Decoding Data: Five Layers of Data Analytics through a Military Lens
As a nerdy military millennial, I can’t get enough of data analytics. But let’s face it, sometimes the way analytics is taught is a PhD-level slamming of complexity or a talking down to. It’s a shame those mistakes happen because data analytics can be clutch for making better decisions — and not just for the military, it’s a game-changer in any field. That’s why I’m writing a “Decoding Data” series of articles, where I’ll dish out six articles focused on making analytics easy to understand, especially when it comes to military decision making. So, let’s boost our data skills and start making those winning choices.
To kick off our journey, let’s dive right into the layers of data analytics and explore how they can revolutionize decision-making, particularly in the military. Fear not if you’re no military strategist or General Patton — the principles we’re going to discuss are universal and can help inform better decisions in any field. Are you ready to start demystifying data analytics? Let’s dig in, layer by layer.
There are five layers of data analytics:
Real-time Analytics — The analysis of data as soon as it is made available.
Descriptive Analytics — The analysis of what happened in the past.
Diagnostic Analytics — The analysis of whether what happened was good, bad, and why?
Predictive Analytics — The analysis of what is likely to happen based on past events or conditions.
Prescriptive Analytics — Decisions and/or protocols on what to do if analytical conditions are met.
Let’s dive deeper into each:
Real-time Analytics
Command and control is a vital military concept that hinges on utilizing resources and real-time information to achieve mission success. In the heat of combat, making informed decisions is paramount, and commanders across various organizations rely on receiving detailed, up-to-the-moment information. This is where real-time analytics plays a crucial role, providing the tools and technology to keep leaders in the loop.
Examples of real-time analytics in the military are abundant. One such technology is Blue Force tracking, which enables the precise tracking of friendly forces on the battlefield. The Command Post of the Future (CPOF) is another powerful tool, providing a wealth of real-time information at the fingertips of commanders to aid in their decision-making process.
Even fighter pilots heavily rely on real-time analytics through their avionics systems. Heads-up displays, multi-function displays, and fuel system displays all deliver updated information in the blink of an eye. This millisecond-level data empowers pilots to execute precision maneuvers and make life or death decisions in the skies.
Descriptive Analytics
Descriptive analytics provides a historical perspective on past events. Although it is one of the most familiar types of analytics, its reliance on consistent and accurate data, along with the challenges of long-term storage, makes it a difficult endeavor.
Let’s consider a practical example to grasp the significance of descriptive analytics. Take this pie chart, for instance:
At first glance, it’s evident that Russia primarily exports oil to India and China. Yet, this chart falls short in providing deeper insights — it’s like stating “It’s raining outside” without the context of historical weather patterns.
Now, imagine the same data presented in a highly descriptive analytical style:
Suddenly, the story unfolds effortlessly. You can tell that, before 2022, India accounted for a negligible portion of Russia’s exports, while Europe held a significant share. Descriptive analytics grants us a holistic understanding of the recent past, infusing meaning that was missing from the pie chart data.
Diagnostic Analytics
Imagine you’ve been closely following the topic of the last two charts, and naturally, you might have pondered or even formulated a hypothesis about why India’s share of oil has risen. Welcome to the realm of diagnostic analytics, where we delve into the “what was good,” “what was bad,” and the “why” behind events.
Now, pay attention to a crucial aspect that turns a mere descriptive chart to a diagnostic one:
This small yet impactful phrase is the key that turns data into insights. Russia’s invasion of Ukraine presents a compelling argument for the increase in India’s share of Russian oil exports. While we know that correlation doesn’t always imply causation, this case seems pretty damn convincing. That’s the magic of diagnostic analytics — it uncovers those “Aha” moments and helps us make persuasive arguments.
In the military, such insights can have a profound impact. They provide leaders with a deeper understanding of events, enabling them to make informed decisions, regardless of political sentiments. To me, this should always be the ultimate aim of analytics — to deliver powerful and meaningful insights that shape our understanding of the world.
Predictive Analytics
Predictive analytics is the delightful icing on the cake, complementing the hard work of descriptive and diagnostic analytics. Once real-time data has been stored and significant diagnostic moments have been identified, predictive analytics comes into play.
This is where the magic of algorithms — mathematical problem-solving tools that predict outcomes — comes to life. The quality of the algorithm directly impacts the reliability of the model’s predictions. However, it’s crucial to note that predictive analytics doesn’t mean events are set in stone. Just like projecting the best baseball team doesn’t guarantee their victory — upsets remain a natural phenomenon.
Consider the chart below, displaying the United States National Debt as a Percentage of Gross Domestic Product (GDP):
It encapsulates the elements of a compelling analysis. The chart tells a story, both in the present and through its projection into the future. Notably, a little box further explains the risk associated with the projection, adding further diagnostic aspects to the chart.
Predictive analytics can be powerful, but like any glimpse into the future, it often encounters skepticism as it ventures into the unknown. Nonetheless, it holds immense value in making informed decisions and understanding potential outcomes. Under the lens of military decision making, predictive analytics can sometimes be meaningless due to the frequency of operations under unknown circumstances.
Prescriptive Analytics
Move beyond the power of predictive analysis, and you’ll find the crown jewel of insights — prescriptive analytics. Going beyond merely predicting outcomes, prescriptive analytics strives to offer solutions that lead to more favorable results than the status quo.
Let’s take a simple yet powerful example in a fictional hurricane scenario:
“We expect expect 10% of our population to be hospitalized if no one evacuates, and less than 1% if we have state-wide evacuations.”
Here, prescriptive analytics steps in, providing a clear course of action to minimize potential harm and maximize safety.
Another example can be found in a simple poster encouraging bikers to wear helmets:
Prescriptive analytics is a game-changer in risk management, a vital aspect of military decision making. On the battlefield, commanders strategically maneuver to manage risk effectively. Descriptive analytics offers more context than a mere snapshot in time, and predictive analytics delve into projecting potential outcomes. Yet, it is prescriptive analytics that truly empowers commanders with actionable insights — revealing what is likely to happen for each choice they make.
If you’ve stuck with me this far, congrats. Hopefully you’ve got a better understanding of the types of analytics that exist. The next time you see an analytic, try to ask yourself how many styles of analysis are at play. When you see our next article, we’re going to employ these skills in a case study about military recruiting.
See you soon.
