Data Literacy for All: Why Every Airmen & Guardian Needs to be Data Literate Published July 11, 2025 By Major Nathaniel W. Flack, USAF In 2021, the DAF added digital literacy as one of its foundational competencies for all personnel. It joins twenty-three other traits identified as key capabilities members should foster. Additionally, it has reformed both officer and enlisted career fields focused on data analysis and engineering and has communicated why data is critical to future success in multi-domain conflicts. Recently, USAF released Doctrine Note 25-1 which sets a high standard for Airmen’s AI knowledge: “USAF personnel must be AI fluent to integrate its capabilities into operations.”[1] The note defines AI fluent Airmen as those who can comprehend, interpret, and navigate AI systems. This high bar is unachievable without first laying a solid foundation of data literacy. The DAF must create a holistic data literacy strategy to encourage data-driven decisions, enable digital transformation, and leverage analytical insights.[2] While positive change is occurring, a holistic strategy is needed to transform the culture and urge members to be data literate. Data literacy can build on existing initiatives surrounding data-driven decisions by providing a common lexicon so that every command and functional community can improve. Every unit may not undertake an all-inclusive digital transformation, but every DAF member should think more often about the data they have, the data they need, and how available tools can help get the right information to the right place at the right time to outcompete our adversaries and prevail in future multi-domain conflicts. To fuel this transformation, this article examines the fundamentals of data literacy and provides unit- and enterprise-level recommendations and potential challenges facing squadrons during implementation. Defining Data Literacy The term data literacy may be unfamiliar or intimidating to some, however, further consideration may reveal ways DAF members already use it on a regular basis. Jordan Morrow, a recognized data literacy expert, defines it as, “the ability to read, work with, analyze, and communicate with data."[3] People who can read data can distinguish good and bad data and quickly identify what data they need to answer relevant questions. Reading data includes identifying essential elements of a dataset such as unique keys and dataset and variable types. Working with data means personnel know how to move datasets between systems and effectively clean data to improve analysis. Analyzing data refers to the use of data tools to answer specific questions and derive insight from data. Typically, this requires a basic understanding of statistics to perform numerical analysis. Tailored, hands-on experience with data tools such as Envision, Excel, PowerBI, or Tableau can help perform analysis. Skills include identifying causation and correlation in a dataset as well as using the four levels of analytics: descriptive, diagnostic, predictive, and prescriptive. Mastering the first three parts of the definition enables DAF personnel to then confidently communicate the insights derived from the data to answer relevant questions and drive decisions. Effective data communicators can present data accurately and in a way that helps the decision-maker quickly understand the underlying reality. However, prior to reading or working with data, DAF members must have the skills to collect, locate, and gain access to the data they need. Currently, due to the size and complexity of the Department of Defense’s (DoD) data landscape, finding and accessing relevant data is a significant challenge and may be more time consuming than the four elements in Morrow’s definition. The DAF Chief Data and AI Officer should continue efforts to make DoD data repositories visible to members and streamline access requests. Right Data, Right Place, Right Purpose, Right Literacy Practically, DAF members use data literacy to identify the right data at the right time for the right purpose with the right literacy to facilitate data-driven decisions. The U.S. Army Command and General Staff College’s data literacy course considers these “rights” as the four truths of data literacy.[4] First, organizations require the right data to make relevant decisions. Second, personnel must collect, analyze, and present data at the right time to optimize decision making. DAF personnel should strive to implement tools and methods that enable real-time analytics to help military professionals gain insight into complex and dynamic environments. Third, members must collect, analyze, and explain data with the organization’s needs in mind. More data is not an inherent good. Advantage lies in identifying data that can advance DAF missions and transform its processes. Lastly, personnel must interpret data according to sound principles. The wrong interpretation of data will degrade an organization’s understanding, while the right explanation will provide a durable foundation for mission success. The four truths enable all personnel to contribute to healthy data collection, management, and consumption. Four Levels of Analytics To understand and apply data literacy, personnel must also understand the four levels of data analytics: descriptive, diagnostic, predictive, and prescriptive.[5] The four levels of analytics provide a roadmap for how DAF personnel can use data and information to enhance decision-making. First, descriptive analytics answers the question, "What happened?" and lays a foundation for understanding the connections between two or more variables. Second, diagnostic analytics look deeper to understand "why" a specific connection exists. Third, predictive analytics ask, "So what?" and attempt to forecast what will happen later based on trends. Finally, prescriptive analytics provides an informed "therefore" statement and answers, "What should we do now?" This level of analysis provides a decision-maker with courses of action to drive the desired outcome. The four levels are like stairs as lower-level analytics must precede the higher. As Figure 1 illustrates, DAF personnel cannot jump to predictive or prescriptive solutions without first performing extensive descriptive and diagnostic data analysis. Figure 1. The Four Levels of Data Analytics[6] While the terminology and data tools may be new to most DAF personnel, data literacy education should reveal how DAF personnel already use the principles above to accomplish their missions. Specific tasks may differ between functional communities and command levels, but all personnel are already invested in having the right data in the right place at the right time and interpreting it correctly to drive better decisions. These tasks are implicit in most DAF job descriptions and are already occurring at every echelon and across functional areas. Greater focus on data literacy does not seek to replace those efforts but enhance them by providing a common lexicon to discuss data initiatives, practices, and habits personnel can implement. This new dictionary and skillset will enhance operations at a tactical level by providing individuals with the means to better understand and support their decisions while also providing their chain of command with better visibility of their activities. Data literate members will also be equipped to evaluate and leverage enterprise-level datasets and tools to enhance operations. The DAF enterprise will benefit by increasing the amount and quality of available data for analysis without additional data calls. Pervasive data literacy will enable organizations and functional communities to establish data flows from the tactical to strategic levels with appropriate meta-data and tagging to enable and quicken decision making. Data Driven Culture Integrating data literacy throughout the force will require both technological and cultural changes. However, senior leaders’ comments and data initiatives focus on the technology changes required and downplay or ignore the cultural shifts that must accompany new tools and processes.[7] The implementation of a data literate culture in the DAF is an adaptive challenge, not just a technical one. According to Ron A. Heifetz, a technical challenge is one that has a clear solution and can be solved using existing expertise or by optimizing existing processes. Adaptive challenges on the other hand are complex and require changes in the way employees think and operate.[8] Adaptive challenges also require organizations to look outside their current expertise and experience for solutions. Heifetz argues that adaptive challenges require organizational learning, experimentation, and wise risk-taking. The problem of digital transformation presents as a technical challenge which requires new training, processes, and tools, but under the surface there is an adaptive challenge lurking: an Industrial Age workforce that is content to preserve current processes and tools and deflect calls for transformation. The DAF faces the adaptive challenge of transforming the mindset and habits of its members. New skills and processes are essential, but to many the digital and information revolutions look like more work piled onto an already full plate. The tyranny of the urgent crowds out the clarion calls for change and stifles innovation and transformation. The "good enough for today" cements the roadblock jamming the future of data rich, agile systems delivering relevant and useful information. Additionally, Heifetz observes that adaptive challenges require solutions owned and implemented by stakeholders instead of senior leaders. Therefore, leaders cannot force the implementation of data-centric processes and habits unless DAF members assume ownership of the problems and proposed solutions. In many cases senior leaders know changes are needed, but do not know exactly what to do and where to start. Tactical units and staff personnel must personally invest their time and energy to learn data literacy and risk failure as they experiment with new tools and processes. A leadership change or key decision alone will not be enough to overcome this challenge. Overcoming the adaptive challenge of data literacy implementation will help the force solve other adaptive challenges. The DAF is facing numerous adaptive challenges including shifting from an Industrial Age to an Information Age force and shifting from a focus on counterterrorism to Great Power Competition. This shift includes moving away from isolated units conducting preplanned missions to a collaborative approach that requires enhanced interoperability and flexibility. Top-down approaches must yield to collaborative team building. This shift is underway both internally and between organizations, however, digital tools and data literacy are needed to accelerate this transformation and provide a toolkit for DAF members to orient to the vast amounts of available data and decide on the right courses of action. As senior leaders press the DAF to transform, they also need to be cognizant of the challenges facing leaders at the lowest level of the organization. These leaders are asked to lead significant change in organizations who likely interpret “innovation” and “digital transformation” as code words for more work. These individuals and organizations have already endured "do more with less" and are likely overwhelmed by change fatigue. Leaders are right to recognize the cost and time savings available through the adoption of new technology and processes, however, there will be a messy middle period that will require clear communication and vision casting to overcome the adaptive challenges. Using data literacy to overhaul DAF processes and operations will require new skills, knowledge, and space to fail. Russell Ackoff, a pioneer in systems thinking, operations research, and management said, “The more efficient you are at doing the wrong thing, the wronger you become…If you do the right thing wrong and correct it, you get better.”[9] Stumbling along the road to data and AI literacy is the right thing, but in many cases, difficulty will increase before improvements are realized. Opportunities and Recommendations The DAF enterprise can encourage every member to consciously think about the way they use data and provide the education, training, and tools needed to enhance their own operations and contribute to enterprise changes related to the management and use of data. The author’s experience at a squadron level highlights several challenges and lessons learned for implementing new data management practices. These reflections illustrate the challenges tactical leaders face while implementing data-centric processes. As they appreciate these challenges, senior leaders can better support squadron and small unit leaders through their advocacy for new technologies and habits. Additionally, unit leaders at various levels as well as staff members can seek to guide their own teams by learning from previous blunders. Recommendations for Squadrons and Small Units 1. Leverage available tools to streamline reports and reduce time duplicating information in PowerPoint slides. Tools such as Envision, PowerBI, NIPRGPT, etc. can help build flexible products that aggregate, store, and display data to reveal both current status and historical trends. Units can replace static weekly activity reports with products that automatically update as new data is added if they take time to collect the information in structured datasets. Administrative personnel and commander’s support staff can replace staff meeting slides with Envision to display personnel data pulled daily from authoritative data sources.[10] Visually striking and filterable dashboards already exist that will automatically pull data based on the user’s unit information and permission level. 2. Invite unit members to take the Digital Literacy assessment in MyVector to reveal individual gaps.[11] Discuss how members can improve digital, data, and AI literacy during all calls and professional development sessions. 3. Determine the best training for your organization and make it easily accessible to your members. Create your own certification for those who engage the recommended training and hold competitions for those who can create the best dashboard, automate an established process, or leverage data to solve an organizational problem. Free training exists through DigitalU, DAF e-Learning, built-in Palantir courses on Envision, and HAF CDO training.[12] 4. Normalize the use of data analysis tools such as Envision and PowerBI in your unit just as Word, PowerPoint, and Excel are today. When correctly configured, PowerPoint slides can pull from live data feeds and provide value beyond a single event, but alternate tools may prove more useful to decision makers in the medium- and long-term as live reports can be revisited later to view the most up-to-date information without additional meetings or coordination. 5. Use Envision, PowerBI, or another available tool to regularly ingest raw exports from other systems to avoid manual data re-entry. These tools may provide better data visualizations and opportunities to correlate data between systems that may not currently share data. Data may need to be added and updated manually, however, Envision may already have a connection to the data you need and pull data on a regular basis. Additionally, data that already exists in DAF 365 may be accessible from PowerBI or Envision. Enterprise-Level Recommendations: 1. Include a lesson or course on data literacy in every accession and professional military education course. These courses, while already full of vital subjects, do not need to dedicate a lot of time to this topic, but should address it early and seek to integrate sound data literacy principles and habits into every other subject. Education and training courses should include the four laws of data literacy and the four levels of analytics. Hands-on learning is strongly recommended for enhanced student learning and engagement. 2. The DAF should integrate a data literacy assessment into the MyVector application. This assessment should reveal participants’ strengths and weaknesses and suggest education and training resources to improve. 3. Functional communities should aggregate their data and make it accessible to base-level users so they can analyze relevant data and integrate it into unit dashboards and common operating pictures. The Manpower, Personnel, and Service Support (A1) community has achieved notable success by creating unit-level access for commanders and admin staff in Envision.[13] These ready-made tools could replace a large portion of the slides created regularly for personnel staff meetings. 4. The USAF communications community should consider rebranding squadron Data Operations offices to Wing Data Offices and diversify expertise. Data Operations was formerly known as Knowledge Operations and Knowledge Management and has historically focused on SharePoint management. Wing Data Offices should serve as the installation’s core for digital transformation and data literacy. Their primary role would be to enable all organizations in the Wing to leverage available training and tools to build their own products. Their expertise in data science and analysis should be reserved for the hardest problems while they empower motivated operators and technicians to analyze data and build dashboards. Focus on How not What Any change toward greater automation, data-driven decisions, and data-centric habits must focus on how members fulfill the mission, not just what they do. The DAF’s guidance on personnel competency models holds, “High-performing organizations recognize that it is not what people do but how they do their jobs that makes the difference in achieving objectives.”[14] Initially, intermediate and frontline leaders may need to mandate new processes and tools, however, long-term and meaningful change will come when members shift their mindset and habits. As members overcome the inevitable pain of transformation they will reap the benefits of greater situational awareness, shared understanding, and a wealth of accurate, accessible, and clear data to support their decisions and those of their commanders. Common Objections Several objections arise when considering the concepts and recommendations in this article. One common objection to DAF-wide data literacy is that it is only necessary for specific career fields such as Operations Analysts and Data Operators. This objection is partially correct. Not every DAF member should be a data engineer, data scientist, or even a data analyst. Data-specific career fields and specialties need to know how to use advanced tools and perform difficult analyses. These specialties should focus on data connectivity, deep analysis, and enabling non-data specialists to use available tools to solve their own problems. These AFSCs should have access to advanced courses and hands-on learning to serve as guides for the force. The focus of data-centric career fields should be to tackle the toughest and most important questions while lowering the learning curve for everyone else. Those with knowledge and access need to make data more accessible while pointing others to the best training resources for their current requirement. Some may argue that the lack of data literacy is due to an aging workforce and younger generations, as digital natives, possess natural data-centric habits. It is true that younger members of the workforce tend to have higher levels of data literacy and are more comfortable using digital platforms.[15] However, specific education and training is needed to orient younger generations to the DoD’s digital ecosystem as well as foster data-centric habits and analytical skills that are not naturally acquired through increased technology exposure. Older members of the workforce have greater experience and subject matter expertise to perform higher levels of analysis and know what data would be the most helpful. All generations in the workforce should be included in data literacy training initiatives, although different emphases may be needed across generations. Another objection is that digital transformation removes manual and analog systems and processes militaries need for resiliency during a conflict. Again, this is a necessary check on digital transformation because the military must consider the realities of combat when enacting change. Pervasive data literacy and innovation does not require the abandonment of all manual and analog elements. Each functional area must consider what manual controls will ensure resilience and safety. Costs and benefits must be weighed by subject matter experts to determine the best course of action. However, data literacy is needed to inform these tough decisions and better understand the benefits and drawbacks of new technologies. Additionally, leaders need wisdom to identify when change-resistant personnel misidentify redundancy and safety as justification to block necessary improvements. Finally, the author admits that widespread data literacy is not a silver bullet. It will not fix all the DAF’s challenges and may even create some of its own in the short term. However, the long-term benefits available through immediate and decisive action should not be overlooked. Data literacy is a skillset and provides a pathway to creatively solve problems in any echelon or functional area. Therefore, while the benefit of greater data literacy will be difficult to measure, the potential cumulative impact of relatively minor changes in education, training, and programs is enormous. Conclusion Widespread data literacy will provide a lexicon and habits on which to build advanced tools and processes informing data-driven decisions. The USAF holds, “The collection and management of high-quality data is a critical challenge in the development and implementation of AI tools.”[16] Sound data and AI practices will be impossible without a firm foundation of data literacy for all Airmen, Guardians, and DAF Civilians. Members do not need to be data scientists to be data literate. Basic education and training will help personnel consciously leverage data and identify ways they already use data and the four levels of analysis to accomplish their mission. To overcome the adaptive challenge of digital transformation, organizations must change both their technology and their culture. If the DAF cannot make widespread changes to value data-centric practices and data-driven decisions, it will continue to lose time mastering opaque, manual processes. The proposed recommendations will contribute to a culture shift toward healthy data practices and increase understanding of the unique challenges ahead. Disparate changes are underway across the DAF, but a comprehensive and unified plan is needed to forge a new data literate culture. These changes will not be easy but will enable the understanding and agility needed to compete with, deter, and defeat near-peer adversaries in the Information Age. Author: Major Nathaniel W. Flack, USAF, is a Warfighter Communications Operations Officer attending the U.S. Army Command and General Staff College in Fort Leavenworth, KS. Bibliography Akello, Travis. “Digital Literacy and Media Consumption among Different Age Groups.” Journal of Communication 5, no.2 (2024): 14-27. Freedberg, Syndney J., Jr. “New Air Force Vice Chief has ‘Passion’ for Better Data, Wants Industry Help.” Breaking Defense, March 1, 2024. Headquarters, Department of the Air Force. Competency Modeling. Air Force Handbook 36-2647. Department of the Air Force, 2022. . Heifetz, Ronald. “Adaptive work.” The Kansas Leadership Center Journal (Spring 2010): 72-77. Jones-Martin, Caitlin. “New ‘Digital Literacy’ and ‘Fosters Inclusion’ foundational competencies now in MyVector self-assessment tool.” Air Education and Training Command. February 26, 2021. Accessed May 28, 2025. Kotter, John. P. Leading Change. Harvard Business School Press, 1996. LaPreze, Robert. “Lesson 1: Introduction.” A205: Data Literacy in the Operations Process. Class lecture at the US Army Command and General Staff College, Fort Leavenworth, KS, April 2, 2025. McCollum, Bill and Shea, Kevin. Adaptive Leadership: The Leader’s Advantage, Arthur D. Simons Center for Interagency Cooperation, Fort Leavenworth, Kansas. Accessed May 15, 2025. . Miller, Stephen. “A Management Philosopher with Heady Ideas About Beer.” The Wall Street Journal, November 11, 2009. Morrow, Jordan. Be Data Literate: The Data Literacy Skills Everyone Needs to Succeed. KoganPage, 2021. Thorton. “Lesson 3: Right Time” A205: Data Literacy in the Operations Process. Class lecture at the US Army Command and General Staff College, Fort Leavenworth, KS, April 7, 2025. [1] USAF Doctrine Note 25-1, 10. [2] Morrow, Be Data Literate, 120. [3] Morrow, 36. [4] LaPreze, “Lesson 1: Introduction.” [5] Morrow, 17. [6] Thorton, “Lesson 3: Right Time” Used and modified with permission from Lieutenant Colonel Thorton. [7] Freedberg, Vice Chief has ‘Passion’ for Better Data.” [8] McCollum and Shea, Adaptive Leadership, 167. [9] Miller, “A Management Philosopher.” [10] DAF members can access Envision on NIPRNet and SIPRNet. A virtual private network (VPN) connection is required. Administrative personnel must submit a request to access unit-level PII to view personnel data for the following unclassified tools: Sq Staff Meeting, UMD+, CSS+, AFGSC’s Sq Staff Meeting, and more. [11] Jones-Martin, “New Foundational Competencies in MyVector.” [12] https://digitalu.af.mil, https://daflearning.af.mil, https://envision.af.mil/workspace/training, https://usaf.dps.mil/sites/CDO/SitePages/CDOTraining.aspx, https://envision.af.mil/workspace/module/view/latest/ri.workshop.main.module.b9de5a2c-a45e-470b-9f19-3efd38916310. Envision access requires a NIPRNet or VPN connection. [13] Envision provides access to personnel data that is updated daily from authoritative data sources. Personnel must have PII access to see their unit’s status. UMD+ and CSS+ are just two of the ready-made tools available to every command in the DAF. Envision access requires a NIPRNet or VPN connection. [14] Dept. of the Air Force. Competency Modeling, 3.1.1. Emphasis added. [15] Akello, “Digital Literacy and Media Consumption,” 14. [16] USAF Doctrine Note 25-1, 10.