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Role of Data Scientists in Military and Intelligence
Role of Data Scientists in Military and Intelligence

September 12, 2022

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Data science has carved its relevance in the military and intelligence in an era where the world's superpowers are continuously modernizing their military capabilities and strategy. The military leaders of the world's superpowers are aware of the crucial role that data science plays in the armed forces and that the leader of artificial intelligence will be in charge of the battlefield of the twenty-first century. Everyone agrees that training a workforce that can adapt quickly to technological change is crucial.

 

Understanding the role of Data Scientists in the Military

 

The fields of intelligence and the military heavily rely on data science. The help that data scientists provide to an organization in the military includes:

  • Lowering the likelihood of failure.
  • Improving event prediction.
  • Gaining a more profound knowledge of anti-national forces.
  • Getting novel insights.
  • Assisting inefficient expansion. 

 

With these advantages, operational casualties, collateral damage, crime rates, terror attack rates, and cross-border intrusion rates will all be reduced even further. Therefore, it goes without saying that in our technologically advanced world, the military, intelligence, and law enforcement gain from and feed data science.

Because of the wide range of fields in which data science is applicable, data science courses have gained widespread support.

 

Requirement of Data for Modern welfare

The extraordinary capabilities of contemporary, sophisticated sensor suites like ARGUS-IS are on the verge of being magical. However, defense tech claims that the Pentagon is having trouble hiring people to sift all of the data produced by such devices. In order to better understand how to handle the inflow of data, the Department of Defense has sought out businesses as varied as National Geographic and ESPN. 

 

Herein lies the military application of a skilled data scientist. Automation is one method for coping with the sudden influx. Data scientists use machine intelligence methods, like those used by the ARGUS-IS, to sift through data sources and find prospective targets for human examination.

 

In dealing with purposeful obfuscation and interference with data collection, military data scientists have a particular challenge that civilian data scientists do not. Spoofing, jamming, and deceit are frequently used during military and terrorist encounters. Advanced-trained data scientists continuously work to create algorithms that recognize such dishonesty.

In order to produce actionable intelligence, data scientists working for military and counterterrorism agencies must combine automated detection with creative interpretation.

 

Detection and Interpretation

 

However, the challenge is not just gathering and analyzing data from drones and other sensor platforms. The other half is the rapid delivery of actionable intelligence to soldiers and operatives on the ground. As a result, on the modern battlefield, big data has become a potent weapon similar to a double-edged sword. 

 

While connecting field agents and troops to the network is a challenging task in and of itself, it is insignificant compared to the complexity of delivering data quickly and clearly in high-stress situations. Moreover, the insights gleaned from the data might offer organizations a huge edge over their competitors. Still, the data stream can conceal critical information under a blitz of less important updates.

 

Tracking of Forces using the Internet Of Things

 

The US military developed software called "Blue Force Tracker" to address these problems. In order to equip tanks and other military vehicles with a GPS receiver, a satellite transceiver, and software, Blue Force Tracker (BFT) employs the Internet of Things. The vehicle's position and other status data are uplinked to military communications satellites and then merged with data from other vehicles and systems to deliver a comprehensive real-time picture of all assets in the neighborhood.

 

In addition to reducing unintentional fratricide, BFT enables commanders to monitor force deployments and optimize routes depending on geography and tactical planning.

 

Big Data is used in military logistics to fuel the machine.

 

For the soldiers, military logistical operations are a matter of life and death. Global commerce depends on civil logistical activities. As it is now understood, the field of logistics started as a study of military supplies and supply chains. 

 

Transporting Reaper components and fuel to forward airstrips is more important than ever since current hostilities are being fought more often on the other side of the world. So today, data scientists are trying to enhance military supply chain management much like their civilian counterparts at FedEx.



 

Automate Various Programs in the Military

 

Due to budgetary restrictions, the military is currently using more useful Big Data applications. Over a third of the federal budget is spent each year on the Department of Defense. In addition, it has a huge physical presence and is the world's most significant workforce (3.2 million people). As a result, programs like the Automated Energy Audit, which mines building environmental control data for up to 2,000 different improvements to reduce energy use, can have a significant impact.

 

In intelligence, data science refers not only to the latest hardware and software but also to the methodology for creating computing algorithms and statistical techniques for finding patterns and linkages in vast volumes of data. 



 

Conclusion


The military employs data science for various tasks, including intelligence gathering, surveillance, border, maritime, and space management, logistical, financial, disaster, and future technology management, and cognitive and historical data analysis. Moreover, they logically communicate their results to a lay audience. Data Science can be applied in every field, and also it takes a lot of time to become a trained data scientist. But that's without proper data science training.


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