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Over the past few decades, businesses, governments, and militaries worldwide have relied heavily on data analytics to advance operations, gain critical insight for tactical advantages, and improve decision-making processes. More specifically, the Army has quickly realized that the ability to empower data analytics for its leaders gives it a distinct opportunity to move into a more data-centric organization, setting it apart from its adversaries. In doing so, it significantly emphasizes developing the training necessary for its formations.

Data analytics and analysis itself is not a new concept. It can be seen throughout history. Whether in the building of the great pyramids, the study of the universe, or today’s Army decision-making process, it all involves data analysis. However, how you gather, process, store, and analyze data has changed throughout the centuries and even more so in the past few decades. Primitive data collection methods and basic statistical analyses have become obsolete, replaced by the advent of advanced technologies and sophisticated algorithms. With these advancements come larger and cheaper storage, faster processing, and more advanced analytical tools, but these advancements also require more skill sets if the Army is to embrace its data-centric approach fully.

To take advantage of these advancements, ask, “What’s the big data?” Big data refers to data that is too large or complex, grows, or changes at such a high velocity that traditional methods can no longer analyze it. All major businesses and corporations suffer from big data issues, and the Army is no exception. This is why leaders at all levels must understand data, data collection, data analysis, and what it means to the future of the Army.

How does data analytics solve this issue, and why train leaders? To answer those questions, the concept of data analytics itself must be broken down. Think of data like a box of building blocks dumped onto the floor. All the different pieces are pulled from the different bags into one big pile. Those bags represent the Army enterprise resource planning (ERP) systems. All the blocks’ different colors, sizes, and shapes represent the raw data extracted from the ERP systems in various forms. The data would then have to be sorted by placing all the colors of the pile together. Once sorted, the pieces must be arranged to make them more easily identifiable. This is done by gathering all like pieces together within the colored piles. After sorting and arranging all the pieces, the data can be presented visually. One way of completing this would be by stacking all liked colors and pieces neatly together. The final step is telling a story and providing context to the data.

Giving leaders at all levels the understanding and tools necessary to navigate the data-centric environment allows for a more effective decision-making process. By collecting, analyzing, and interpreting vast amounts of data, Army leaders can identify patterns, trends, and potential threats, enabling them to make more fact-based decisions. Data analytics also allows commanders to leverage real-time information on the status of vehicles and weapons, weather conditions, enemy movement, and many other elements within battlefield dynamics.

Predictive analytics is also a key component of having data-driven leaders. Using advanced analytical methods to analyze historical sustainment trends more efficiently at the lowest levels provides significant advantages for future planning. Sustainment leaders can combine various sources such as the Global Combat Support System–Army, the Army Enterprise Systems Integration Program, the Integrated Personnel and Pay System–Army (IPPS-A), social media, or open source information to develop predictive models for future operations. This helps leaders better prepare their formations for any scenario.

Data analytics can assist the Army in optimizing the allocation of limited resources. By giving leaders the proper tools, they can identify areas where resources are underutilized or overextended, such as bench and shop stock. This knowledge enables managers to allocate resources more efficiently, ensuring they are deployed where they are most needed. From personnel allocation to strategic asset management, data-driven insights help leaders make more informed decisions, maximizing the effectiveness of available resources.

Take IPPS-A, for example. This massive database gives the ability to manage personnel more efficiently. The data from IPPS-A and other databases can be employed to assess Soldiers’ skills, training records, and performance evaluations. By analyzing this data, the Army can identify gaps in certain areas and allocate resources for targeted training programs to enhance Soldiers’ capabilities, with data analytics being a prime example.

Additionally, in strategic asset management, data analytics can be used to track and analyze the performance and utilization of military equipment and vehicles. By monitoring data such as fuel consumption, maintenance records, or deployment history, the Army can optimize the allocation of assets, ensure proper maintenance, and extend its life cycle.

These are but a few advantages that can be leveraged by developing more data-literate leaders. The Army’s emphasis on becoming more data-centric has engaged leaders at all levels to reevaluate how it trains within an ever-evolving, data-driven space. Still, it must start at the lowest levels. All leaders within the officer, warrant officer, and noncommissioned officer cohorts must develop valuable training for their formations if the Army is to be successful and take full advantage of the tools at its disposal.

Take the Army Sustainment University’s Technical Logistics College (TLC), for example. The TLC trains six military occupational specialties among the ordnance warrant officer advanced courses on 80 hours of data analytics. This program started as a 40-hour block of instruction and has since moved to 80 hours, broken down into three programs (MS Excel, Access, Power BI) and a group project. Having had the distinct privilege of instructing this course over the past two years, here are a few lessons learned.

Humans shy away from intimidating things that are too difficult to understand, so starting with the basics to train leaders earlier in their careers is essential. Therefore, it is important to simplify the introduction to these programs. This simplification sets the foundation for the students, who often need more knowledge of even the most basic data analytical tools. Applying this approach increases their confidence within the programs and helps encourage their continued learning of the programs beyond the classroom walls.

Don’t teach the shortcuts first. Shortcuts are invaluable time-saving tools within every analyst’s toolbox, but they don’t teach a beginner the why behind what is happening. If students are taught the theory of operations behind the various analytical methods, then the shortcuts begin to make sense. This also assists in applying troubleshooting techniques when a shortcut does not work. Students can more quickly identify the fault and take corrective measures, ensuring their product remains fully mission capable.

Sometimes, it’s best to leave the digital age behind when explaining. While that contradicts everything discussed so far, when the operation is related to its simplest form, the concept is grasped more quickly. For example, if you are working on referencing data between three different reports, ask the students to imagine those reports as hard copies presented before them. Ask them to explain how they would manually analyze the data. Their step-by-step explanation can directly relate to operations taking place within the program. They may have to put on their thinking caps, but it works.

Make the training relevant to those taking the course. It is hard enough to learn a new way of processing data and even harder when using data irrelevant to someone’s daily activities. Demonstrating actual functionality and how it relates to the individual keeps them engaged, helps them visualize real-world use, and validates the advantages of the tools being taught.

Lastly, encourage the idea that the more time spent on the front end creating a product, the more time there is on the back end to focus on other projects and, more importantly, family. The whole point of advanced data analytic methods is to analyze massive amounts of data more quickly and accurately. It may take months to create a product, but the effectiveness of that product and the time it saves will be worth it.

As the Army moves into the future, effectively collecting, analyzing, and utilizing data will be critical for its success on and off the battlefield. The advancements in technologies, data creation, and the Army’s shift to becoming a more data-centric military will only accelerate. Embracing these facts and understanding the importance of data analytics will enable leaders to be more adaptive and effective in an increasingly complex and interconnected world.

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Chief Warrant Officer 4 Jason Andrew Joseph Celestino Sr. has recently served as the site lead of the Armament Systems and Data Analytics departments and an instructor for the Technical Logistics College at Army Sustainment University, Fort Gregg-Adams, Virginia. He has a master’s in advanced business analytics from the University of South Carolina.

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This article is published in the Fall 2023 issue of Army Sustainment.