The quantity of data/information available in the world today is revolutionary. It streams from phones, credit cards, televisions, computers, sensor-equipped buildings, trains, planes, companies, etc. If 2.8 billion terabytes of data is created today, it is likely to more than quadruple the next year. This data deluge, or the “next big thing” of this decade, is transforming businesses, industries, governments and consumers across the world. Harnessing, channelising and analysing these large sets of data through data analytic tools and solutions hold the promise of significant profitability for industries globally.

However, while everyone is talking about “big data”, the term remains ambiguous to many. A 2013 survey by the Economist Intelligence Unit revealed that despite the widespread usage of the term, about 50 per cent of employees in organisations today do not understand the role and relevance of big data in their operations and profitability.

Simply put, big data is nothing but a humongous volume of unstructured customer- or enterprise-centric data, being created on a daily basis across the networks and databases of companies. However, from a technological perspective, it also covers the dimensions of velocity and variety in data streams. Thus, while traditional analytics would work around data that is transactional and structured, and uses past data for predictions, big data would imply the analysis of a high volume and large variety of data streams in real time.

In recent years, with the growing usage of internet and social media, the increasing adoption of smartphones and explosion of online access to services, the data obtained in most cases is highly customer-centric and unstructured. But when properly captured and analysed, it can provide deep insights into consumer behaviour, sentiments and usage patterns. This quality information, derived from large sets of data, gives a competitive edge to businesses across industries by improving the quality and increasing the speed of decision making, improving planning and forecasting, developing new products/services, and opening up new revenue streams.

“Big data has a significant impact on the competitive differentiation of enterprises and their ability to avert business risks. A recent study affirms this trend as more than 90 per cent of surveyed Indian enterprises believe that better usage of big data technologies will lead to improved decision making. Therefore, Indian companies will continue to look to IT innovation to compete better on the global stage,” says Sethumadhavan Srinivasan, director, corporate marketing and strategy, Huawei India.

Among the various industry verticals that can leverage the big data strength, telecom operators stand to benefit the most in this space, given that they sit on huge piles of consumer data pertaining to call data records, cell sites, networks, location, and operation support systems (OSS) and business support systems (BSS). Further, the mobile broadband segment has received a boost with the introduction of 3G/4G services, giving operators a huge amount of data related to internet usage patterns, preferences for websites, etc. This data, once adequately collated, can be monetised by operators, who can sell it to companies in other industry verticals, or simply use it to enhance their own product/service portfolio with more customised offerings.

According to Hemant Joshi, partner, Deloitte Haskins and Sells, “With the huge volume and variety of data passing through telecom networks, operators have the opportunity to turn the bit pipe from a dumb pipe into a smart one. Telecom operators can leverage the unstructured data available with them by converting it into structured data through data analytics, which can then be used to create innovative services for end-users. They have a competitive edge over other industries in providing productivity and efficiency solutions in real time, through data analytics.”

Monetising data is a key trend across various telecom markets in the world. The Indian telecom industry, with over 900 million subscribers and close to 300 million internet subscribers, makes a significant business case for big data adoption. Moreover, as voice and SMS-based revenues are near saturation and while data revenues wait to reach the inflection point, data monetisation will open up new revenue opportunities for operators in the near term. “Monetisation of data could provide a new revenue stream for operators in maturing voice markets,” says Joshi. This could further drive the adoption of big data analytics in the near future.” Globally, operators are playing a key role in bringing the right content to the right user, by making consumer data available to content providers, and in the process, charging customers as well as advertisers. They are using big data to enhance marketing practices, arrest subscriber churn and improve network productivity.

tele.net takes a look at the opportunities that the big data space offers to telecom operators, the challenges faced in big data adoption and the way forward for the segment…

A big deal

Over the years, wireless communications have become an indispensable part of people’s daily life, giving telecom operators a vast amount of mobile user and network data. As the gatekeepers of this vast, untapped wealth of information, operators can explore several business models for data monetisation.

For one, they can use the data for in-house innovation across products and services. The need to be creative on the service front has never been more profound than it is today. Competition in the telecom markets has resulted in the emergence of customer experience and quality of service as key parameters for ensuring customer loyalty. This, coupled with services such as mobile number portability, has enabled subscribers to make a swift shift across operators in case service quality is unsatisfactory. Further, the need for customised services has become more pressing, given the growing number of enterprise customers in operator portfolios, as well as their contribution to total service revenues. In such a scenario, big data analytics can help operators in differentiating their service offerings. By dedicating a substantial amount of time and resources, operators can develop transaction processing, warehousing and analytical capabilities to unlock insights from network and OSS/ BSS data. This, in turn, would help them introduce personalised products/services, deliver tailored offerings, ensure network optimisation and manage customer churn, all of which will result in incremental revenues as well as improve performance.

Apart from using data to innovate on the service front, operators can explore the option of providing these data sets to other enterprises. They can make their network and OSS/BSS data accessible to businesses through a controlled application programming interface environment. In fact, going a step further, they can also offer insights from the data available with them. Consequently, several operators today are exploring the option of earning revenues by selling pure data or data analysis to third parties for advertising or market research.

Companies in the advertising industry rely heavily on consumer data for effective advertising. As mobile advertising gains traction, operators stand to benefit by sharing their data with advertisers.  This data can include customer-specific information such as age, gender and home address; device-specific information like the number of live mobile devices and the amount of time spent on them; and usage-specific information on content, applications and browsing patterns and other transactions that users carry out online.

Market research firms can also access operator data to derive better survey results. The growing number of smart devices provides the opportunity to collect an almost-complete data set on a real-time basis. This kind of information becomes extremely crucial and helpful for industries such as retail, hospitality, health care and banking.

Indian scenario

Despite several benefits, big data adoption in the Indian telecom industry continues to be at an exploratory stage. That said, operators realise that turning to big data analytics and exploring data monetisation strategies will become indispensable for driving long-term business and revenue growth. For instance, as per the results of a study conducted by EMC, a leading provider of IT storage hardware solutions, in 2013, 91 per cent of Indian businesses were aware of the potential benefits of big data, and only 26 per cent of the companies had no immediate plans to deploy this technology.

Currently, Bharti Airtel depends on big data analytics to help create more than 5,000 targeted campaigns a day, to increase monetisation. Reliance Communications has also been involved in big data solutions and has adopted EMC’s Greenplum platform to store and analyse call data records and unstructured data. Vodafone India too has plans to leverage big data to analyse unstructured data and combine it with the existing structured data, in order to gain insights into customer behaviour and preferences, service experience, etc. In fact, in February 2014, Idea Cellular partnered with big data analytics firm Flytxt to deploy the multi-channel mobile advertising platform, QREDA. The platform helps brands and agencies connect to Flytxt’s mobile advertising marketplace, mADmart, and directly engage with over 130 million subscribers of Idea Cellular by delivering relevant ads.

Big challenges

While there is growing awareness regarding big data analytics and its benefits, several challenges hinder its wide-scale adoption. Companies across the board are yet to be fully convinced of the quantifiable benefits that big data analytics brings to their top line, especially in light of the huge investments this demands. Big data analytics requires large amounts of money to be spent on storage, servers, cloud infrastructure, discovery and applications. While 75 per cent of organisations are confident of driving new revenue streams using big data, only 35 per cent of them plan to make investments in building big data capabilities related to analytics, security software and real-time applications. As a result, most companies in India are following a wait and watch policy as far as the adoption of big data analytics is concerned.

Further, data security is a big challenge. Consumers need to be assured that protocols and processes related to security, confidentiality, privacy and protection are adhered to while handling and analysing data. Putting proper security software in place entails costs that Indian telecom operators are clearly not willing to bear at this point in time. Over the years, the operator outgo on spectrum acquisition has taken a serious toll on their profitability, as a result of which they have had to compromise their investment plans in various areas, including big data analytics.

Aside from cost, the shortage of skilled technical professionals to manage the big data space is a key issue. “There is a lack of skilled talent to analyse complex data and give useful insights,” says Joshi. Moreover, data is often gathered from systems working in silos, and capturing a cross-organisational view of data becomes very difficult. Collaborating with several industries to provide data with 100 per cent accuracy in real time continues to be a major concern.

Focusing on the big picture

Going by industry definitions, organisations that generate over 100 terabit of data and have an average data growth rate of 60 per cent per year should consider undertaking big data analytics. Thus, Indian telecom operators, which are recording a two-fold increase in data usage every year, are best suited to venture into the big data space.

Further, as the number of connected devices and online transactions continues to grow at an exponential rate, operators will be compelled to get into the big data space in order to enhance customer experience as well as revenues. However, addressing the challenges related to security, cost and talent shortage will be crucial in convincing companies to invest funds in this space. Quantifying performance to justify investments will be important. These challenges notwithstanding, the big data analytics ecosystem is expected to evolve significantly in India over the next three to five years.