Importance of Big Data in Telecom Companies-Samples for Students

Question: Explore the Importance of big data in telecom companies. Answer: Introduction In the information era, decision makers have to use massive amounts of data in making decisions (Brynjolfsson, Hitt Kim, 2011). Big data defines to data sets that is big as well as high in velocity and diversity, which makes difficulty to handle them by using traditional techniques and tools. It is essential for the big data that solutions need to be provided for handling and extracting value from these databases. The scope of big data is wide as telecommunication companies are using big data to do things to understand the potential of latest product offerings, improving customer services, reducing churn of customers and forecasting network capacity and execute value-based network capacity planning. The project objective of this research will tell about the activities conduct in literature review section. The scope of this research is wide as it has provided many opportunities to the telecom companies. Project Objective The research objective is to explore the importance of big data in telecom companies as there are number of companies that use big data in its operation to manage the data. It is vital for the research to reflect the advantages and challenges faced by the telecom companies by using big data and how it can be reduced so that potential customers cannot get influenced adversely. Project Scope The scope of the research is extensive as the use of big data has numerous advantages. Big data has diversity, scale and distribution needs the utilization of new technical tools, analytics and architectures for enabling insights that release new sources of the value of business there are number of telecom companies that uses big data analytical tools and methods to analyze the data in an efficient manner. Literature Review As per Manyika, et. al., (2011), it has been considered that big data is referred as the datasets whose size is further than the capability of what distinctive software of database can detain, supervise and store. It has been analyzed that the technology has become more advanced and can be varied between countries, companies and industries. It has been analyzed that big data has specific five values which is helpful to increase the productivity of the employees within the telecom companies. As per Ericsson Consumer Insight, (2013), the primary value is dependent on the principle of letting data turn into more translucent. It has huge advantageous factor as it can be reachable to those it is relevant for and builds an efficient mutual perceptive between companies and customers. Secondly, the role of big data is huge in experimentation that enables company to discover for new things within the companies. These requirements can be covered anything from improving performance to multiplic ity among employees. Big data builds huge probabilities in the terms of segmentation. Thus, the third value included the improvement in segmentation that may influence how companies modify actions towards huge audiences. The automated system is another value of the big data that is made in case of lots of information and facilitates about how to handle things and work. It is essential for the companies to replace human decision making with automated algorithms (PWC, 2013). Finally, big data facilitates company to create and invent latest services and products in a way that was not done previously by companies. It has been analyzed that the companies of telecom have had right to use to wide bits of data with huge base of their subscribers that is linking daily to their network and services. The telecom companies are confining excess data volume in the context of making more calls and concerning more and more to internet through broadening their voice business to broadband. This strategy is advantaging from a huge variety of sources such as wide use of several internet broadband functions to the higher velocity in data generation. It has been found from the research of Bughin, (2016), that the big majority of 77% telecom companies has adopted it and have initiated schemes in the domains of sales and marketing. While 57% of telecom companies have utilized big data for consumer care and 41% of telecom companies using for competitive intelligence. It is mentioned in the research of Bughin that 36% of telecom companies are utilizing big data for network load optimization and 30% companies is tak ing use of it for supply chain optimization. It has been reported that 72% of companies have spent in big data to launch big data applications for specific domains such as fraud, marketing and sales and retention. It has been analyzed that big data has many advantages in telecom companies as it allows company to identify the errors instantly without making any mistakes. It is helpful to save the operation from falling behind. The foremost advantageous reason of big data for companies is recognizing the new or latest strategies instantly for competition. It can be more expensive term for the telecom companies to execute of Real time Big Data Analytics tools which will ultimately save extra costs of the company (Datafloq, n.d). It is able to reduce the burden on a companys entire IT landscape. It has been analyzed that there were more than 430 million phones shipped globally in 2013 and smart phones were half of them. In 2013, 342 MB of traffic had been developed by smart phones every month, while 820 MB of traffic was generated by tablets per month (Zimmerman, 2013). It is vital for the telecom companies to share data between users, cell towers and various centres of processing. This data can be transferred to various data centres for further use. MapR Streams is a latest messaging system which is quiet effective to transport wide amounts of data centres. It is an effective approach in which telecom companies can duplicates flows in a master slave between numerous of geographically distributed clusters. The most benefit factor of big data is enhancing recommendation engine in which the operators can develop effective and modified offer recommendations. It would be for remaining individual subscriber through uniting internal structure data from various platforms such as Twitter and Facebook (PWC, 2013). The recommendations engine can be enabled due to information on customer behaviour and preferences to match expected price and provide effective add-ons like actors add for fans and free audio book for commuters. At last, operators can lower the co sts of remaining subscribers and recognize cross and up selling chances to develop revenue per user (Waller and Fawcett, 2013). Daln and Dahlblom, (2014), explained that there are some recent examples companies that using big data which show that how unlike market actors utilize this kind of information. There is another example of Telestra Corporation that uses big data for attracting number of customers towards it services. This company has made a Net Promoter Score (NPS) attribution modelling approach. This approach is effective to determine customer meeting efforts and the efficiency of its marketing and services performance. With the help of big data, Telestra is recorded NPS diagonally 30,000 consumers on average (CMO, 2015). Apart from that the use of big data is not limited up to telecom companies it is used by many other sectors. Google is the latest example that comes under global company. It has access to the biggest pool of information internationally and utilizes their information imaginatively. The company has developed well efficient spelling programs in which user does not put effort to make c hanges in spellings; it would be turn into correct format automatically. Along with that there are other examples of Telecom Company that is Vodafone and Argyle Data which are considered as the top most companies in telecom. These companies are using the big data to prevent the impact of fraud on the telecom (Daln and Dahlblom, 2014). As per Elgendy and Elragal, (2014), that there is no uncertainty that by using correct tools and analyses, big data can recognize statistical patterns that can be utilized to forecast processes and behaviours in huge range of sectors. It has been analyzed that nig data has certain challenges which may impact the customers as well as the companies in adverse manner. There are many critics raised the concerns about dangerous connection to the practice of big data. Initially, there is a risk that the user needs to neglect to consider of this kind of information that some outlines recognized by big data might need causality (McAfee, Brynjolfsson, Davenport, Patil and Barton, 2012). There is a potential of big data to place communications services providers (CSPs) in higher place to with the conflict for customers ad build new revenue streams. This attitude would enable organization to get information about the customers taste, preferences and movements. However, many communications servi ces providers facing issues to entirely obtain the huge value from big data (Bughin, Chui and Manyika, 2010). The challenges come in the telecom industry due to velocity, variety and complexity. In the context of complexity, the user developed data mostly in unstructured form that brings complexity for the telecom companies. It has been found that the inheritance network and storage devices have not certain layout to keep data which can be suitable for superior analytics (Deloitte, 2015). Analytics may provide the unwanted results to the customers due to not filtering data in an appropriate manner. It has been found that every minute Australians spend more than 100 hours on seeing videos. According to Demchenko, De Laat and Membrey, (2014), data integration and quality generally considered as the largest chunk of time in the project of data analytical. As per Tripathy, (2017), a scale of terabyte data was measured as humongous for instance the data of Wal-Mart warehouse attained 1 terabyte in the year of 1992. In current terabytes scale data is considered as universal. Telecom data has developed rapidly since the time of 3G and broadband. This movement allows customers to share contents on the internet. The use of commodity hardware and open source in big data platform is pointed the above mentioned concern to a definite amount. It has been defined by Chen, (2016), the most challenging task in the project of data analytical is to recognize the correlations among the features or variables and expose the relationship between the features. In case of exposing in an adequate manner can make effective business decisions. The diverse source of information is the biggest cha llenge that may lead the telecom companies to thousands of variables (Tripathy, 2017). Many challenges has to face by telecom companies but it can be reduced in an effective manner by following certain in which first step should be brought data down to its exclusive set and decrease the amount of data to be controlled. In next step, telecom companies can control the power of virtualization technology. It would be effective for the companies in which the operations of telecom should virtualized the exclusive data set which would be effective for the multiple applications to reuse it again along with that the lesser data footprint can be kept on any self-sufficient storage device of vendor. According to Datafloq, (n.d. ), virtualization is considered as the secret weapon which can be used by telecom companies to fight the management challenge regarding big data. It has been analyzed that it is eventually altered into little data which can be managed like virtual data and it is possible due to reduce the data footprint, centralizing the management of the data set and virt ualizing the again use of same data and storage of the data. It would be easier for the telecom companies to improve data management in specific areas due to smaller data print. The major concern is data security and it can be better saved since the management is centralized. After managing the big data in an adequate manner company would require less time to process the data. Outcomes of data analysis will be more accurate (Davenport, 2014). It has been found from the article of Ryan Fuller that big data is not supportive in only managing the customers of the telecom companies but also providing effective services to the employees of the company. It has been found from Google that the decisions of people management should be not unlike than decisions of engineering. It has been evaluated that the key business decisions are required to be rooted in data (Bughin, 2016). By eradicating the prejudice from the decisions of people management using analytics, Google Company has promoted productivity more effectively in comparison of policing time surfing of employees on the web. There are some examples provided by Ryan Fuller that how people analytics would be helpful for the managers of telecom companies to make a productive work culture. The role of big data facilitates to reduce distraction of organization and increase the efficiency, it has been found that high tech company delivered weekly reports that is personalized and it send to both employees and managers. The reports permitted both parties to look trends in their work patterns and recognize the issues that may lead the organization into distraction from key initiatives (Fuller, 2014). As per the report of Bughin, (2016), there are two kind of investment in big data such as investment in IT and on top of recruiting new big data capabilities. Investment in IT such as architecture, data bases like Hadoop, Pig, Apache and many more along with the applications. Investment in hiring big data capabilities include talents for running analytics and IT architectures. A devastating amount of telecom companies have required to recruit new big data explicit resources. Along with that it is vital for the telecom companies to use cases for improving the productivity of the organization (Manyika, et. al., 2011). It has been found that the average telecom companies reports that the contribution of big data in the telecom company profit is approximately 2.9%. As per Bughin this reported influence is better than the spent share in big data but vaguely inferior than the share of capex spent. It has shown that the big data takes to hardly the similar profitability in comparison of oth er projects in telecom companies. Conclusion In the limelight of above discussion, it can be concluded that the role of big data in telecom companies is wider as it allows grabbing the data regarding the preference, choices and most likely place of visiting of customers. Telecom companies has found number of opportunities to increase revenues. It has been found by analysing various reports and journal articles on big data that the return of it can be increased appropriately to the degree that companies follow to the practices of managerial and organization. It has been evaluated that company should focus on right IT architecture to gather the advantages of big data talents instead of spending money on big data use cases. Telecom companies should invest in several use cases to manage it an appropriate manner. Reference List Brynjolfsson, E., Hitt, L., Kim, H., 2011. Strength in numbers: how does data-driven decision making affect firm performance? Bughin, J., 2016. Reaping the benefits of big data in telecom.Journal of Big Data,3(1), p.14. Bughin, J., Chui, M. and Manyika, J., 2010. Clouds, big data, and smart assets: Ten tech-enabled business trends to watch.McKinsey quarterly,56(1), pp.75-86. Chen, C.M., 2016. Use cases and challenges in telecom big data analytics.APSIPA Transactions on Signal and Information Processing,5. 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