Case Study On Cloud Computing And Data Analytics

Implementation and Competitive Advantage of Adopting Cloud Computing in an Enterprise System Environment

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Cloud Computing has the potential to reduce the expenditure of an organization significantly. Many organizations including public institutions are turning toward cloud computing to take advantage of new and evolving technologies, while at the same time meet their objectives regarding always-on service, flexibility and cost savings (Dinh et al. 2013).

St?edni škola a mate?ska škola, o.p.s. Litom??ice, a kindergarten school which houses 40 pre-school children and a secondary school where students can learn grammar or different courses on offer including graphic designers, shopfitters, cabinetmakers, hairdressers, and beauticians. Other two buildings are a university of the third age and an Academy of the third age. The primary objective of the organization was to streamline administration regarding human resources and expenses. As the organization houses three different buildings, therefore the aim was to connect all of the buildings with simple synchronization. The organization considered the idea of the idea of adopting a stand-alone cloud only solution for data back and data storage along with register, system document sharing and e-mail accounts. For data storage purpose and e-mail, the organization used Microsoft 365 service, which is a cloud service and charges nothing for educational institutions (Chandrasekhar, Zhang and Koh 2016). iŠkola is used as register system which charges an annual fee. Both the systems were in operation for six months to make the process smoother, and the transition to Microsoft Office 365 became quicker. It took only two months for the transition to data migration and training of the employees. Employees reported that utilizing the cloud services helped them to save time and teachers had more time to spare as they can work from anywhere. IT administrators focused on other aspects of IT operation as routine maintenance like data backup, and data storage became automated. The organization saved a lot of money as it saved costs from purchasing licenses, and brought about financial savings for the employees. Cloud computing opened up a lot of opportunities for the users while data synchronization among the buildings went smoothly (Maresova and Kacetl 2015).

The organization is a UK based SME which delivers IT solutions for the Oil and Gas industry. The organization has offices in the Middle East and UK with around 50 employees. The organization has two divisions – Engineering and Administration, of which Engineering is the bigger department. The company chose to migrate their application to Amazon EC2 with IaaS or Infrastructure-as-a-Service layer as the cloud service type (Perrons and Hems 2013). The reason for this selecting IaaS as the organization can migrate all applications to cloud without the need of any modification (Huo et al. 2016). Citrix XenApp and Oracle Database are two available Amazon Machine Images for the enterprise applications. After migration, the benefits that the organization enjoyed are the opportunity to administer outgoings and income, status improvement, more work satisfaction, the opportunity to develop new skills, and the opportunity for growth of the organization. Work satisfaction among support engineers increased as time-consuming work like the performance of switching backup, network support, and hardware support were automated as part of routine maintenance provided by the cloud vendor. Third party cloud infrastructure solutions minimize the variability of expenditure on electricity, the cloud pricing model has minimum monthly billing and upfront cost and facilitates cash-flow management for finance workers (Perrons and Hems 2013).

Cloud Computing in the Public Sector

According to Talia (2013), Big data is going to change the way business operate and compete in a fundamental way. Organizations that invest in and derive value from data analytics will have a competitive edge over others. A performance gap will generate as organizations integrate more and more relevant data, digital channels and emerging technologies will offer better delivery mechanisms and better acquisition, and technologies that enable faster and data analysis will continue to develop. Big Data can be described in four key characteristics – Volume, Velocity, Variety, and Value.

Ernst & Young (EY) uses big data analytics to create their value chain. The objective is to improve the effectiveness and efficient of every action and every decision. The analytics value chain starts by leveraging the essential techniques and tools to extract and manage relevant data from big data sources. Applications of analytics can vary from real-time decisions support for enterprises based on making predictions to historical reporting. The organization then drives change from the insights generated. EY follows a loop based method and it goes like this: Data is collected from different sources, then relevant data are selected to perform analytics (Ey.com 2016). Based on the analytics rules and algorithms are applied to create insights which are used to drive decisions. Decisions taken are used as feedback to manage data, and the loop continues. EY uses big data to revolutionize the way audits are conducted in continuous internal audit functions and external audit firms. With external and internal auditors combining analytics and big data, the organization found greater access to comprehensive industry data, to aid them in understanding the business, recognize issues and risks, and provide improved coverage and quality while delivering more business value. Insights and information that were only limited to members of the board now extend much beyond recording traditional financial transactional data in the general ledgers of the company and extends to data from voice, video, social media, email to texts of unmanaged data. Risk assessment can be easily overcome from the insights gained from the data (Ey.com 2016).

Actuate, a telecom company in Europe, wanted to leverage data it captured on a mobile device to able to use it for creating opportunities. With more accurate and relevant data about the activity of consumers, the company will be able to redesign its portal and give users an easy way to access the applications they use most. The company also wanted to use big data analytics to identify latest trends and then deliver them to the customers in a smart and intelligent way. Being updated on consumer activities, the company would be able to leverage the latest mobile technologies as supported by the network and inform the customer in case of any incompatibilities (Joshi 2015). The company aims to utilize the minute details to keep its web portals and applications fresh and are relevant to the interest of the customers, reduce customer churn and increase its revenue through the greater number of mobile application sales. The consumer data services sought a solution for this purpose that would consolidate all information in a single environment, which will enable reliable end-user self-service and ad-hoc analysis. The combined ActuateOne and Hadoop solution enables the company to report on usage, and provide information on KPI. Hadoop allows processing huge quantities of data to be kept in a minute way that is both performing and cost effective. Actuate carries the capability to report against Hadoop Big Data source, while it allows business clients to generate on-demand reports and analytics consisting of thousands of pages in few seconds, through a simple web interface with little training. At an IT level, ActuateOne solution fits right out of the box which saved the company from spending approximately US$ 150,000, and neither needed to build any additional process or software. The company claims that instead of putting their finger in the air, it now has clean data which the company can back up with confidence (Actuate.com 2016) 

References

Actuate.com. (2016). Telecom Hadoop Case Study. [online] Available at: https://www.actuate.com/download/casestudy/Telecom-Hadoop-Case-Study.pdf [Accessed 9 Oct. 2016].

Chandrasekhar, B.K., Zhang, Y. and Koh, L.S., Trend Micro Incorporated, 2016. Security system for cloud-based emails. U.S. Patent 9,275,242.

Dinh, H.T., Lee, C., Niyato, D. and Wang, P., 2013. A survey of mobile cloud computing: architecture, applications, and approaches. Wireless communications and mobile computing, 13(18), pp.1587-1611.

Ey.com. (2016). Big opportunities, big challenges. [online] Available at: https://www.ey.com/gl/en/services/advisory/ey-big-data-big-opportunities-big-challenges [Accessed 9 Oct. 2016].

Ey.com. (2016). EY Reporting: Issue 9 – How big data and analytics are transforming the audit. [online] Available at: https://www.ey.com/gl/en/services/assurance/ey-reporting-issue-9-how-big-data-and-analytics-are-transforming-the-audit [Accessed 9 Oct. 2016].

Huo, Z., Mukherjee, M., Shu, L., Chen, Y. and Zhou, Z., 2016, September. Cloud-based Data-intensive Framework towards fault diagnosis in large-scale petrochemical plants. In Wireless Communications and Mobile Computing Conference (IWCMC), 2016 International (pp. 1080-1085). IEEE.

Joshi, P., 2015. Analyzing Big Data Tools and Deployment Platforms. Int J Multi Approach Studies, 2, pp.45-56.

Maresova, P. and Kacetl, J., 2015. Cloud Computing in the Public Sector–Case Study in Educational Institution. Procedia-Social and Behavioral Sciences, 182, pp.341-348.

Perrons, R.K. and Hems, A., 2013. Cloud computing in the upstream oil & gas industry: A proposed way forward. Energy policy, 56, pp.732-737.

Talia, D., 2013. Toward cloud-based big-data analytics. IEEE Computer Science, pp.98-101.