to begin is with operational planning because it serves as the connective tissue between military strategy and tactics. It is where most data converges to achieve theater-level and strategic objectives across multiple domains. Operational planning is the vehicle (ways) that Service and joint force headquarters use to employ assets and resources (means) to achieve their assigned mission and objectives (ends). It often makes nebulous or abstract strategy documents in terms of geography and American blood and treasure. Similarly, formalizing the use of data during operational planning for the Joint Staff and the Services will make the required data-first cultural shift a reality.

Operational headquarters that adopt data-informed planning drive requirements for more data-centric tools, processes, integrations, and visualizations to dynamically capture critical information both up and down the chain of command. To formalize this use of data, operational headquarters must adopt and implement a data collection plan and associated products in their current operational planning processes. This is where current doctrine falls short. There is no formalized step, substep, template, or matrix that reflects the importance of the data-first mindset in operational planning. Planning doctrine should be updated to operationalize this mindset via a data collection template even before receipt of mission begins. This template and the associated substeps of template refinement, as well as building the data collection plan during mission analysis, will equip staff to understand the operational environment more effectively. It will also help them develop and execute courses of action faster and more accurately than the adversary.

The Joint Planning Process Is Antiquated

Operational planning is also a good place to start because traditional joint planning is antiquated and therefore ripe for the technological disruption that true operationalized data offers. Traditional planning typically involves hours of fact-finding and subjective expert analysis to gather the critical information for mission analysis and course of action (COA) development. For decades, antiquated methods and products such as PowerPoint slides and spreadsheets emailed among decisionmakers provided analog and static facts and figures that commanders and staff built their plans on and made decisions about. This is no longer necessary or viable. Traditional use of email and Microsoft Office products leaves commanders making critical decisions with stale information. The modern battlefield is simply too dynamic to allow commanders to make decisions with static data.

To that end, doctrinal advances for data, automation, and artificial intelligence within operational planning need to be addressed. The U.S. military will derive operational advantage from implementing real-time data that accelerates and enhances staff planning processes, improves battlefield situational awareness, builds decision support tools, and automates digital plans for a future mission command system. All these capabilities rely on foundational data operations. When teams and commanders adopt a data-first mindset and take steps to “get the data right,” the power of artificial intelligence can be truly unlocked for planning, decisionmaking, and execution.

Data-First for Operational Planning

Data-first military organizations eschew the old-school, hard-copy, blue-folder approach to staffing actions. Rather, commanders demand real-time visualizations and dashboards to make decisions. Data-first organizations place command emphasis and prioritization on data and data-sharing. Commanders not only make data maturity an organizational key task but also take ownership of the data and demand that subordinate commands and staff share it. In an operational headquarters, a data-first mindset means staff initially ask: What data do I need or want? Where does this data exist? How do I find this data? How often do I need this data updated? Can I automate this data from sources such as emails or Microsoft Office products or systems of records? It also means that roadblocks to retrieving this data are broken down via command emphasis, prioritization, and action.

As an example, an intelligence officer would identify the information needed to understand the operational environment. In a typical planning scenario, this would include data such as terrain, weather, and light levels; staff estimates of enemy capabilities and limitations; and enemy and friendly doctrinal or situational templates. Once the J2 (intelligence) has identified the required information, teams can ask themselves questions like: Where does this data reside? Is there a system of record or open-source database that I can pull this data from? How often does this data get updated, and how often do I need this data updated? This data-first approach allows teams to think about how to systematize the gathering of data from resources and platforms, such as Advana or a government repository of enemy doctrine, or even to identify data gaps that require and benefit from standardized field reporting—subsequently maturing them from the status quo of emailing antiquated requests for information and the associated products.

Figure 1. Receipt of Mission

Source: Field Manual 5-0, Planning and Orders Production (Washington, DC: Headquarters Department of the Army, May 2022), 5-4.

Figure 2. Sample Data Collection Template

Key: APIs: application program interfaces; NIPRNet: Non-Secure Internet Protocol Router Network; SEC: Security; TS: Top Secret.

Of note, skilled data operators who can help planners identify available data sources, build the required data pipelines, clean and merge the data, and even derive greater meaning from the data via various analytic techniques are a critical support function required for the data-first organization. These data operators should work side by side with the functional staff subject-matter experts. This cross-functional team approach ensures that data operators have adequate access to domain expertise essential to a relevant solution and are building the right infrastructure to the right data sources to answer the right questions at the right time. It also ensures that staff experts are considering data realities, structures, formats, and templates in their processes, questions, and products.

The Data Collection Plan

Formalizing and operationalizing the use of data during the planning process necessitates additional data-first planning products—the data collection plan along with its associated data collection template and matrix. Simply examining current operational planning doctrine highlights the lack of formal data employment in these processes. For example, there is no step, substep, product, annex, or appendix that discusses data, identifies data sources, highlights data requirements, or requests data automation anywhere in the joint planning process. Operational planning headquarters need to adopt and implement the creation of a data collection plan in their doctrinal planning processes as early as possible—even before receipt of mission. The data collection plan will become a fundamental tool for operational planning as its application can improve and even accelerate the planning process during receipt of mission, mission analysis, and COA development. Traditionally, during receipt of mission, planners use higher headquarters plans and inputs to update running estimates, conduct initial assessments, issue guidance, and publish a warning order (see figure 1).¹

Lacking an initial data picture to begin this step, staff typically gather tools, update running estimates, and issue the warning order using analog or traditional digital software products, to include email, whiteboards, word processors, slides, spreadsheets, and even individual or directorate-level shared drives. A data-first approach would modify that paradigm by beginning with a digital data collection template. The template would identify questions and data requirements; validate system-of-record data sources, previously built data pipelines, and application program interfaces; and even contain links to relevant data streams. The data collection template would serve as an additional input to receipt of mission, streamline staff updating of running estimates, and even be updated itself to serve as a key output of this step before undertaking mission analysis. The goal of the data collection template is to move commanders and staff from the traditional, unstructured emailed reporting for information to a more standardized, structured, formatted, and even automated approach to data-gathering. The data collection template will serve as the precursor to the data collection plan and data collection matrix described during mission analysis (see figure 2).

As teams approach mission analysis, commanders and staff attempt to better understand the problem, what must be done, at what time and location, and why it must be done.² At this stage, the drafted data collection template is now a key input along with the commander’s guidance and staff’s running estimates. In addition to including the data collection template as an input, the data collection plan itself should be drafted as a specific substep during the mission analysis phase.

The data collection plan is the data- first corollary to the information collection plan developed in substep nine of mission analysis (see figure 3). Staff design the information collection plan to answer questions necessary for further planning. The information collection process typically focuses on intelligence, surveillance, and reconnaissance assets and sets them in motion for use in COA development.³ The proposed data collection plan serves an identical role but should extend further than just intelligence data. The plan would identify information gaps in the data required versus data available while also attempting to source the associated authoritative data sources for all staff sections. It would also identify which systems and sensors are used to collect data about the operational environment and capture it if the unit has access to the data from certain systems or sensors. The data collection plan would also outline the required format and establish requirements for refresh rate of the data. The data collection plan should not be siloed in any staff section, such as the communications directorate. Rather, it should serve an accelerating role for each staff function. As such, it is fundamental to the planning process.

While the data collection plan is being developed, the data collection template input should go through refinement and augmentation, which transforms it into a data collection matrix. The resulting data collection plan and matrix should also be in its own appendix (included in annex C—Operations) at the completion of the planning process, given its fundamental contribution to operations and decision support. The data collection matrix streamlines and visualizes where, when, and how data applicable to the mission are gathered and updated so commanders can make better decisions.

The mission analysis stage has traditionally been the most time-consuming and labor-intensive step of the planning process, as it requires 18 substeps, including intelligence preparation of the battlefield, developing the initial information collection plan, and executing the mission analysis briefing.⁴ This is where the data collection template, data collection plan, and resulting data collection matrix can also assist. The addition of data operators and the data collection products will help planners maintain and automate their running estimates, review available assets, and develop information requirements.

During the review of the mission analysis substep that analyzes available assets and identifies resource shortfalls, the data collection template should be used to identify and refine authoritative data from systems of record. Additionally, the template should be used to highlight required additional reporting from the field on composition, disposition, strength, and readiness of forces available. With this information compiled into the data collection template, planners (with data operator support) can generate a “forces available” table through Global Force Management and readiness systems. By doing so, the template will aid in the next step of COA development as well as aug- ment future digital plans ingested into a mission command system.

Figure 3. Current Steps Within Army Mission Analysis

Source: Field Manual 5-0, Planning and Orders Production (Washington, DC: Headquarters Department of the Army, May 2022), 5-9.

Information gaps identified during mission analysis using the data collection template become information requirements, which, if significant enough, could be elevated to commander’s critical information requirements (CCIR). Since CCIR is fundamentally tied to commander decision points and serves as a mission-critical element, the method of collection, reporting, and communication to the commander should be well understood. As information requirements and decision points are identified through the planning process, planners should identify the data required to inform those decisions, where it would be collected from (system, organization, report, etc.), and how it would be visualized for the commander. This approach should be augmented by an automated data system of record to accelerate transmission of the data and provide this visualization. Using the recommended data-first approach, the planning staff can refine the initial data collection template to identify how the data required to answer the respective CCIR will be collected and incorporated into the data collection plan as well as used in a dynamic visual dashboard or CCIR decision support tool (see figure 4).

Figure 4. Commander’s Critical Information Requirements

Note: General CCIR criteria that planners must consider when proposing them to the commander for approval include the following: answering a CCIR must be a decision required of the commander, and not of the staff, and answering a CCIR must be critical to the success of the mission.

Source: Joint Publication 5-0, Joint Operation Planning (Washington, DC: The Joint Staff, August 11, 2011), IV-12.

(Baby) Steps to Automation

The overall goal during the initial steps of the planning process is for data to reach the commander in a more timely and efficient manner so that decisions can be made more quickly and with more fidelity than previously. To move from the planning status quo to an automated mission analysis template for the commander, teams and organizations (especially tactical organizations) should prioritize standardizing data collection. Standardization is key for data systems to produce accurate and consistent results. For example, teams can start small by standardizing data collection for routine tasks such as personnel status reports or logistical status reports.

Once teams have established standardized templates, the data operators can begin to build the infrastructure that connects the data sources to the automated data pipelines and systems that can build reports in a timely manner for planning purposes. Additionally, these clean, updated, and automated reports can be stored in a central repository for retrieval and use in future planning scenarios, especially as more advanced capabilities such as large language models come online.

Another effective-use case of standardized data templates is its application to risk assessment and risk management. Given that DD Form 2977, Deliberate Risk Assessment Worksheet, is already standardized, teams could use it as a catalyst for automated insights derived from larger data repositories.⁵ Traditional risk assessment uses expert judgment and subjective estimates from mission analysis to quantify and qualify the probability and consequence of an event causing harm to friendly forces or missions. Aggregating these standardized risk assessments in repositories at the appropriate operational level would allow for basic risk data automation and analytics, providing a more streamlined and comprehensive approach to risk management. Aggregated and standardized risk data would help military planners identify previously unforeseen challenges and devise novel risk-reduction measures. Lastly, these automated risk assessments could be more easily updated as planners develop multiple courses of action.

From taking this initial step to standardize and automate basic reports, organizations and their data operators could progress to the more complex task of creating live dashboards that link dynamic data streams to visual representations. This allows the commander and staff to truly see the operational environment, painting a holistic picture for decision-making. Processes such as intelligence preparation of the battlefield, joint intelligence preparation of the environment, or the commander’s decision support matrix are ripe elements for this technological evolution. For example, geographic information system data could be integrated with social, economic, and cultural data to create a comprehensive information operations environment model. This synthesized data would assist commanders and planners as they seek to understand and influence the interconnected political, military, economic, social, information, and infrastructure factors that affect the mission environment. However, none of these advanced tools would be possible without first getting the data right. The recommended data collection plan and its associated template and matrix will be key to this pursuit.

Figure 5. Traditional COA Development Stage for Army Planning

Source: Field Manual 5-0, Planning and Orders Production (Washington, DC: Headquarters Department of the Army, May 2022), 5-24.

Driving Data for COA Development

Developing the data collection plan correctly during receipt of mission and mission analysis would generate additional data-driven inputs to aid in COA development. The inputs to development should include the data collection matrix and forces available table, in addition to the augmented CCIR decision support matrix. These updated tools would aid in completing the substeps of relative combat power analysis and the array of forces. As the commander and staff begin to develop courses of action, they should continue to refine the CCIR decision support matrix and data collection matrix.

The first substep in traditional COA development employs relative combat power analysis. This analysis assesses tangible factors such as units by quantity and type, equipment types, numbers, as well as intangible factors such as morale. The newly recommended data-derived matrices and tools would provide more dynamic insight into this substep. Here, system-of-record data sources that were identified by the data collection matrix— such as the Integrated Personnel and Pay System–Army, the Global Combat Support System–Army, or the Chief Digital and Artificial Intelligence Office’s Advana platform—could generate a forces available table with a more comprehensive visual aid. It could give more insight into objective unit facts, capabilities, and limitations such as number of dead-lined vehicles or weapons systems, most recent qualification percentages, and unit end- strength. These factors, coupled with automation and frequent refresh rates from the data sources such as the Army’s unit status report, could help commanders and staff turn assumptions into facts and improve confidence in the courses of action developed (see figure 5).

The same concept applies to help generate options and array forces. With improved real-time data and understanding of friendly force capabilities, information requirements, and tentative decision points, planners could more confidently identify the decisive and shaping operations, brainstorm valid courses of action, and realistically array forces on a sketch or map.

New Data-Driven Outputs

At the conclusion of COA development, staff should be able to produce a decision point wire diagram, deployment concept appendix, and sustainment concept appendix rather than waiting for the orders production step. These new products could accelerate decision advantage for the commander with a clear trajectory of the course of action. The decision point wire diagram is the visualization of the key data required to inform the commander’s decision (see figure 6). As decision points are identified through the planning process, planners should identify the data required to inform those decisions, where it would be collected from (system, organization, report, etc.), and how it would be visualized for the commander. This product should be incorporated into annex C—Operations, appendix 3—Decision Support Tools, along with the data collection plan and matrix.

For larger joint plans, the deployment concept appendix should aid planners with understanding and even aid in the visualization of additional capabilities being introduced to the theater as well as existing theater capabilities required to complete the mission. It should capture requests for forces details, desired movement timelines, draft mission and task alignment, and projected duration of requirements. This data, captured in the deployment concept appendix, would be used to assist Global Force Management planners in building plans in the Joint Operational Planning and Execution System and model capability effects using tools like ORION. In concurrence with the deployment concept appendix, Global Force Management planners should also develop visualizations to depict the ideal force flow to support the operation as well as the actual force flow as they are approved to highlight any risks to the plan based on delays (see figure 7).

Lastly, the sustainment concept appendix would identify key locations and resources required to sustain the operation. Tools such as Advana’s nodal health application could be used to identify key logistics nodes to plan the buildup of forces prior to an operation, their sustainment during the operation, and the withdrawal of forces on completion. Class-of-supply data from Advana’s logistics data holdings could also be used to identify the status of the critical items for the operation and the items that should be expedited to sustain operations. These critical locations and items should be consolidated into the sustainment concept appendix visualization to support logistics planners’ understanding of the logistics enterprise.

Practical Challenges

Two practical challenges to implementing this data-first approach remain. The first is data validation. How do planners know that their pertinent data streams are accurate—and to what level of accuracy? While system-of-record data is assumed to be accurate, the tactical realities are often muddy and incomplete. While some levels of inaccuracy can be accounted for with prudent assumptions during deliberate planning, delayed or inaccurate data can be costly in crisis action planning events such as noncombatant evacuation operations. The best way to get ahead of this reality is through well-crafted data policies and standard operating procedures that account for the appropriate level of data fidelity and required refresh rate. The data collection template could also be used to task subordinate organizations with data reporting requirements outlining the required data, format, and frequency.

A second challenge is with data access and sharing. Planners frequently bemoan gaining access to a command’s various data sources. Combatant commands can have over 40 programs-of-record data sources used by component commands and subordinate organizations. In addition to simply knowing these data sources exist, planners not only need access to these sources; they also need the data streamed to a location with a proper technical ecosystem that allows real-time analytics and visualization. This challenge is only magnified with the addition of a command’s interagency and international allied partners. Identifying and integrating the associated authoritative data sources from these agencies and countries are key to building a truly robust operational plan and whole- of-government approach. In addition to the data access challenges, these data-sharing agreements are typically handicapped by well-founded security concerns. Solving this problem requires a robust data integration layer and allied data pipelines that could securely move data across security classifications and among partner agencies and countries. While this solution may be in the future, this should not prevent operational headquarters from adopting the required data-first cultural shift—especially in lower level Service component operational headquarters, where interagency and international planning cooperation is less of a concern.

Figure 6. Decision Point Wire Diagram

Figure 7. Traditional Course of Action Development Stage for Joint Planning

Source: Joint Publication 5-0, Joint Operation Planning (Washington, DC: The Joint Staff, August 11, 2011), IV-17.

Conclusion

These are just a few recommendations on how to begin integrating data and analytics tools into the planning process. By harnessing data requirements, sources, formats, and refresh rates into organized tables and matrices and transforming those tables into visualizations, teams can truly unlock the capabilities of automation for operational planning. Each of these recommendations allows planners to leverage existing data, define data capture requirements (reports), enable understanding, and accelerate decisionmaking. The visualizations built to understand the data during the planning process are also just as important during mission execution to monitor the operation and identify where assumptions are no longer valid.

There is historical precedent for these modernization efforts. Forty years ago, the Cold War with the Soviet Union witnessed similar research and development efforts. Those efforts gave rise to now-ubiquitous military technologies such as intercontinental ballistic missiles; the Bradley fighting vehicle; global positioning systems and precision strike, stealth, space-based communications; and the Apache helicopter. These new advancements were were doctrinally integrated and employed with the new AirLand Battle concept that saw its zenith during Operation Desert Storm in 1991. It was these technologies and associated doctrine that helped the Department of Defense maintain its military advantage during the Cold War.

Similarly, the mature use of data in planning would make the resulting plans more iterable, robust, and timely. These plans would be based on a more accurate picture of an increasingly complex and dynamic battlefield. By prioritizing data and incorporating its use in fundamental planning processes, leaders, teams, and decisionmakers would be able to rapidly collect, organize, and visualize data to quickly gain situational awareness and make data-informed decisions during planning and execution. Doing this faster than our adversaries is key to the future employment of advanced technologies currently under development and to the maintenance of the U.S. military advantage. JFQ

Notes

¹ Joint Publication (JP) 5-0, Joint Planning (Washington, DC: The Joint Staff, December 1, 2020), K-4–K-6, https://irp.fas.org/dod-dir/dod/jp5_0.pdf.

² Field Manual (FM) 5-0, Planning and Orders Production (Washington, DC: Headquarters Department of the Army, May 2022), https://armypubs.army.mil/epubs/DR_pubs/ DR_a/ARN36775-FM_5-0-001-WEB-3.pdf.

³ FM 5-0.

⁴ JP 5-0, Joint Planning, III-14, III-15, K-5, K-6.

⁵ DD Form 2977, Deliberate Risk Assessment Worksheet, November 1, 2020, https://www.esd.whs.mil/Directives/forms/ dd2500_2999/DD2977/.