Technology Evaluation - Big Data Analytics - Research Paper Example

Introduction: Big data analytics is a process of organizing, collecting and analyzing an enormous set of data, for purposes of discovering useful information and patterns. Big data analytics does not only help in understanding information that is contained within a data, but it also helps in the identification of data that is important to the business organization, and for the future business decisions. Zadrozny and Kodali (2013) maintain that big data analysts normally want the knowledge that emanates from the analysis of the data under consideration. Business organizations are continually looking for methods of finding actionable insight and knowledge in the data under their possession. Furthermore, Sathi (2012) explains that most enormous data projects normally originate from the desire of answering specific questions relating to business operations. These questions might include on how to increase sales, effectively manage the organizations human resources, and methods of cutting organizational costs. Liebowitz (2013) therefore maintains that with the right platform of big data analytics, an organization will gain the capacity of increasing efficiency, boosting sales, improving operations, risk management, and customer service. This paper is an evaluation study plan, aimed at understanding the benefits and costs of using the technology of Big Data Analytics. In meeting the objectives of this paper, the researcher will identify the research questions that this technology aims to solve, methods used in conducting the study, the limitations, and timeline of events. Research Questions: As large business organizations initiate measures of improving the security system of their computer networks, a common method is being used to employ security staff, as well as establishing a central Information Technology security system (Bethlehem and Biffignandi, 2012). Business organizations have increased an emphasis on the physical security, personal safety, as well as the protection of their important critical infrastructures. This has led business organizations and higher learning institutions to develop a wide range of job titles, reporting relationships, segmented job functions, for purposes of describing how to approach the privacy of data, and protecting personal information, intellectual properties, and security of networks and computer systems (Kibert, 2012). Big Data Analytics concerns itself with analyzing enormous volumes of data, and this becomes a security issue. This is because experts would have to find ways in which this system does not compromise the privacy and security of the information that is being analyzed. On this basis, this evaluation study plan seeks to answer the following questions, Is information derived from Big Data Analytics confidential? What are the protective mechanisms adopted to ensure that information derived from Big Data Analytics are protected from third parties? Is this information well documented, with the most relevant information properly stored? How available is information derived from Big Data Analytics? Is it possible to tamper with the authenticity of information emanating from Big Data Analytic procedure? Methods: In reviewing this technological innovation, the method used is the technological acceptance model. This is a type of information systems theory that explains how users would use and accept a given technology. According to this model, when users of technology are presented with a new technology, there are a number of factors that will play a role in influencing their decisions on when and how they would use the technology under consideration (Krynicki, 1995). One of the major factors influencing their decisions is the perception of the usefulness of the technology under consideration. This refers to a situation whereby an individual believes that by using a particular technology, then his or her job performance would increase. For example, Sue and Ritter (2012) maintain that one of the advantages of Big Data Analytic is that it makes information to be easily available for managers and business leaders (Ohlhorst, 2013). This is an important usefulness of this technology, and hence business leaders would seek to use this technology in analyzing information touching on their business operations. Through this method, the researcher aims at carrying out an electronic survey, for purposes of asking how useful this technology is to its users. It is important to denote that the questions contained in the survey forms, would aim at providing an answer on the five major research questions identified. Teo (2011) further gives another characteristic of technological acceptance model. Under this characteristic, a user would evaluate a given technology based on the perception on how easy it is to use the technology under consideration. Under this concept, a user would be attracted to a technological knowhow, based on the knowledge that it is not complicated, and it is efficient in meeting the objectives of the individual concerned. The researcher therefore aims at finding out whether Big Data Analytics is easy to use. In meeting this objective, the researcher would still rely on electronic surveys to collect data and information. The researcher would also use focus group discussion for purposes of collecting data that answers the above mentioned research questions. The people targeted for focus group discussion are students of information technology and computer systems. This is because they have the knowledge and the necessary information concerning the functionality of Big Data Analytics. Limitation: There are a number of limitations that the researcher would face while collecting data for this evaluation study. One limitation is that it would be difficult to verify the people filling out the electronic survey forms. It is important to understand that because the survey forms would be filled through the internet, anybody can fill them. This is despite the targeted population being information technology experts, and business personalities. Based on this fact, the researcher will have to verify the information gathered from the surveys. To verify this information, the researcher will have to look for any inconsistencies in the answers given. He will have to remove the inconsistent information, and use information that is consistent for the evaluation. Under focus group discussion, the researcher would be limited by time, and the number of participants wishing to participate. This is because the students might have limited time to discuss on the issues presented. Furthermore, the researcher might find it difficult to attract many participants, and this is because some might demand for money. Timeline of Events: The Set up Stage: This stage will take 2 days, and it involves analyzing the issues of evaluation and study. This will occur between the days of 28th of July, to 30th of July. The testing stage would involve collecting information through the methods identified in the methodology. This stage would take approximately one month, after the 1st of August 2014. The reporting stage on the other hand involves analyzing and recording the results of the study. This would take approximately one week. References: Bethlehem, J. G., & Biffignandi, S. (2012). Handbook of web surveys. Hoboken, N.J.: Wiley. Kibert, C. J. (2012). Working toward sustainability ethical decision-making in a technological world. Hoboken, N.J.: Wiley. Krynicki, M. (1995). Surveys. Dordrecht [u.a.: Kluwer Academic Publishers. Liebowitz, J. (2013). Big data and business analytics. Boca Raton, FL: CRC Press. Ohlhorst, F. (2013). Big data analytics turning big data into big money. Hoboken, N.J.: Wiley. Sathi, A. (2012). Big data analytics. Boise: MC Press. Sue, V. M., & Ritter, L. A. (2012). Conducting online surveys (2nd ed.). Thousand Oaks, Calif.: Sage. Teo, T. (2011). Technology acceptance in education: research and issues. Rotterdam: Sense. Zadrozny, P., & Kodali, R. (2013). Big data analytics using Splunk. Berkely, Calif.: Apress ;. Read More