Journal of Defense Management
Open Access

Data Analysis of Defense Management and Budgeting

Chaves Wiles^{* *}Correspondence: Chaves Wiles, Department of Defense Management, Jamia Millia Islamia University, Jamia Nagar, Okhla, New Delhi, India, Email:

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Description

The potential efficiency and efficacy of government programmes will increase as our budget projections become more accurate. An example would be budgets for fuel forecasting. This two-part article examines a number of common data analysis methods with several managerial applications. Each method is briefly explained in the context of budgeting for fuel and includes instructions for using the method in Excel [1]. Risk mitigation is the process of preparing for calamities and figuring out how to lessen their negative effects.

Even though a company should be ready for any conceivable risk, a thorough risk mitigation plan would analyse each risk's effects and give those effects precedence when setting planning priorities [2]. Mitigation focuses on the aftermath of a disaster and the actions that can be taken before the event to minimise risk rather than creating a plan to prevent it. negative and potentially long-term effects.

In an ideal world, a corporation would be fully prepared for any threats or dangers [3]. Yet, a risk mitigation plan can help a business prepare for the worst by admitting that some amount of damage will happen and putting safeguards in place to handle it.

Data analysis of defense management

With one crucial exception, the cost of fuel makes for an easy case study to demonstrate numerous common data analytic techniques to enhance incremental public budgeting [4]. The methods of data analysis we have examined here are just "the top of the iceberg." Defense analysts use a combination of these technologies together with other data analytic techniques to tackle the difficulty of an actual fuel budget estimate [5]. A process whereby some risk is transferred to a third party. An insurance policy serves as one example.

The process of keeping an eye out for changes in hazards and how they could impact an organisation is known as risk monitoring [6].

Data analysis of budgeting

Common RAFs include the Operationally Critical Threat, Asset, and Vulnerability Evaluation (OCTAVE) from Carnegie Mellon University, the Control Objectives for Information and Related Technology (COBIT) from the Information Systems Audit and Control Association, and the Risk Management Guide for Information Technology Systems from the National Institute of Standards and Technology (NIST) (ISACA) [7]. The Mitre website also offers detailed risk reduction recommendations.

This two-part article examines a number of common data analysis tools with numerous military financial management applications [8]. Take fuel budgets for the military. The efficiency and effectiveness of our forces will increase as our budget predictions become more accurate. Spending too little can have an impact on operations, training, and equipment in the future, or it can mean sacrificing other goals in order to cover funding gaps. Spending too much also runs the danger of losing military goals since surplus money might not be identified in time to be effectively reallocated. Better assumptions are needed to reduce wasted reprogramming and expensive modifications. Fuel costs provide a problem because they fluctuate widely. There are three options available to us ignore, capture, or explain variability [9].

Extrapolative Forecasting and Sample Means (Averages), which mostly neglect variability, are covered in the first section before moving on to Confidence Intervals and Crucial Values, which make an effort to incorporate variability. The effectiveness of simulation and regression analysis, or "parametric estimation," which aims to explain variability, is explored in the next section. This two-part guide provides a brief overview of each method in the context of fuel budgeting, along with pointers on how to use the methods in Excel. The widespread use of these and other data analytics techniques could enhance military planning and budgeting while also boosting the effectiveness and efficiency of our armed forces [10]. In addition to having a solid understanding of internal demands and resources, external specialists can be a significant part of a risk mitigation plan.

Several BC and disaster recovery (DR) vendors focus on risk reduction, so even smaller businesses can benefit from DR as a service (DRaaS) providers to keep expenses down.

Conclusion

The fuel budget case study is used to review and summarise eight potential hypotheses that were produced using six common data analysis methods. The methods begin simply. Starting with extrapolative forecasting and sample means (averages), which mostly disregard variability, we move on to Confidence Intervals and Crucial Values, which try to capture variability, in the first of this two-part series. The effectiveness of simulation and regression, or "parametric estimation," which seek to explain variability, is examined in the next section.

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