CONCEPTS OF CLOUD COMPUTING AS USED IN BUSINESS COMPUTING

CONCEPTS OF CLOUD COMPUTING AS USED IN BUSINESS COMPUTING

1. On demand usage–businesses develop and use services on demand through cloud service providers. Some of the examples include Application Software Services such as the ERP systems (Harding, 2011).
2. The virtualization concept– Creation of a seamless kind of a performance in businesses within an emulated system hence lowers operational and upfront costs (Schaffer et al.,2009).
3. The multitenancy concept -Pooling of resources that can be able to serve multiple clients in terms of different virtual and physical resources assigned and reassigned according to consumer demand (NIST, 2011).
4. Software as a service-Businesses can access cloud online delivery of data storage(cloud storage)and online delivery of services(cloud services) (Vogel, 2009). For example, Amazon EC2.
5. Infrastructure as a service–Business users are able to leverage resources through implementation of virtual capabilities .For example, Amazon EC2 .(Spinola,2009)

Merits of cloud computing

1. It assures security and privacy: – Data can be accessed as everything is stored on cloud and risks are minimized via encryption and authentication (Mahesh et al., 2011) & (Jain, 2011).
2. Reliability-Files can be easily retrieved even in times of power failures and network downtimes a (Devaki, 2011)
3. It enhances flexibility-Provide organizations with a variety of services as it can rapidly meet the business demand (Sultan, 2011)
4. Cost reduction-Reduce cost on capital investments since there is an immediate access to software and hardware resources.
5. Ease of use and convenience-Eliminate administrative overhead and permits access from any geographical location (McAfee, 2011).

Demerits of Cloud Computing

1. Security and risk management: Static security measures are not effective in an open networked environment where threats and vulnerabilities are constantly evolving.
2. The loss of IT-control: A certain degree of control is lost since a significant part of IT infrastructure and services are moves to cloud providers who have the technical control for example, updates or new release of software.
3. Lack of Privacy: Data subject tends not to be cloud-specific, but rather draw from the broader set of privacy challenges posed by the internet and outsourcing arrangements
4. Difficulty in adapting cloud services to the specific businesses and its processes-The integration is complicated and they have to operate in a dynamic environment.

Roles of the key technologies in cloud computing

1. Scalability-It has enabled users’ to dynamically change the hired resources depending on their current need.
2. Efficiency in usage of resources- Large numbers of users can be served using virtual servers  which offers them an opportunity to work with various applications hence maximum utilization of the hardware capacity.
3. Pooling and sharing of resources-Computing resources are pooled in order to serve multiple users and are distributed and assigned  dynamically according to consumers’ needs.
4. Cloud computing technologies has allowed companies to carry out their main function in a new environment – This has open up opportunities for optimization of the business  processes and reduction of time to adjust and adapt  to changing market condition.

CONCEPTS OF DATA WAREHOUSING AS USED IN BUSINESS COMPUTING

1. Business intelligence concept-Businesses users can access the right information at the right time and transform it into smart decisions. For example Based on intelligent information, media companies can strategize a sustainable competitive edge, tap into new customer markets, retain existing customer base and increase operational efficiency (Giovinazzo,2003)
2. Metadata concept-It has enabled businesses in driving the accuracy of reports, validation of data transformation and ensures accuracy of calculations which has improved firms’ ability to create metadata and exchange the metadata across different vendors’ products(Soschin,2001)
3. Subject oriented-Provides decision makers in a business with all-encompassing picture of the organization and distinguishes it from the operational database which is product oriented.
4. Data-mart- Integration of multiple data marts ensures consistency and synchronization which streamline the business activities such as sales analysis and marketing

Merits of data warehousing

1. It enabled integrating of data from multiple sources
2. It facilitates the performance of new types of analytical analysis
3. It improves the turnaround time for analysis and reporting
4. It ensures removal of informational processing load from transaction-oriented databases
5. It has facilitated standardization of data across the organizationhaving single vision of the data.

Demerits of data warehousing

1. Requires information driven analysis-One spend time in understanding ,documenting business requirements especially in the modeling of the business goals and concepts.
2. Requires data integration-Designing of a data warehouse must present an integrated process that is efficient enough to manage large volumes of data and eliminate redundancies while integrating from various systems.
3. Complexity in data warehouse testing due to a high amount of dependency.
4. It is an expensive investment -Organizations need to spend most of their resources in training and implementation purpose requires involves high maintenance cost.

Role of key Technologies in data warehousing

1. It has enhanced better business intelligence–The multi-tiered data structures has increased quality and flexibility ranging from detailed transactions to high level summary information.
2. It has facilitated repositioning of products-facilitates the management of product portfolios by geographic regions and by comparing of performances of sales by yearly, quarterly in order to come up with production strategies.
3. Business process reengineering–It has provided business users access to information yielding to insights into business processes.
4. It provides a foundation for information systems and information technologies application development. For example Enterprise resource systems and the customer relationship management which provide competitive advantages.(Al-debei,2011)

CONCEPTS OF DATA MINING AS USED IN BUSINESS COMPUTING

1. Association rules-It that enables businesses to determine the groups that are compatible with others and predict the future behavior based on current behavior. For example ,the automated buying decisions.
2. It has enabled fraud detection-Fraud detection is made possible using predictive models to detect existing fraudulent behavior and identify customers who are likely to commit fraud in the future.
3. Data mining in cloud computing has allowed centralization of the management of software and data storage –It has assured a reliable, cost effective and efficient services for their users. (Patil, 2013).

                                                Merits of data mining

1. Facilitates making of strategic and tactical decisions to enable detection of key information.
2. Enables searching; attracting and retaining of customers thus reduce the risk of losing customers by offering special products.
3. Data mining models are reliable-The models are tested using statistical techniques in order to ensure predictions obtained are valid and reliable.
4. It enhances relationship with client –organizations improve customer service based on information obtained.

Demerits of data mining

1. Inaccurate information affects the outcome of decision making process hence not a reliable.
2. It relies on the amount of database and it takes some time to preprocess all that information
3. It lacks an appropriate security system hence put at risk users’ private information.
4. Requires high investment in high performance teams and staff training due to extensive work intensity.

Role of key technologies used in data mining.

1. Business intelligence technology-It provides historical, current and predictive views of business operations such as the market, supply, competitors etc.
2. It ensures efficiency in algorithms –data mining algorithms must be efficient and scalable to effectively extract information from a huge amount of data in databases.
3. Data mining query of languages and ad hoc data mining– allows users to describe ad hoc mining task that should be integrated with the data warehousing query language to optimize efficient and flexible data mining
4. Clustering- It specifies the similarity of the object that is assigned to one group using distance. For example businesses can cluster using partitioning or in a hierarchical method, (Hand, 2001).

References

Al-Debei, M. M., & Avison, D. (2011). Business model requirements and challenges in the mobile telecommunication sector. Journal of Organisational Transformation & Social Change, 8(2), 215-235.

Devaki, S. (2011). File storage trends in cloud computing era. Siliconindia, 14(8), 34-35.

Giovinazzo, W. A. (2003). Internet-enabled business intelligence. Prentice Hall Professional.

Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.i

Hand, D., & Manilla, H. P. Smyth,(2001) Principles of Data Mining.

Harding, C. (2011). Cloud Computing for Business-The Open Group Guide. Van Haren

Mahesh, S., Landry, B. J., Sridhar, T., & Walsh, K. R. (2011). A decision table for the cloud computing decision in small business. Information Resources Management Journal (IRMJ), 24(3), 9-25.

March, S. T., & Hevner, A. R. (2007). Integrated decision support systems: A data warehousing perspective. Decision support systems, 43(3), 1031-1043.

McAfee, A. (2011). What every CEO needs to know about the cloud. Harvard business review, 89

Mell, P., & Grance, T. (2011). The NIST definition of cloud computing

Midha, N., & Singh, D. V. A Survey on Classification Techniques in Data Mining. IJCSMS (International Journal of Computer Science & Management Studies) Vol, 16.

Patil, V., & Nikam, V. B. (2013). Study of Data Mining algorithm in cloud computing using MapReduce Framework. Journal of Engineering, Computers & Applied Sciences (JEC&AS), 2(7), 65-70.

Schaffer, H. E., Averitt, S. F., Hoit, M. I., Peeler, A., Sills, E. D., & Vouk, M. A. (2009). NCSU’s virtual computing lab: A cloud computing solution. Computer, 42(7), 94-97..

Soschin, D. (2001). Meta Data As on IT Platform The Strategy of Meta Data in Your Organization. JOURNAL OF DATA WAREHOUSING, 6(4), 30-40.

Spínola, M. (2009). The Five Characteristics of Cloud Computing. Retrieved March, 17, 2011.

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