Case Study Analysis: Cloud Computing and Big Data

Case 1: ‘Cloud computing for competitive advantage’byInnovation Nation, NZ Herald

1. What is Cloud Computing?

The origin theory of Cloud Computing began half a century ago. According to “The Challenge of the Computer Utility” by D.F.Parkhill (1966), computers have become powerful enough to provide information and services to everyday living, but inconveniently large and expensive devices have forced people in the future to use them in the form of remote access. So this is where the concept of ‘Utility Computing’ comes in, and later on, it became the predecessor of Cloud Computing.

Technically, Cloud computing is a combined product of Distributed Computing, Parallel Computing, Utility Computing, Network Storage Technologies, Virtualisation, Load Balance, High Available and other traditional computer and network technologies.

Cui (2018) has described that in previous literature “Cloud Computing Services Research Report”, the ‘Cloud’ is a colloquial term used to describe a service that utilises the concept of using server-based tools and data storage that can be accessed from anywhere, anytime through the internet. Cloud Computing is defined as delivery of computing resources or services via the internet on-demand, especially servers, computing power, storage, databases, network, software and data analysis, etc. (p. 4)

2. Competitive advantages that may present from adopting cloud computing in organisations.

Convenient and Cost Saving

There are many internationally well-known cloud providers such as Google, Amazon, and Microsoft to choose from and when applications are deployed to the cloud, those hardware and software headaches will be solved by professional teams from cloud service providers. Users can purchase services according to needs, and can even charge exactly by usage. This can save the costs on the IT department of the organisation, the overall utilisation of resources will be significantly improved and also the on-demand expansion of resources is done automatically by Cloud provider.

Virtualisation

Cloud computing includes virtualisation technology, users do not have to pay attention to the specific hardware entities for applications, only need to select a cloud service provider and register and login cloud console to purchase and deploy services as needed (such as cloud server, cloud storage, APIs,.)and then just simple configuration for the application to be ready to use. This made the development process simpler and easier than it ever was traditionally.

Database Management and Security

Cloud computing has big advantages on database management and disaster tolerance/recovery. Cloud service providers have professional management and technical teams to guarantee the security of the database. For example, most people will keep their money in the bank rather than build their coffers, which is the case. From the perspective of cost, technology, and security, cloud computing can provide service for low cost and high-security guarantee.

3. Cloud Computing allows small business to access IT services that historically would have been the domain of larger organisations

Traditional application development is becoming more and more complicated: need to support more users; need more computing capacity; need a more stable security, etc., and in order to support the growing demand, enterprises have to buy all kinds of hardware (servers, storage, bandwidth, etc.) and software (database, middleware, etc.), and also need to establish a complete operational team to support the normal operation of the equipment or software, the maintenance work, including installation, configuration, testing, operation, upgrading, and ensure the safety of the system, etc. People will find that the cost of supporting these applications becomes very large, and their cost will increase as the number or size of the applications increases. This is the reason why even in large organisations with great IT departments, users continue to complain that the systems they use are not meeting their needs.

For small businesses and individual entrepreneurs, the cost of developing applications is even more unacceptable. But Cloud computing allows small businesses using shared hardware and cloud services just like renting a car or a house, only need to pay as much as used or sign a long term contract with the cloud provider to get a better deal.

4. How will Cloud Computing be able to handle the emergence of the Internet of Things（IoT)

First, Cloud Computing and IoT are conceptually different. The second is they mutually reinforcing. The IoT realizes the interconnection among billions of objects in the world, closely connecting physical entities in different industries, regions, applications and fields according to their internal relations, and connecting and interacting with huge quantities of objects, from screws and pencils to airplanes and ships.

IoT is divided into three layers (Abdmeziem 2016):

1. Application layer
2. Network layer
3. Perception layer.

The future of the IoT should be Cloud + Terminals, with large-scale deployment of sensor networks, a variety of terminals will like water in the ocean, distributed to various infrastructure to collect information, then sends the information to the cloud computing and processing through calculation and finally to the application layer for different areas of support services.

Cloud computing provides users with a new high-efficiency computing model that combines the convenience and affordability of Internet services with the power of large-scale devices.

Resources are concentrated in the data center on the Internet, which provides centralized services at the application layer, platform layer and infrastructure layer to solve the inefficiency caused by the fragmentation of traditional IT systems. Cloud computing emphasizes the aggregation, optimization, dynamic distribution and recycling of information resources, aiming to save informatization (Rogers 2000) cost, reduce energy consumption, reduce the burden of user informatization, and improve the efficiency of the data center.

The original intention of cloud computing is to solve specific large-scale data processing problems, so the industry considers to be the best choice to support the “back-end” of the IoT. Cloud computing provides back-end processing power and application platform for the Internet of things.It brings a new computing and service mode to the development of the Internet of things. It is to build data centers or supercomputers through distributed computing and virtualisation technologies, and provide data storage, analysis and scientific computing services to technology developers or enterprise customers in a leasing or free manner. The “back-end” construction of the IoT should start from the Internet and industry cloud.

Case 2: ‘A dip in the Big Data pool’byInnovation Nation, NZ Herald

1. What is Big Data and what sources does it come from?

Big Data refers to the collection of data that cannot be captured, managed and processed by conventional tools within a certain period. It is a massive, high-growth and diversified information asset that needs new processing mode to have stronger decision-making power, insight and discovery power and processability.

The term Big Data comes from “The third wave” (1980) by Alvin Toffler, in the book A. Toffler predicted that Big Data would become “the grand wave of the third wave”.

It wasn’t until about 2009 that “Big Data” really became a buzzword in Internet technology. McKinsey & Company, the world’s leading management consulting firm, released a report titled “Big Data: The next frontier for innovation, competition and productivity” by J. Manyika in May 2011. The report says, Big Data refers to the “scale is beyond the typical database software, storage, management, and it can get analysis ability of data sets”, the report put forward the idea of Big Data collection and analysis, and will produce the influence of the large datasets, the key technology and application field are detailed analysed.

2. How the use of Big Data might provide business values for two different organisation domains: Business and Medical/Health-care

Business:

Big Data helps e-commerce companies to recommend products and services to specific user groups, tourism websites/travel agencies to provide tourists with preferred travel routes, second-hand market buyers and sellers to find the most suitable trading targets, and users to find the most suitable purchase period, merchants and the most favorable price;

Big data helps enterprises improve the pertinence of marketing, reduce the cost of logistics and inventory, reduce the risk of investment, and help enterprises improve the accuracy of advertising;

Big data helps social networking sites to provide more accurate friend recommendations, provide users with more accurate corporate recruitment information, and recommend games they may like and commodities suitable for purchase(Barreda, Bilgihan, Nusair & Okumus 2015).

Medical/Health-care:

Among other technologies comes with Big Data, image recognition is also the most mature aspect of machine learning. In the medical industry, a large amount of image data can be learned and trained, to more objectively identify medical imaging case characteristics and more accurately identify pathogens.

Big data also helps medical institutions to establish disease risk tracking mechanism for patients, pharmaceutical enterprises to improve the clinical use effect of drugs, and AIDS research institutions to provide customized drugs for patients. (Austin & Kusumoto 2016)

3. Three challenges attached to Big Data and possible actions that organisations could take to address the challenges.

1. Lack of understanding of data resources and their value

The challenge in the age of big data is that once information is created, it’s hard to know where it should be used. The general public has not yet formed an objective and scientific understanding of big data, and insufficient understanding of data resources and their value utilisation in human production, life and social management. There are some phenomena such as blindly chasing the investment in hardware facilities, ignoring the accumulation of data resources and value mining and utilization.

Possible actions will be put more attentions on data security solutions and related products, enterprise development strategy should pay attention to strengthen cyberspace security protection, completes the critical information infrastructure, strengthening the data encryption, reinforce the intelligent terminal, to protect sensitive information under the background of Big Data.

2. Insufficient use of data analysis

The application of Big Data is in its infancy and its potential is far from being released. The construction and application level of data resources is not high, and users generally do not pay attention to the construction of data resources. Even institutions with data awareness mostly only pay attention to the simple storage of data, and seldom process and organize for the subsequent application requirements. Data resources are generally characterized by poor quality, lack of standards and regulations, and weak management ability. Data sharing across departments and industries are still not smooth, and valuable public information resources and commercial data are not open enough. It is difficult to effectively exploit and utilize the value of data.

It is necessary to extensively analyze the application and effect of products, target customer group data, various transaction data and pricing data, etc., and then decide whether to link the massive external consumer data with the massive internal operational data of the enterprise, so as to gain new insights in the analysis and improve operational efficiency.

3. Information security and data management

In the big data technology environment, the data presents the characteristics of dynamic change, semi-structured and unstructured data. For the unstructured data accounting for more than 80% of the total data, the non-relational database (NoSQL) storage technology is usually used to complete the capture, management and processing of big data. However, non-relational databases do not have strict access control mechanism and perfect privacy management tools. Existing privacy-protection technologies, such as data encryption and data desensitization, are mostly used for relational databases and have effects, which cannot effectively deal with the evolution of non-relational databases and are prone to privacy disclosure risks.

The Big Data security framework should include the following five core modules: data management, identity and access management, data protection, network security, and basic security. Therefore, possible actions to address this kind of security challenges will be consult with professional expertise to develop a security management plan, take data as the center, establish a perfect management system, strengthen the security protection of data use from the level of access control and data protection, and finally reinforce the security deployment of platform from network and foundation layer.

References：

• Parkhill, D. F. (1966). Challenge of the computer utility.
• Cui, J. (2018). Cloud Computing Services Research Report. Unpublished manuscript, Massey University, New Zealand.
• Abdmeziem, M. R., Tandjaoui, D., & Romdhani, I. (2016). Architecting the internet of things: state of the art. In Robots and Sensor Clouds (pp. 55-75). Springer, Cham.
• Rogers, E. M. (2000). Informatization, globalization, and privatization in the new Millenium. Asian Journal of Communication, 10(2), 71-92.
• Toffler, A., & Alvin, T. (1980). The third wave (Vol. 484). New York: Bantam books.
• Manyika, J. (2011). Big data: The next frontier for innovation, competition, and productivity. http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation.
• Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904.
• Arthur, L. (2013). Big data marketing: engage your customers more effectively and drive value. John Wiley & Sons.
• Barreda, A. A., Bilgihan, A., Nusair, K., & Okumus, F. (2015). Generating brand awareness in online social networks. Computers in human behavior, 50, 600-609.
• Austin, C., & Kusumoto, F. (2016). The application of Big Data in medicine: current implications and future directions. Journal of Interventional Cardiac Electrophysiology, 47(1), 51-59.
• Yu, C. H. (1977). Exploratory data analysis. Methods, 2, 131-160.
• Shapiro, C., Carl, S., & Varian, H. R. (1998). Information rules: a strategic guide to the network economy. Harvard Business Press.
• Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.