Importance of Big Data Analysis in Telecom Companies

This research explores the importance and challenges of big data analysis in telecom companies. It discusses how big data can be used to improve productivity, customer service, and network capacity planning. The advantages and disadvantages of big data in telecom companies are also discussed. The research includes a literature review and examples of telecom companies using big data.

Running head: Big Data Analysis 1
Business Research

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Contents
Introduction.................................................................................................................................................3
Project Objective.........................................................................................................................................4
Project Scope...............................................................................................................................................5
Literature Review........................................................................................................................................6
Conclusion...................................................................................................................................................7
Reference List..............................................................................................................................................8
Appendix.....................................................................................................................................................9

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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.

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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.

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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.

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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 multiplicity 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

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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 taking 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
company’s 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

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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 costs of remaining subscribers
and recognize cross and up selling chances to develop revenue per user (Waller and Fawcett,
2013).
Dalén 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 changes 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

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companies are using the big data to prevent the impact of fraud on the telecom (Dalén 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 customer’s taste, preferences and
movements. However, many communications services 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

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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 challenge 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 virtualizing 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

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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

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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 other projects in telecom companies.

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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.

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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.
CMO, 2015. How Telstra is applying data analytics to customer experience. Available at
https://www.cmo.com.au/article/574112/how-telstra-applying-data-analytics-customer-service/
Accessed on 25th April, 2018.
Dalén, C. and Dahlblom, F., 2014. Big Data in the Telecom Industry. Master Thesis in
Marketing Spring 2014 , Stockholm, available on: http://arc.hhs.se/download.aspx?
MediumId=2120.
Datafloq, n.d. The Advantages and Disadvantages Of Real-Time Big Data Analytics. Available
at: https://datafloq.com/read/the-power-of-real-time-big-data/225 Accessed on 25th April, 2018.
Davenport, T., 2014. Big data at work: dispelling the myths, uncovering the opportunities.
harvard Business review Press.
Deloitte, 2015. Opportunities in Telecom Sector: Arising from Big Data. Available at:
https://www2.deloitte.com/content/dam/Deloitte/in/Documents/technology-media-

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telecommunications/in-tmt-opportunities-in-telecom-sector-noexp.pdf Accessed on 25th April,
2018.
Demchenko, Y., De Laat, C. and Membrey, P., 2014, May. Defining architecture components of
the Big Data Ecosystem. In Collaboration Technologies and Systems (CTS), 2014 International
Conference on (pp. 104-112). IEEE.
Elgendy, N. and Elragal, A., 2014, July. Big data analytics: a literature review paper.
In Industrial Conference on Data Mining (pp. 214-227). Springer, Cham. (grnuine).
Ericsson Consumer Insight, 2013. Personal information economy: consumers and the evolution
of commercial relationships, Sweden: Ericsson.
Fuller, R. 2014. How Big Data Will Change Everything About Managing Employees. Available
at: https://www.entrepreneur.com/article/236030 Accessed on 25th April, 2018.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H., 2011.
Big data: The next frontier for innovation, competition, and productivity.
McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J. and Barton, D., 2012. Big data: the
management revolution. Harvard business review, 90(10), pp.60-68.
PWC, 2013. Benefiting from big data &A new approach for the telecom industry. Available at:
https://www.strategyand.pwc.com/media/file/Strategyand_Benefiting-from-Big-Data_A-New-
Approach-for-the-Telecom-Industry.pdf. Accessed on 25th April, 2018.
Tripathy, C. C. 2017. Challenges in telecom big data analytics. Available at:
https://www.linkedin.com/pulse/challenges-telecom-big-data-analytics-chandan-c-tripathy
Accessed on 25th April, 2018.

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Waller, M.A. and Fawcett, S.E., 2013. Data science, predictive analytics, and big data: a
revolution that will transform supply chain design and management. Journal of Business
Logistics, 34(2), pp.77-84.
Zimmerman, G. 2013. Leveraging the Big Data Advantage You Already Have. Available at:
https://www.communications.neustar/blog/leveraging-big-data-advantage. Accessed on 25th
April, 2018.

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Appendix
Figure : Telecom Operators
Figure2: Telecom Big data analytics framework