The use of cloud-based analytics tools is increasing across the Department of Defense.
From using geospatial analytics to augment situational awareness, to identifying criminal networks using social media, cloud capabilities have proven to be far greater than the sum of their parts.
Moving applications onto the cloud doesn’t just make those applications more convenient to use. Putting them onto the cloud allows techniques of scale to emerge, including the ability to collect and correlate vast amounts of data, then to apply machine learning and artificial intelligence tools to process that data.
Intelligence Support Services that work with the DoD have become very adaptable, taking on new techniques for gathering and developing intelligence. We have learned to handle many analytical tasks related to conflicts against smaller adversaries. We have refined our work in the Middle East and against adversaries embedded within civilian populations, including terrorist organizations, criminal networks and more.
But with an increasing change of focus from conflict in the Middle East and mounting tensions in the Great Power Competition (GPC), new perspectives and techniques will need to be developed to meet the challenges of adversaries with a much larger scope. Both the Intelligence Community and Intelligence Support Services will be required to adjust as new types of threats arise.
Which leads to the question: how can cloud-based analytics be used to enable decision-making dominance in the Great Power Competition?
Table of Contents
What are cloud-based analytics?
Cloud-based analytics are analytics driven by applications hosted by a cloud services provider. Cloud-based analytical applications provide tools for analysts to assess the quality of data, categorize it, and connect it to other data to provide actionable intelligence.
That data may range from Publicly Available Information (PAI), such as social media posts, to secret data stored on appropriate Security Requirements Guide (SRG)-compliant (a.k.a. Impact Level) servers.
The analytical tools range from sophisticated geospatial analytics programs like Esri’s well-established ArcGIS to open-source tools newly posted on GitHub.
Using cloud-based building blocks, analytical tools can be created and tested quickly in response to newly available data, changes in how that data is formatted, and other considerations.
Some of these tools have a limited niche, such as understanding customer return rates for commercial products. But others, such as Esri’s ArcGIS and Google Earth Engine, span the globe and have been invaluable in locating bad actors, tracing their connections, and building and executing plans of attack.
Cloud-based analytics have been well positioned to develop solutions for the types of conflicts spanning the previous thirty years. But they still have untapped potential with regards to other types of developing conflicts, like the Great Power Competition.
What is the Great Power Competition?
The end of World War II brought with it a new conflict: the struggle against the spread of communism. The Cold War then established the U.S., Soviet Union, and China as superpowers. The major power struggle of the era was seen primarily as a conflict between democracy and communism, often carried out via conflicts in smaller, less developed nations.
Strategies for fighting the Cold War could be measured on massive scales and devoured enormous resources—enough to eventually destroy Soviet Russia from within.
The completion of the Cold War ended focused conflict with the Soviet Union and China, and replaced it with economic cooperation between the U.S., Russia, the former Soviet states, and China. Today, China builds our phones; Russia buys our real estate.
After the Cold War, an emergence of smaller conflicts across the globe also occurred.
These conflicts were driven by the Cold War and other conflicts from the past, and they were intensified and enabled by the “flattening” effect of new technologies. Conflicts, such as those that started after the events of 9/11, with less-powerful groups and nations who previously could not threaten the U.S. moved onto a worldwide stage.
Since the end of the Cold War, over a period of thirty years, the U.S. and its allies retooled their defenses and capabilities to shift their focus to these emerging conflicts.
As new technologies are integrated, their flattening effects are disruptive. The greater powers of the world are mastering the use of cloud computing in ways that require training, resources, personnel, and access that are not cheap to attain and maintain.
The problems we face in asymmetric conflicts with smaller groups and nations will never completely go away. But once again, global conflicts are coalescing around the U.S., China, and Russia and their allies, and this newest digital-age conflict is now known as the Great Power Competition.
We have not yet reached the level of tensions felt during the Cold War. How can we make decisions to prevent those dangers from returning?
What is decision-making dominance?
Decision theory is the study of how an agent—that is, a person, organization, or even an artificial intelligence program—makes choices. Through cataloging and databasing causes and effects of those choices, we can study them to determine how they were made. Results often reveal less-than-rational decisions yielding unintended outcomes. This study also offers more insight for maximizing choices in order to gain advantages over whatever situation is being studied.
A decision rule, or guideline for how choices are made, is said to “dominate” a second rule if the first rule is sometimes better, and never worse, than the second rule. Dominant strategies produce the highest overall payoff of any available strategy, regardless of the strategies employed by other agents.
In decision theory, decision-making dominance is the process of evaluating different rules to establish the best of all possible strategies.
In military terms, according to General John “Mike” Murray, decision-making dominance is “the ability for a commander to sense, understand, decide, act, and assess faster and more effectively than any adversary.”
That decision-making dominance fundamentally depends on having the best intelligence possible. Human analysts acting alone have limitations on how quickly they can produce intelligence, and how much data they can include.
It’s not easy to train a top-quality analyst; not even the most sophisticated computer program, cloud-based or otherwise, can replace a good analyst.
How can the production of intelligence be made faster and more effective?
How do cloud-based analytics drive decision-making dominance?
Over the last thirty years, weapons and defense systems have developed ever-increasing speed, range, and accuracy. Communications, data collection and storage, the reliability of hardware and software systems: our ability to apply technology to a diverse set of problems has vastly increased. We are even taking advantage of cloud-based platforms to make data more accessible, secure, and usable all the way to the edge of the battlefield, where connectivity is less than reliable.
Despite all this, we have only just begun to scratch the surface of what cloud-based applications can allow us to do.
To use the technology of the cloud in building decision-making dominance means understanding the capabilities available:
- Using the cloud, with its decentralized hardware locations across the globe, provides more resilience from disasters, physical disruption, and cyberattacks.
- Storing data centrally on the cloud allows it to be shared across services and other groups, providing a larger pool of data to analyze, potentially increasing the accuracy of intelligence.
- Managing access to data using the cloud means the data can be managed more consistently and reliably, while also giving administrators the ability to tailor working environments that limit data in a fluid way for sharing with our allies.
- Working from the cloud gives developers the ability to innovate faster, starting with application building blocks rather than coding software from scratch, and giving them the ability to more easily integrate new technologies from the commercial and open-source realms.
- More sophisticated tools are available on the cloud, including artificial intelligence and machine learning tools. These tools provide more accuracy and rapid applicability across several fields, with surprising developments arising from seemingly unrelated business cases.
- Performing processing-intensive activities on the cloud increases the ability to push data and capabilities to edge devices using a wide range of communication methods, and for data to be relayed back to the cloud more easily.
- Bringing that data back to the cloud gives analysts the ability to receive feedback from the field, allowing them to test and retest their assumptions by seeing the results of how their intelligence was implemented.
- Bringing data back to the cloud allows for a lot more computing power than can be had in more remote locations.
To make sounder strategic and tactical decisions, leadership needs to be able to obtain the clearest, fastest, and most effective intelligence picture possible.
That picture will always require human analysts to receive, process, analyze and develop.
But cloud-based analytics tools allow analysts to focus their talents on quickly and accurately developing good intelligence products that are easy to communicate and useful all the way to the edge of the battlefield. Cloud-based tools can also address issues of analyst bias and blind spots, bringing in relevant data from areas outside the analyst’s expertise with relative ease. Finally, cloud-based tools help analysts objectively assess the results of their intelligence in combination with leadership decisions, helping them develop iterative processes that lead to better and better intelligence, and decision-making dominance among leadership.
In the end, there will always be conflict.
Conflicts will change in focus and scope, changes to technology will cause new conflicts to emerge, and bad actors involved in the conflict will behave less than rationally. As is proven through the testament of time, we are perpetually either entering an entirely new conflict or simply rehashing a previous one.
Measuring the similarities or differences of today’s Great Power Competition with the Cold War may not be the right method for assessing it as a conflict. Conflicts across history are often driven by similar reasons. A key factor of the Great Power Competition (the thing that sets it apart from similar previous conflicts) may be how technological capabilities have changed.
Over the last thirty years, we have seen how easy it is for a group or nation to take a new technology and use it to disrupt existing power structures. The dangers of letting other nation states get ahead of us regarding our decision-making capabilities should be obvious.
Using cloud-based analytics capabilities to the fullest should be a priority, even in the face of security risks, political inconvenience, and struggles between cloud providers. Use of the cloud to perform cutting-edge analytics will certainly be a priority for at least some of our adversaries.
We can’t make other actors behave rationally, but we can ensure that we consistently make better informed decisions over time by using cloud-based analytics platforms.
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