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Name: Keshava Dilwali
UNI: KD2593
Embracing Big Data and Analytics in Telecom
A. Company Profile
M1 Limited is one of the three major telecom operators in Singapore providing mobile services and broadband
connectivity to over 2 million customers. Founded in 1997, M1 was the first operator nationwide to launch
data-driven services like LTE and high-speed fixed broadband networks [1]. Given their commitment to
becoming a key player in the digital value chain across South-East Asia, M1 is looking at new avenues to grow
their business. This paper discusses their opportunities for growth in the analytics domain and the framework
for Big Data adoption. Based on current industry trends, it also describes the benefits of a data-guided business
approach.
B. Opportunity for Growth
Organizations today have a remarkable opportunity to add value and drive innovation by embracing Big Data
and advanced analytics. Telecommunications is one such industry where the role of Big Data has the potential
to play an immense role in not just the growth but also sustainability of a company. Telecom companies have
been sitting on a rich source of data collected daily from a large customer base connecting to their network.
Over the last 10 years, consumers have become heavily dependent on their mobile devices and expect
ubiquitous coverage and fast data performance. Network operators are encountering an overwhelming
amount of data traffic that is forcing them to adopt data-centric technologies like LTE. This shift in the industry
from voice-driven business models to data-driven business models has provided telecom operators a green
field of opportunities to explore. A team of the right kind of skilled data professionals can help them spot
trends, give deeper insights into customer behavior, and optimize processes to create value faster than
conventional methods.
Big Data offers the opportunity to augment existing processes across the telecom business domain. This
enables cellular network performance to be monitored in real-time that can be used for optimizing traffic flow,
enhancing coverage, and ensuring Quality of Service (QoS). Customer behavior can be analyzed to identify any
fraudulent activity in real-time. It can also be utilized for personalizing user experience, developing new
products and services, and planning targeted marketing campaigns. Additionally, this information can be sold
to a third-party which would help create new channels of revenue [2].
Due to the nature of Big Data generation, there will be a substantial increase in the volume, velocity, and variety
of available data [3][4]. Given the explosive growth in mobile data users across the world, there is huge volume
of data generated each day. This comprises of information regarding network behavior, volume of data traffic,
network coverage parameters, cell handover data, call and text records, location-specific data among many
other data points [5]. The frequency of collecting these data points is high due to the large number of cellular
devices distributed across the network. Furthermore, this information is obtained in both structured and
unstructured formats, adding to the variety of data.
Big Data is still in early stages of deployment in the telecom industry. Fueled by the rise in mobile penetration,
and increase in analytics services and affordability of Big Data tools, Big Data in the telecom market is expected
to grow at a Compound Annual Growth Rate (CAGR) of 65%, as per the Novonous report [6]. Currently, telecom
makes up about 14% of the global Big Data market revenue, and is poised to become the largest contributor
to Big Data market revenue by 2020.

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Telecom operators worldwide have begun to focus their resources towards the new industry trend. Verizon
Wireless has been using mobile device data to pursue new offerings through a business unit called Precision
Market Insights. They generate revenue by selling information on user demographic, geographic, and general
interests while maintaining their privacy [7]. One of M1’s chief competitors, SingTel, has already formed a team
of data scientists that are developing analytical models to track and understand customer data that can be
channeled towards understanding consumer behavior and improving business processes [8]. These examples
and many more demonstrate how Big Data analytics projects are delivering value. Hence, it is imperative for
M1 to start utilizing data and analytics to stay competitive and create paths for business development.
Going forward, the telecom industry has started to explore new opportunities in the field of Internet of Things
(IoT) [9]. IoT is a dynamic global network of people and smart devices (things) which provides a platform
(internet) for them to collect and exchange data. As per a Cisco report [10], the number of connected things in
the IoT ecosystem are expected to grow from 9 billion in 2012 to 50 billion by 2020 at 2.7% penetration rate.
This will help provide telecom companies a source of revenue in the form of managing the connectivity
requirements of billions of objects. Moreover, the volumes of data generated by these objects is expected to
grow exponentially with IoT, thereby creating enormous opportunities for monetizing processes such as
tracking, storing, curating, and analyzing of this data.
Figure 1: Growth in number of connected objects and mobile penetration rate by 2020 [10]
C. Big Data Adoption
A telecom company like M1 looking to leverage and incorporate Big Data in their framework must avoid the
temptation to first construct a business problem and then search for the required data. This is a complicated
route where the firm might arrive at a suboptimal data set that does not solve the problem at hand, and worse,
deters them from avidly pursuing data analytics in the future. Instead, they must look at available internal and
external data repositories for patterns and correlations which can be then directed towards business
development. This information can be used for expanding their customer base and preventing churn. Only
after a level of data maturity and expertise is achieved, should the company start mining for the right data to
solve their business problems [3].

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The initial targets and expectations out of Big Data analytics must be reasonable. It takes several projects
before the company can start to see a momentous Return on Investment (ROI). Even companies with mature
data models only see modest revenue and cost improvements in the initial years. Research conducted by
McKinsey & Company [11] of more than 700 companies around the world showed that the profitability and
value-added benefits of data analytics resembled those experienced in early periods of the IT boom. The
average increase in profits from Big Data investments was found to be about 6% in the initial years, and rose
to 9% over a period of 5 years, with potentially larger future payoffs. Therefore, companies adopting a data-
driven approach must prepare a practical assessment of the short and long term impacts of this new technology
wave.
To obtain maximum benefit from Big Data, it is necessary to have analytics representation in the C-suite at the
company. It will facilitate the involvement of top level management in setting analytics strategy and focusing
on analytics projects. According to NewVantage Partners 2016 Big Data Executive Survey [12], there has been
a sharp rise in the number of firms that have appointed a Chief Data Officer (CDO) from 12% in 2012 to 54% in
2015. Increasingly across sectors, firms are understanding the need for a CDO with accountability and
authority. This helps companies develop a Center ofExcellence (COE) organizational approach, where analytical
resources are located within business units but have central COE guidance [4].
Figure 2: Rise in number of firms with a Chief Data Officer (CDO) [12]
The company must be ready to invest considerably in the requisite talent and tools. There are generally 4 types
of analytics roles in a company:
- Data Analysts, who analyze and build a narrative through available data,
- Data Scientists, who develop core models used for data analysis,
- Business Analysts, who act as the communication channel between data professionals and business
users, and,
- IT Experts, who build and maintain data resources.
Moreover, it is not just important to get the right kind of people but also to get enough of the right kind of
people [13]. Much of the company invariably deals with data. Therefore, a few professionals in analytics
positions would not be sufficient to meet the large data demands of the firm. In addition to talent, a telecom
company must ensure that their traditional relational databases in SQL are integrated with Big Data
technologies through proper tools such as NoSQL and Hadoop [6] [14]. As a result, information from NoSQL
systems, Hadoop clusters and other data sources can be joined with data from relational databases and data
warehouses to build a complete picture of customers, market trends and business operations. This will enable

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M1 to extract insights from customer data that can not only be used to improve its own network services and
reduce costs, but also create additional revenue sources by way of sales to third parties without compromising
data privacy.
D. Conclusion
To become a key player in the South-East Asian telecom market, M1 must integrate the data analytics
component into their business strategy. In today’s hyper competitive world, companies must do so quickly and
effectively. Since Big Data comprises of actual customer behavior data generated in real time, it can help
innovate and develop customer-centric strategies with huge long term payback. Another stream of endless
opportunities will be provided with the evolution of IoT. Telecom companies will be faced with the task of
handling massive volumes of data generated by billions of smart devices. They will need to invest in resources
that will help build models for storing, analyzing and making sense of vast amounts of data. Tactics discussed
by this paper offers M1 a tangible starting point to embrace Big Data and advanced analytics for its growth. It
gives them a realistic evaluation of the short-term and long-term benefits of this technology, and what they
need to do to achieve them.
E. References
[1] https://www.m1.com.sg/aboutm1
[2] http://www.adlittle.com/downloads/tx_adlreports/ADL_BigDataGoldMineforTelcos.pdf
[3] http://www.strategyand.pwc.com/media/file/Strategyand_Benefiting-from-Big-Data_A-New-Approach-
for-the-Telecom-Industry.pdf
[4] DROM8127 Class 1 – Lecture Notes, Columbia University, Prof. Asha Saxena
[5] www.huawei.com/ilink/en/download/HW_323807
[6] http://www.prnewswire.com/news-releases/big-data-in-global-telecom-market-key-trends-market-
opportunities-and-industry-forecast-2015-2020-300282778.html
[7] https://www.sas.com/en_id/news/sascom/2014q3/Big-data-davenport.html
[8] http://www.zdnet.com/article/big-data-analytics-key-play-for-singtel/
[9] https://www.cloudera.com/content/dam/cloudera/Resources/PDF/solution-briefs/Industry-Brief-Big-
Data-Use-Cases-for-Telcos.pdf
[10] https://newsroom.cisco.com/feature-content?type=webcontent&articleId=1208342
[11] http://www.mckinsey.com/industries/high-tech/our-insights/big-data-getting-a-better-read-on-
performance
[12] http://newvantage.com/wp-content/uploads/2016/01/Big-Data-Executive-Survey-2016-Findings-
FINAL.pdf
[13] https://cb.hbsp.harvard.edu/cbmp/product/7710BC-PDF-ENG
[14] http://bigdata-madesimple.com/11-interesting-big-data-case-studies-in-telecom/