Information Technology Infrastructure Management Essay

Question:

Explain Information Technology Infrastructure Management?

Answer:

Introducation

Just-in-Time Delivery – Many organisations implement this inventory strategy to achieve incremental efficiency and less accountof wastage is sustained by obtaining items in the right amount for the process of production. This method requires producers to plan an accurate demand of supplies (Bookbinder & Dilts, 2016).
E-Commerce – It is a process by which buying and selling of any type of goods at any proportion is conducted electronically, that is via internet within a buyer and a seller (Laudon & Traver, 2013).
SaaS – Software-as-a-Service is a method or model used for software distribution in which an external provider makes software available to customers through the web. SaaS, PaaS and IaaS are the main categories in cloud computing (Brown & Nyarko, 2012).
Strategic Planning – The method adopted by an organisation to create strategies that will aid in the development of the same and determining resources required to maintain the strategy is known as strategic planning (Bryson, 2012).
Supply Chain Systems – It is the accumulated activity and involvement of an organisation, its employees in every level of the organisation, resources and information that is required to transport a service or product from the supplier to the consumer (Stadtler, 2015).

DSS – A Decision Support System is a digital information system that helps the people of an organisation in the activities of decision-making (Shibl, Lawley & Debuse, 2013).
Cloud infrastructure – To meet the computing requirements of a cloud-computing model, certain hardware and software components are necessary. These components are referred as the cloud infrastructure. These include storage systems, servers, networking and virtualisation software (Jadeja & Modi, 2012).
Web 2 – It is a term used collectively for some applications of the internet and the world wide web that includes video sharing services, blogs, social media websites, wikis and such that serves the purpose of interactive sharing and participatory collaboration instead of simple content delivery (Berthon et al., 2012).
Extranet – A private network utilising the technology of Internet and the public telecommunication system to share some part of organisational data or functions with partners, suppliers, customers, vendors or other companies securely. It is also referred as section of a company’s intranet that is extended to external users (Caber, Albayrak & Loiacono, 2013).
Big data analysis – It is the process by which examination is conducted on big data to identify market trends, secret patterns, customer preferences, unknown correlations and other such vital Intels, which is beneficial for companies in making detailed decisions in business (Trnka, 2014).

The key elements of data mining are as follows:

Increment in quantity of data – Data mining has become easier in recent years due to the involvement of information technology. A user can use information technology for gathering data without taking the help of any client (Liu & Motoda, 2012).

Complicated Data Structure – The process of information collection in data mining involves manual as well as digital technique to acquire the required information. Understanding and determination of mining may involve complicated data structure (Liu & Motoda, 2012).

Providing incomplete data – Insufficient information is generally input by people in anticipation of getting their information exchanged during surveys that is conducted by data mining system for its own beneficial purpose (Liu & Motoda, 2012).

It serves the purpose of predicting future – It is easier to predict future of any organisation using a data mining system as it stores all the data related to the same (Liu & Motoda, 2012).

Reserve Stock Level is referred as the minimum resource that is mandatory to be maintained in the inventory of an organisation or system. The surplus resource that has exceeded the set limit of reserve stock can be used in the areas of resource shortage. To ensure the perfect functionality of an ERP system within an organisation, it is necessary to determine the proper management of certain operations like maintaining the Reserve Stock Level of business (Maddah et al., 2014). Maintaining the resource availability as per the standard of Reserve Stock Level set by the system will allow a steady flow of work within the projects conducted by the organisation, without running the risk of dismissal or halting of project due to resource shortage. A company that has used up the stock resources irrespective of the set limitation may be affected later. That is why it is necessary to understand the significance of Reserve Stock Level function of ERP for flawless performance of the system (Maddah et al., 2014).

The initial reason that adds to the business risks of Liberty Wines organisation is with the IT facility within the organisation that has to handle excess data than it is capable of with the increase in the dimension of the business. Apart from this, the systems slowed down in performance gradually and required even more maintenance than was required earlier. Finally, loss of employee productivity was caused as more work forces were involved to increase the productivity of the company. The increase in work forces affected the core business processes as well as the inventory management functions and the order processing ("Liberty Wines (UK)", 2017).


The company improved resilience and stability using a backup system, minimised expenditures in hardware replacement, power and air conditioning, and sped up business processes by enabling apps to perform faster. In doing so, the company helped its employees in providing better customer service with enhanced productivity. The improvement of business is faster and easier. In this way, the IT infrastructure of Liberty Wines has provided competitive advantage in terms of improved service, reduced cost and providing for further growth ("Liberty Wines (UK)", 2017).

The reduction in the quantity of servers from 10 to 4, among which one is a backup system, has led to the reduction in costs that is required to maintain power usage and air conditioning within the facility up to 60 percent. This has improved the bottom line and reduced the carbon footprint. Better utilisation of applications has led them to perform efficiently thereby aiding in better customer service and inventory management. The costs required for replacing hardware components were also reduced by 69,500 dollars and the implementation of servers is easier and more efficient than the hardware components when necessary ("Liberty Wines (UK)", 2017).

The data reported to FinCEN by financial institutions had inconsistency in quality and lacked authentication and standardisation. The bureau was limited to simple routines and small datasets while trying to analyse its data. FinCEN failed to analyse large datasets and did not have the capability for proactive analysis and trend prediction. Various offline systems are utilised as a mode to report data to various agencies. The reasons provided above had a combined role in causing issues for FinCEN in detecting new and emerging threats faster and help in disposing criminal organisations (“FinCEN.gov”, 2017).

FinCEN needs to upgrade its IT infrastructure, analytical capabilities and database to achieve its mission. An upgraded analytical system is required to collect and analyse data more efficiently from multiple sources and facilitate the federal, local and state law enforcement and regulatory authorities with those data. The analysts of the company should have better analytic and examination capabilities. The databases of the company are upgraded from its conventional legacy systems to FinCEN’s new and advanced System of Record. The improvement in the IT infrastructure will allow the organisation to collect process and store FinCEN reports electronically (“FinCEN.gov”, 2017).


Financial intelligence is solely dependent on the level of effectiveness in analysing a data that is required to recognise patterns and relations that help in revealing criminal activities (“FinCEN.gov”, 2017).

The ability to identify patterns and relationships is critical to national security, as it has improved the ability to identify individuals involved in financing terrorist activities or those who fund criminal activities and disrupt criminal activity before they occur (“FinCEN.gov”, 2017).

Some of the recent financial crimes that has been detected and disrupted by FinCEN are as follows:

FinCEN fines Canton Business Corporation (BTC-e) on July 27, 2017 for intentionally violating US law for anti-money laundering (AML). The organisation is fined a total sum of 110 million dollars for providing financial aid to the ransomware Dark Net Drug Sales ("FinCEN Fines BTC-e Virtual Currency Exchange $110 Million for Facilitating Ransomware, Dark Net Drug Sales | FinCEN.gov", 2017).


FinCEN has penalised Merchants Bank of California on February 27, 2017 for intentionally violating several provisions of the Bank Secrecy Act (BSA) ("FinCEN Penalizes California Bank for Egregious Violations of Anti-Money Laundering Laws | FinCEN.gov", 2017).

Data Analytics plays a vital role in detection and prevention of criminal activities. It collects data and analyses the same for the detection of possible common patterns as well as relationships to data obtained from old criminal activities. In case, any similarity is found, it can prove useful for the detection and disruption of any future criminal activities (Cardenas, Manadhata & Rajan, 2013).

References

Berthon, P. R., Pitt, L. F., Plangger, K., & Shapiro, D. (2012). Marketing meets Web 2.0, social media, and creative consumers: Implications for international marketing strategy. Business horizons, 55(3), 261-271.

Bookbinder, J. H., & Dilts, D. (2016). Logistics information systems in a Just-In-Time environment.

Brown, C. W., & Nyarko, K. (2012). Software as a service (SaaS). Cloud Computing Service and Deployment Models: Layers and Management: Layers and Management, 50.

Bryson, J. M. (2012). Strategic Planning and Management. The SAGE Handbook of Public Administration, 50.

Caber, M., Albayrak, T., & Loiacono, E. T. (2013). The classification of extranet attributes in terms of their asymmetric influences on overall user satisfaction: an introduction to asymmetric impact-performance analysis. Journal of Travel Research, 52(1), 106-116.

Cardenas, A. A., Manadhata, P. K., & Rajan, S. P. (2013). Big data analytics for security. IEEE Security & Privacy, 11(6), 74-76.

FinCEN Fines BTC-e Virtual Currency Exchange $110 Million for Facilitating Ransomware, Dark Net Drug Sales | FinCEN.gov. (2017). Fincen.gov. Retrieved 3 September 2017, from

FinCEN Penalizes California Bank for Egregious Violations of Anti-Money Laundering Laws | FinCEN.gov. (2017). Fincen.gov. Retrieved 3 September 2017, from

Jadeja, Y., & Modi, K. (2012, March). Cloud computing-concepts, architecture and challenges. In Computing, Electronics and Electrical Technologies (ICCEET), 2012 International Conference on (pp. 877-880). IEEE.

Laudon, K. C., & Traver, C. G. (2013). E-commerce. Pearson.

Liberty Wines (UK). (2017). Libertywines.co.uk. Retrieved 3 September 2017, from

Liu, H., & Motoda, H. (2012). Feature selection for knowledge discovery and data mining (Vol. 454). Springer Science & Business Media.

Maddah, B., Yassine, A. A., Salameh, M. K., & Chatila, L. (2014). Reserve stock models: Deterioration and preventive replenishment. European Journal of Operational Research, 232(1), 64-71.

Shibl, R., Lawley, M., & Debuse, J. (2013). Factors influencing decision support system acceptance. Decision Support Systems, 54(2), 953-961.

Stadtler, H. (2015). Supply chain management: An overview. In Supply chain management and advanced planning (pp. 3-28). Springer Berlin Heidelberg.

Trnka, A. (2014). Big data analysis. European Journal of Science and Theology, 10(1), 143-148.

United States Department of the Treasury Financial Crimes Enforcement Network | FinCEN.gov. (2017). Fincen.gov. Retrieved 3 September 2017, from

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