Risk Analysis For Natural And Man-Made Disasters ✓ Solved

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Risk Analysis for Natural Disaster and Man-Made Disaster Incidents

Risk Analysis for Natural Disaster and Man-Made Disaster Incidents In the IT operation department, a risk analysis of natural disasters is helpful not only for disaster prevention and reduction, but also in reducing economic and social losses. Currently, there are many methods for natural disaster risk analysis. Natural disaster risk analysis is the premise and foundation of natural disaster risk management, natural disaster risk zoning, and disaster loss assessment.

Natural disasters are extreme geophysical and biological events, which include geological disasters (earthquakes, volcanic eruptions, landslides, avalanches), atmospheric disasters (tropical cyclones, tornadoes, hail, ice, and snow), hydrological disasters (river floods, coastal floods, and drought), and biological disasters (epidemic diseases and wildfire) (Smith 2003). Some scholars believe that natural disasters are caused by natural events or forces, resulting in casualties and loss of human social property and data (Huang 2009).

According to Ni and Wang, natural disaster risk is the uncertainty of future loss caused by the evolution of the natural disaster system itself (Ni and Wang 2012), while risk is a future scenario related to certain adverse events (Huang et al. 2010). Natural disaster risk analysis can help reduce the loss of server production. As such, natural disaster risk has three characteristics, namely uncertainty, and unforeseen circumstances.

This can be achieved by identifying the company’s key assets and their values and by protecting them from before incidents occurs. In performing the natural disaster risk analysis, the risk analysis must focus on what disaster problems are about to happen, how likely they are to happen, and the possible consequences thereof. However, problems in terms of data in disaster risk analysis are either insufficient data, a small sample, and poor information, or massive data, a large sample, and poor information.

Analyzing such requires not only conventional mathematical modeling and system analysis methods, but also decision analysis tools for dealing with uncertain information. Traditional mathematical statistics tools, measurement methods, optimization, and predictive decision-making techniques, as well as constantly innovating gray system theory, uncertainty theory, and intelligent algorithms provide a solid tool for disaster risk analysis modeling.

Note that different risk analysis methods may produce different risk assessment results; thus, the nature and applicability of risk analysis methods should be correctly understood (Liu and Shang 2014). Analysis methods based on risk uncertainty: The uncertainty of natural disaster risk corresponds to the randomness and fuzziness of things (Huang et al. 1994).

Therefore, the probability and statistics method are often used in the risk analysis of natural disasters.

Man-Made Risk Analysis: These are usually caused by people which is also known as human error. For example, an employee may forget to enter key data. An IT technician could fail to follow a backup procedure resulting in an incomplete backup. An administrator may write incomplete or incorrect backup procedures. Undiscovered software bugs can also cause serious problems (Gibson, 2015).

The Company’s Disaster Recovery Team It is imperative for organizations to have a group of stakeholders that would be identified as the incident recovery team. These group of individuals are responsible for establishing and maintaining business procedures and business processes.

For an effective IT disaster recovery plan to be implemented and maintained, a disaster recovery team is essential. The disaster recovery team is also responsible for analysis of existing network or IT structure, applications, databases and organizational setup. They are also responsible for having the master list of all storage locations, inventory, customers, forms, policies and alternate locations for operations. It is often recommended for a disaster recovery team to have members from all departments of an organization.

In an IT operation, specific responsibilities should be assigned to different representatives. There should be teams responsible for administrative functions, facilities, logistics, user support, computer backup, restoration and other important areas in the organization. The structure of the contingency organization may not be the same as the existing organization chart.

The contingency organization is usually structured with teams responsible for major functional areas such as:

  • Administrative functions
  • Facilities
  • Logistics
  • User support
  • Computer backup
  • Restoration
  • Other important areas

The management team is especially important because it coordinates the recovery process. The team should assess the disaster, activate the recovery plan, and contact team managers. The management team also oversees, documents and monitors the recovery process. Management team members should be the final decision-makers in setting priorities, policies and procedures. Each team has specific responsibilities that must be completed to ensure successful execution of the plan. The teams should have an assigned manager and an alternate in case the team manager is not available. Other team members should also have specific assignments where possible (Wold, 2006).

Techniques Used for Data and Application Backup and Recovery. The replication of data and application within an IT operation is very essential because it helps an organization to restore business functions after a disaster. It’s important for businesses to plan ahead and put data backup systems into place in case of unforeseen events, well before it happens.

Successful data backup systems are accomplished by using an offsite server or separate drives to store your massive amounts of information. Without putting these systems in place, data recovery becomes difficult resulting in loss of information when the worst happens.

Examples of techniques that would be suitable for data and application backup and recovery include:

  • Storing the replicated copy of the data in another geographical location. Organizations should follow the best practice of having the duplicated data stored in another safe location in case when unplanned disasters occur (Gibson, 2015).
  • The second copy protects against disasters, such as fire or flood. If the only backup of the data is kept with the server, you’ll have no backups if the server room burns.
  • Cloud Backup: Many organizations have adopted the cloud backup technique as a means of backing up their company’s file. Cloud virtualization is considered as the most cost-effective and less time-consuming way of storing and protecting data against data loss.
  • There are different methods that could be used to back up data in the cloud and they are:
  • a) Direct-to-cloud: With direct to cloud backup, offsite file backups are copied directly to the cloud, bypassing the need for a local device.
  • b) Cloud-to-cloud backup: This is the process of copying data from one cloud to another cloud.
  • c) SaaS backup refers to backing up data created in SaaS applications such as Microsoft 365 or Google G Suite.

Paper For Above Instructions

Risk analysis in the context of natural and man-made disasters is critical, especially within IT operations. Natural disasters, ranging from earthquakes to flooding, present unique risks that operational departments must manage effectively. In 2012, Ni and Wang defined natural disaster risk as the uncertainty of future loss stemming from the evolution of disaster systems (Ni & Wang, 2012). Such losses can take various forms including economic impacts and disruption of services. Conversely, man-made disasters often arise from human error, underscoring the need for rigorous backup systems and disaster recovery plans (Gibson, 2015).

To tackle the threats posed by natural disasters, organizations employ various risk analysis methods, complemented by modern statistical and decision-making tools. For instance, in implementing risk management strategies, understanding the nature and probability of anticipated disasters enables organizations to devise robust protective measures (Huang et al., 2010). Mathematical modeling conjoined with contemporary theories like gray system theory aids in assessing disaster risk, allowing for model innovativeness and adaptability (Liu & Shang, 2014).

The integration of comprehensive disaster risk assessments is pivotal, as they facilitate the identification of key assets within an organization that must be safeguarded. Effective risk analysis revolves around forecasting potential scenarios, their likelihood, and subsequent consequences. In this pursuit, organizations sometimes grapple with data challenges including inadequate or excessive data, forming a barrier to effective decision-making. Such challenges necessitate the application of advanced analytical tools that can navigate uncertainties associated with substantial data (Huang et al., 1994).

To accurately assess the risks involved, organizations must not only analyze current vulnerabilities but also prepare for potential human-related errors - a defining feature of man-made disasters. They involve meticulous practices, such as strict adherence to procedures for data entry and backup protocols to mitigate risks associated with operational lapses (Gibson, 2015). IT departments must remain vigilant, as uninterrupted service delivery is frequently contingent on the systems' robustness against such mishaps.

On the organizational front, effectively structured disaster recovery teams play a vital role in this sphere. According to Wold (2006), successful disaster recovery not only requires a dedicated team but also an organized approach to business processes essential for maintaining operations during crises. Ensuring representation from all departments within a recovery team guarantees comprehensive coverage and efficient management during such incidents. Clear delineation of responsibilities forms the foundation of effective recovery plans, enabling organizations to execute strategies seamlessly, despite facing challenges.

Strategies for data and application recovery intensify the need for an early investment in backup technologies and approaches that safeguard critical information from unforeseen events. Techniques such as geographical data replication provide a subsequent layer of security and reassurance against physical loss from disasters (Gibson, 2015). The prominence of cloud-based solutions further simplifies the backup process and offers a practical, efficient method for organizations seeking to enhance their data protection strategies. It is paramount for businesses to deploy variations of cloud backup relevant to their operational frameworks, such as direct-to-cloud strategies and SaaS integration for seamless data preservation.

In conclusion, the evolving landscape of natural and man-made disaster risks demands a proactive approach to risk analysis and management. By comprehensively understanding risk factors, implementing efficacious recovery strategies, and ensuring ongoing training and preparedness among staff, organizations can mutualize their operational resilience against these unpredictable events. The integration of sophisticated analytical tools, structured recovery frameworks, and cloud-based data strategies are imperative for organizations navigating the precarious nature of disaster impact.

References

  • Gibson, D. (2015). Managing Risk in Information Systems. Burlington: Jones & Barlett Learning.
  • Huang, C. F., Liu, A. L., & Wang, Y. (2010). Discussion on basic definition of disaster risk. J Nat Disasters, 19(6), 7–15.
  • Huang, C. F., Shi, P. J., & Zhang, Y. M. (1994). One level model of risk evaluation on city natural disaster. J Nat Disasters, 3(1), 3–8.
  • Liu, X. L., & Shang, Z. H. (2014). Risk analysis methods of natural disasters and their applicability. Progress Geogr, 33(11), 1486–1497.
  • Ni, C. J., & Wang, J. (2012). Further discussion on the definition of natural disaster risk. J Catastrophol, 27(3), 1–5.
  • Smith, K. (2003). Environmental hazards: assessing risk and reducing disaster. Routledge, London, 8–9.
  • Wold, G. H. (2006). Disaster Recovery Planning Process. Disaster Recovery Journal, 5.

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