Data analytics is one of the main functioning of bi data technology. Managing the large amount of data and extracting the information from the large set of data cannot be managed by the traditional database management. In this case, the data analytics can be helpful. Data analytics is about gaining the useful information and conclusion after processing the large amount of data. The analysis of data can be done through some specialized software and tools (Marjani, Nasaruddin & Gani 2017, p.1). Data analytics is widely used in commercial industries in order to understand the business situations and market scenario (Chen & Zhang 2014, p.143). However, there are certain problems those are faced by the ICT professionals regarding the usage of data analytics (Buyya et al. 2015, p.75).
There are certain issues those can be faced by the professionals regarding this matter. In this paper some of the major problems are highlighted regarding the practice of data analytics (Chen & Zhang 2014, p.143). Some of the existing issues are- lack of experts for handling the data analytics, privacy and security of the data, managing the large amount of data. These are the existing problems regarding this technology. Some of the future challenges regarding this technology are-cost and privacy of the whole process. The discussions are made on the basis of highlighting the problems. In some cases recommendations are made to mitigate those problems. The conclusion has been drawn on the basis of the discussion and it can be said that mitigation of challenges in data analytics will make this technology effective for the industry.
Discussion
The identified issues will be discussed through gathering relevant information from different journals and articles. The articles will help to gain the insights of different issues present in the application of the data analytics in present days. It also elaborates the future problems in data analytics and big data technology.
Issue 1: Lack of experts in handling the data analytics:
The analysis of the data is a new technical innovation. The whole process is different from the traditional data managing process. However, it has been seen that the big data and data analysis are treading in the technology industry. However, for most of the professionals the idea behind the big data and data analysis is not clear (Hu et al. 2014, p.652). This indicates the lack of expert people handling the analysis of data.
The business solutions are becoming dynamic as well as the market situations and the demand of the consumers are changing. In order to keep the organization updated about the ongoing situation most of the commercial organizations are aiming for solutions derived by data analysis. However, they are taking this service from selected source of technology and IT companies, as data analysis is not practiced by many IT service providing organizations and companies. In that case, the monopoly regarding this technology has arrived (Lv et al. 2017, p.10). There is lack of professionals experts in providing the data analysis solution. It can be said that the main problem of data analysis that there are not many professionals who are aware about the usage and managing of data analytics tools and software.
In order to mitigate this problem the professionals are needed to give the training regarding data analytics. Some of the prominent IT services providing companies are giving their employees training regarding data analytics and big data technology (Chen & Zhang 2014, p.143). The increase of number of professionals will help to implement the result of data analytics in different sectors of industries.
Issue 2: Cost
The cost of operating the data analytics and implement the result of data analytics is both present and future challenge (Najafabadi et al. 2015, p.10). The operating cost of the data analytics is comparatively higher than the conventional data managing system. This technology is an emerging technology and is used by few of the companies. This makes the technology costlier as it requires specialized software and tools for the operations. However, this factor can be resolved through the improvisation of the tools and the software.
The implementation cost as well as the application cost of the data analytics can be higher than the traditional data management tool at initial stage (Najafabadi et al. 2015, p.10). However, this can be seen as the one time investment as the analysis will be a cost effective solution for the organization in many ways.
Issue 3: Quality of the analysis:
One of the main present challenges is the quality of analysis of the data. This factor is related to the lack of experts in data analytics filed. The quality of the analysis of data is dependent on the knowledge and the skill of the experts (Haldorai & Ramu 2018, p.300-314). The analysis of the data is done on the large amount of data or information. In the chunk of information there are two types of information. One type of data or information is important for the business and rest are not useful or unnecessary (Chen & Zhang 2014, p.143). A good professional will take the useful information from the data chunk and will conduct the analysis on the specified information. This will help to generate meaningful and useful result.
It has been seen in most cases, the experts are unable to identify the useful information from the chunk of data (Rumsfeld, Joynt & Maddox 2016, p.350). Sometimes overflow of information unable to produce the expected report. Most of the business decisions are taken on the basis of the result of the data analytics. On that case, the wrong analysis can mislead the direction of the business.
Apart from misleading the direction of the business, the wrong analysis also increases the cost o the whole operation. This maximizes the overhead cost and the requirement for resources which is not desirable for any organization or company.
Issue 4: Security of the Data
Maintaining the security of the data is important as most of the information used for the analysis of data are sensitive and confidential (Najafabadi et al. 2015, p.10). In that case, the security and confidentiality of the data is needed. In the data analysis the information and the data are taken from numerous resources. In that case, there is a chance that the data are vulnerable to threats and data breaching can be happened form the software and tools used for the data analysis.
There are certain ways for the mitigation of the security threats in case of data analytics. In certain cases, data analytics is used by the government and national organizations. In this case, the chances of data breaching is not desirable (Yin & Kaynak, 2015, p.143). There are ways and the technology those are helpful to mitigate the security threats in the data analytics.
Issue 5: Complexity of data
The complexity of the data analysis is another issues. The data objects are complex and varied in the quality. The inter relationship of different types of data are complex structures and patterns. This makes the perception of the data analysis along with the understanding the computational complexity along with the representation and understanding more challenging (Kache & Seuring 2017, p.10).
The traditional data analysis using the data management system has become more complicated using the data analytics (Suthaharan 2014, p.70). In this case, the main problem is regarding the way for the formulation or the quantitative description of the complexity of the acquired data (Najafabadi et al. 2015, p.10). However, the knowledge regarding the abstraction and complexity can help to gain the understanding regarding the analysis. In order to do this designing of algorithms and computational models are important.
Conclusion
The discussion is regarding data analytics and its current and future challenges. In order to conduct the gathering of the information different journals and articles are used for the retrieval of information. The issues and the challenges are identified. The briefing is made regarding the respective challenges. It has been observed that all the issues and the problems are interrelated to each other. Though in this case, the discussion regarding different issues has been done in a separate way. The main identified issues are the security issues, handling the complexity of data, lack of professional experts and the cost of maintaining the data analytics system along with maintaining the quality of analysis.
The problems have been discussed from the different perspectives of different journals and articles. However, in some of the cases the mitigation plan has been discussed. It can be said from the above discussion that there is a need of improvement of data analytics concept. Many business organizations are using data analytics tools in order to understand the current business situation. However, the practice of data analytics has gained the popularity but is not practiced by most of the organizations. The highlighted issues are the main factors for not using the data analytics over the use of the conventional management of data. However, it can be said that the greater benefits from the data analytics along with the advanced technology will help to enhance the use of data analytics technique in a more effective way.
References
Buyya, R, Ramamohanarao, K, Leckie, C & Calheiros, R.N 2015, December. Big data analytics-enhanced cloud computing: Challenges, architectural elements, and future directions. In Parallel and Distributed Systems (ICPADS), 2015 IEEE 21st International Conference on (pp. 75-84). IEEE.
Chen, C.P & Zhang, C.Y 2014. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, pp.314-347.
Gahi, Y, Guennoun, M& Mouftah, H.T 2016, June. Big data analytics: Security and privacy challenges. In Computers and Communication (ISCC), 2016 IEEE Symposium on (pp. 952-957). IEEE.
Haldorai, A. & Ramu, A 2018. The Impact of Big Data Analytics and Challenges to Cyber Security. In Handbook of Research on Network Forensics and Analysis Techniques (pp. 300-314). IGI Global.
Hu, H, Wen, Y, Chua, T.S & Li, X 2014. Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, pp.652-687.
Kache & Seuring, S 2017. Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management, 37(1), pp.10-36.
Lv, Z, Song, H, Basanta-Val, Steed, A & Jo, M 2017. Next-generation big data analytics: State of the art, challenges, and future research topics. IEEE Transactions on Industrial Informatics, 13(4), pp.1891-1899.
Marjani, M, Nasaruddin, F & Gani, A 2017. Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access, 5, pp.5247-5261.
Najafabadi, M.M, Villanustre, F, Khoshgoftaar & Muharemagic, E 2015. Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), p.1.
Rumsfeld, J, Joynt, K.E & Maddox, T.M 2016. Big data analytics to improve cardiovascular care: promise and challenges. Nature Reviews Cardiology, 13(6), p.350.
Suthaharan, S 2014. Big data classification: Problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Performance Evaluation Review, 41(4), pp.70-73.
Yin, S & Kaynak, O 2015. Big data for modern industry: challenges and trends [point of view]. Proceedings of the IEEE, 103(2), pp.143-146.
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