Understanding Big Data Characteristics
Bid data is very outsized and intricate data that is somewhat challenging to process, capture and evaluate using the existing computing set-up. (Russon, 2011) It is usually characterized by its volume, for example, Exabyte’s, terabytes, the rate at which it arrives, the variety, variability, inconsistency, and usefulness. Big data magnitudes are regularly increasing, presently vacillating from a little dozen terabytes to voluminous petabytes of figures in a distinct data set. Subsequently several of the complications linked to big data comprise of: capture, loading, exploration, distribution, visualizing and analytics. This research will be mainly based on the identification of challenges that arise from big data analytics. (Zikopoulos, 2011). Big data is utilized by many companies in and its need will continually grow as the world continues to develop in technology. Some of its current applications include, security: big social data acts as a primary pointer in impending action by a prospective agitator, business intelligence: that is in manufactured goods endorsement, and product anti-counterfeiting intelligence: that is a product worth reconnaissance and early exposure to hostile happenings and food-borne illness to mention just a few. It is also applicable in scientific research with it providing a new paradigm in research.
Examples of big data include; The Large Hadron Collider, social media, for instance, Twitter, the Square Kilometer Array and NASA’s Solar Dynamics Observatory. However, with all of its aptitude and potential, big data presents a lot of major complications for scientists and researchers. (Srinivasa, 2012) This research task will run a summary of the challenges faced in big data analytics. It will also recommend a theoretical outline for handling the challenges and improving big data analytics. This study will be helpful in bringing up-to-date researchers about the most contemporary advancements and challenges in big data analytics.
The significance of developing, enhancing and managing big data to enable companies and individuals to manage their work in an easier way has long been recognized by big data users. It is a need that has to be addressed sooner or later. A numerous number of big data users face challenges in identifying and sifting through relevant data and irrelevant data. The assortment, storage, and recovery of big data use lots of analytical methods. Conventional data mining systems undergo computational challenges when handling big data. There is a lot of storage and processing time issues. (LaValle, 2011).The algorithms suffer a lot of limitations in analysis and retrieval of data. These include, how to mass big data, how to transmit and share big data, how to clean and protect big data. (Chen, 2012). As such, there is an increasing need to develop better and more efficient algorithms that will manage big data successfully with very little limitations. To do this, a researcher needs to identify better and fully understand the challenges that arise when it comes to big data analysis. In successfully identifying the challenges, the researcher will recommend ways in which the challenges can be managed and ergo find a way to come up with a better program or algorithm to analyze big data more efficiently. (Lin, 2013).The following questions need to be specifically addressed: (Saunders, 2012)
- What are the basic and major challenges that arise when analyzing big data in various institutions and companies?
- Is there a way in which these problems can be classified for easier identification and management?
- What are the current methods used in analyzing and processing of big data?
- How efficient are the current methods in curbing the challenges arising from and processing of big data?
- How can the challenges and the current algorithms be manipulated in forming and coming up with bigger and better algorithms in the analysis of big data?
Challenges in Big Data Analytics
The eventual and long-term goal of this research is to propose a better way of managing the challenges that come up in analyzing big data and hopefully aid in coming up with a better algorithm to fix the imminent issues. Current innovations in the field of big data can only be successful and applicable if they can identify the problems, select their goals and work toward achieving them. (McAfee, 2012). Big data analysis can be defined as a way of managing, computing and effectively utilizing numerous amounts of data gathered by computer systems. The chief and principal purpose of this study are to propose a better way for innovators in the field of big data analysis to view the challenges and work towards their treatment. The objective of this study is therefore to outline a comprehensive and extensive overview of the literature and technological practices about big data management and as such propose ways in which big data can effectively be managed. (Glessne, 2015) The study particularly has the following sub-objectives:
- To identify and broadly recap an outline of the problems and challenges that industries and technological firms face when it comes to the analysis of big data.
- To identify and analyze the current methods used by industries and individuals in the analysis of big data
- To develop a way of classifying the challenges and problems arising from big data analysis starting from the most basic challenges to the major challenges.
- To propose ways in which big data can effectively be managed and analyzed.
The result of this study will turn out to be very valuable to the industry experts as well as associated system designers and software suppliers in developing better systems and software in the management and analysis of big data. (De Mauro, 2015).This study will also help all interested individuals in fully comprehending the challenges of big data analysis and will extensively help when it comes to research and innovations.
A preliminary literature review shows that past studies are primarily focused on understanding and explaining the concept of big data analytics. Numerous and wide-ranged studies have also been carried out in the identification of the adoption and use of big data and the supply chain. The previous researches have been aimed at identifying more and more applications for big data in various industries and giving good insights on the use of big data analytics. A few studies have also been carried out and aimed at providing solutions to problems faced in big data analytics. However, most of the problems are still not dealt with and continue to be a snag as limited headway has been achieved in categorizing the challenges and determining them methodically. Some of the studies, however, have come up with very good solutions to some of the problems faced. (Agrawal, 2011). In classifying and demarcating the encounters is a logical way, this study aims at providing much larger and more long-term solutions to the problems that are faced in the analysis of big data. Some of the methods that have been used in the previous studies will also come in very handy in the current research. In general, what is mislaid from the preceding studies is an inclusive and well-thought-out tactic in the treatment of big data.
Current Applications of Big Data
The key research process for this study will be assessment and analysis of collected works and theoretical modeling. The identifications of problems and categorization through a structured approach is the very first step towards solving and coming up with relevant solutions in the management of big data. This study is going first to review most of the available written works of literature relating to the analysis of big data and the most recent academic searches that have been carried out. (Taylor, 2015). By reviewing the classical algorithms used in big data analytics, challenges will be well identified and recognized starting from the basic challenges. Based on this understanding, a classification method will be developed to categorize the challenges for the purpose of analysis.
In the second stage of research, a survey will be carried out in various industries that use bug data in their works. Some relevant organizations and institutions will be contacted, and permission will be asked from the management on if research could be carried out within the premises. A survey will be carried out to identify the methods mostly used in the analysis of big data and the results well classified in a table. (Glessne, 2015). During the survey, we will be able to identify the most commonly used methods and the gaps that they have. Each algorithm used in the analysis will be classified, and beside it, all the problems that it is not able to solve when it comes to analysis will also be classified.
During the survey stage, questionnaires will also be handed out to individuals’ well-versed in the analysis of big data which includes the software and algorithm developers in the identified organizations to ask them about the challenges that arise in big data analytics in the various companies. (Saunders, 2012) Software developers will help in the identification and classification of the bigger and major challenges faced in big data analysis. Finally, once the classification of the problems has been carried out, and the analytical techniques have been identified, a theoretical framework for the most effective management and optimization of big data will be carefully and concisely outlined.
References
Agrawal, D. D. S. &. E. l. A. A., 2011. Big data and cloud computing: current state and future opportunities. Proceedings of the 14th international conference on extending database technology, March, pp. 530-533.
Chen, H. C. R. S. V., 2012. Business Intelligence and Analytics; From big data to big impact. MIS Quarterly, 4(36), pp. 1165-1188.
De Mauro, A. G. M. &. G. M., 2015. What is big data? A consensual definition and a review of key research topics. AIP conference proceedings, 1644(1), pp. 97-104.
Glessne, C., 2015. Becoming qualitative researchers: An introduction, s.l.: Pearson.
LaValle, S. L. E. S. R. H. M. K. N., 2011. Big data analytics and the path from insights to value. MIT Sloan management review, 2(52), p. 21.
Lin, S. &. Y. E., 2013. Challenges of big data analytics. International Symposium on Grids and Clouds, March.
McAfee, A. &. B. E., 2012. Big Data; The management revolution. Havard Business Review, 10(90), pp. 60-68.
Russon, P., 2011. Big Data Analytics. TDWI best practices report, fourth quarter, Volume 19, p. 40.
Saunders, M. &. L. P., 2012. Doing research in business & management: An essential guide to planning your project, s.l.: Pearson.
Srinivasa, S. B. V., 2012. Big data analytics.In Proceedings of the First International Conference on Big Data Analytics BDA. pp. 24-26.
Taylor, S. B. R. &. D. M., 2015. Introduction to qualitative methods: A guidebook and resource. S.l.: John Wiley & Sons.
Zikopoulos, P. &. E. C., 2011. Understanding big data: Analytics for enterprise class Hadoop and streaming data, s.l.: McGraw-Hill Osborne Media.
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