Discussion

Discuss about the Big Data Anaytics.

Big data analytics gather large amount of data and successful insights and specific patterns on them. This uncovering of information can help the companies or organizations involved make decisions that are more informed. This big data analytic concept has evolved in recent times and organizations or companies feel the need to collect large sets of information to analyze them and apply them in their business.

This report discusses about the meaning of big data analytics, and its working. It also covers the way it is used and the benefits and disadvantages of the system.

This big data analytic concept has evolved very much from the ancient times and organizations or companies feel the need to collect large sets of information to analyze them and apply them in their business (McAfee and Brynjolfsson 2012, p5). In the year 1950, companies and organizations used to collect all the information and store them in a spreadsheet to analyze them, which took too much time to implement and lacked the ability of fast implementation. With the introduction of big data, efficiency and speed is improved to a much extent.

Big data refers to the acquisition of large data that are very large to be accessed by normal data processing applications. The challenges on big data are storage, analysis, search, transfer and sharing. Big data analytics refer to the term of capturing predictive data or user behavior analytical data that extracts information from a particular skill set of varying sizes.

The data sets grow rapidly due to the sensing of information from various devices. Since 1980, the technological per-capita has increased 40 times in the modern era. Every day, (2.5*10^18) 2.5 Exabyte of data is being generated. “From 2005 to 2020, the global data volume will increase by a factor of 300” (Hu et al. 2014, p1). To handle such large amounts of information, relational database management system (RDBMS) or desktop statistical packages lags behind to gather such amounts of data, as they need to be handled in hundreds or thousands of servers.

According to Gartner and the most of the industries, the big data is defined by the 3Vs model (Fahad et al. 2014, p269). Big data is said to be high volume, high variety information and high velocity of assets gathering that made an innovative process of information gathering to enable automation of process and making of decisions.

Big data analytics is used to store and process large amounts of data that cannot be successfully analyzed by small data applications. However, there are large amount of technologies that big data analytics provide help in certain sectors.

• Data management is needed in a company or an organization to analyze the data flow. The data required is supposed to be very high quality in nature and specific before it can be sent for analysis (Singh and Reddy 2015, p14). However, the data flow requires some application to manage these data and that is the reason of the implementation of a data management program.
• The data mining property provided by the big data analytics help to examine the data sets to discover certain specific patterns (Witten et al. 2016, p4).
• The open source network of Hadoop can successfully store large sets of data and run all the applications on hardware clusters (White 2012, p20). 
• In the process of memory analysis, big data can lead to efficient data processing by accessing the information from system memory rather than the hard disc. This helps in improving efficiencies and helps the company to run new and iterative scenarios.
• Big data uses predictive and statistical algorithms to determine the future outcome that are based on past data. Major applications in this technology are fraud detection or marketing.
• Text mining is another scope of big data analysis where texts from different sources are analyzed and new relationships and data is gathered from them.

The various uses of big data are used in mostly every industry as well as in government decisions.

• In many countries, the big data analytics is used to determine the outcomes of certain activities. For example, in USA, big data analytical information played a huge role in the re-election campaign of Barack Obama (Weber, Mandl and Kohane 2014, p1).
• Hotels and resorts need their customers to be happy with their experiences and that is the reason of adaptation of big data analytics that helps to give predictive analysis if some problems have occurred to let the customers know.
• Big data analytics is also being adapted in the health care systems. The effective information about the patients likes health record or medical plans are quickly analyzed and treatment is applied immediately without wasting time.
• Retail industry also uses the big data analytical information as the retailers get successful insights from the customer experiences and they can apply that insight to promote new products to specific audiences.

Many benefits are provided by big data analysis over data science. Data science is a way of applying statistical and mathematical knowledge to get certain insights to find patterns and information.

• The information gathering from customers is achieved in a very fast process by applying big data in the system. According to the searches and preferences, the customers are shown lucrative offers, which they are guaranteed to buy, and this helps to enhance the finance of the company.
• Predictive analysis is also provided by big data analytics, as the future outcome of an activity can be determined (Chen, Chiang and Storey 2012, p1175 ).
• Big data also informs about the effective threats in a system and any vulnerable data present. This sensitive information cannot be handled by data scientists.
• Predictive analysis allows the effective usage of information and helps in the development of smart cities.

Big data analysis is used in almost every industry. However, it poses some disadvantages that are required o be addressed.

• Correlation algorithm is the most important aspect as big data analysis is done by correlating. However, not every variable are correlated and this might lead to misinformation (Chen and Lin 2014, p517). For example, between the span of 2000 and 2009, the number of divorces and the consumption of margarine both decreased. This does not imply that they are linked with each other.
• The analytical data are always not secure and this is the reason of data breach.
• The data required to be analyzed is not always present in the system. They have to be accessed through a firewall and this requires some technical knowledge.
• Data collection is a major part where any changes in the data can change the information as well (Davenport 2014, p14). For example, if the source of information is Google, then there is no consistency as the information change in a daily basis. Thus, information gathering is a challenge.

Conclusion:

Big data analytics often gather information from internal as well as external sources. The requirements of third party sources are also required to do the job. However, this effective searching process is risk-based and is very open which provides the possibilities of data breach in the system. Thus, it is concluded in the report that the advantages provided by the use of big data analytics can successfully change the process of doing things, but certain steps are to be taken so that the work done does not provide risks to the system.

References:

Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4).

Chen, X.W. and Lin, X., 2014. Big data deep learning: challenges and perspectives. IEEE access, 2, pp.514-525.

Davenport, T., 2014. Big data at work: dispelling the myths, uncovering the opportunities. harvard Business review Press.

Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A.Y., Foufou, S. and Bouras, A., 2014. A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE transactions on emerging topics in computing, 2(3), pp.267-279.

Hu, H., Wen, Y., Chua, T.S. and Li, X., 2014. Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, pp.652-687.

McAfee, A. and Brynjolfsson, E., 2012. Big data: the management revolution. Harvard business review, 90(10), pp.60-68.

Singh, D. and Reddy, C.K., 2015. A survey on platforms for big data analytics. Journal of Big Data, 2(1), p.8.

Weber, G.M., Mandl, K.D. and Kohane, I.S., 2014. Finding the missing link for big biomedical data. Jama, 311(24), pp.2479-2480.

White, T., 2012. Hadoop: The definitive guide. ” O’Reilly Media, Inc.”.

Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., 2016. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.