The amount of big data telecommunication industry generates has high velocity and volume. In a hypercompetitive industry, to be profitable and successful telecommunication companies have to differentiate their offerings and target customers effectively. With information collected about customer behaviour and preferences, big data in telecom means bigger opportunities for telecommunication companies. Using big data in telecom telecommunication companies are combining various real time insights like interests, location, activities with the habits and preferences to achieve unparalleled marketing power. The article highlights top big data use cases that are at the heart of digital value chain of telecommunication companies.
The past decade has seen an exponential growth in the telecommunication industry. The cost of communication has steadily decreased and thanks to the innovation in the electronics industry, mobile phones have become affordable and feature rich.
Now one no longer expects a phone to just make and receive calls and text messages. Smartphone’s have become an invaluable part of our lifestyle. The developing countries such as India and China have been driving the mobile phone market and are no longer considered as electronic dump zones by the major players of the mobile phone manufacturing industry.
Technology and business are coming closer than ever before. In the modern age no industry touches as many technology related business sectors as the Telecoms.
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Telecommunication industry includes the traditional local and long distance telephone calls, text messaging, wireless communication, optical fiber based high speed broadband communication, television streaming and other modes of satellite based communication.
Telephone service, cable television, internet services and wireless are all being provided as an integrated telecom solution and at present, the International Telecommunications Union (ITU) estimates approximately 6.9 billion wireless service subscriptions worldwide as of mid – 2014.
The revenue model of Telecom sector has seen a marked shift from the traditional voice and messaging driven model to a data driven model.
This churn in the industry has given rise to an enormous amount of data which has never been seen before. Around the globe, hundreds of thousands of phone calls take place simultaneously, which need to be accurately tracked by the second and the report has to be made available to the customers in the form of an itemized bill.
Big Data has made its presence felt across industries and Telecom sector is no different. Big data telecom is in need of robust, scalable and accurate data analysis software which is capable of tracking and analyzing such large volume communication in real time.
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As is the case in most other industries, Apache Hadoop has come to the rescue for the Telecom sector as well in Telecom data analytics for providing real time monitoring and Big data solutions.
Big telecom companies have a number of verticals such as Marketing, Product, Sales, Human Resources, Information Technology, Research and Development, etc. that are in constant need of information. Using Hadoop, the existing databases can be suitably mined and information can be extracted which would eventually be fed to the respective verticals for decision making.
For example, the Telecom data consumption trend of the past few years can be extrapolated to determine the expected bandwidth usage by consumers in the future and appropriate products can be pitched to them. Also, the technology division can scale up its network infrastructure well in advance to determine.
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Big data telecom gives you the unique ability to segregate data and computation which is a big improvement over the traditional tools.
Hadoop data analysis engines perform the calculations and processing at the database end and transmit only the final result which allows big data analysis of telecommunications data in an efficient, fast and secure manner. It prevents unnecessary clogging of bandwidth which can be efficiently used for other more important network oriented operations.
Hortonworks puts Hadoop as one of the best placed tools to look into Telecom data analytics. It states that of the present solutions capable of handling Telecom data effectively, Hadoop is the best suited for delivering a modern data architecture. It allows Telco’s to store new types of data, retain it for longer periods, join different data sets together and derive new information from the resultant combination which can be valuable for the business users.
Hortonworks lists down how modern data architectures using Hadoop can provide Big data solutions to telecommunication industry which is in an attempt to gain competitive advantage:
One of the biggest challenges the telecommunication industry faces is to have an infrastructure in place for analyzing CDRs which can be ably addressed using Hadoop.
Telecommunications companies carry out a lot of forensics on their data for monitoring the quality of service. It involves using Hadoop to perform dropped call analysis, monitor and report poor sound quality, root cause analysis and pattern recognition. Considering the fact that millions of records flow into the Big data Telecom databases every second, there is a need to perform real time, accurate analysis and using Hadoop provides exactly that, with the help of Apache Flume (capable of ingesting millions of CDRs into Hadoop database per second) and Apache Storm (capable of processing data in real time and identifying irregular, potentially troublesome patterns). These can be combined to improve the overall customer satisfaction levels.
Big telecom companies stay ahead of the market and invest in huge Telecom data infrastructure well in advance in order to gain competitive advantage and be ready to service customers as soon as there is a demand for their service. This calls for regular performance monitoring of the Telecom data equipment such as s, conductors, signal boosters, antennas, etc.
Using Hadoop enables telecom companies to analyze big data produced by telecommunications systems through performance indicators (voltage and current levels, up time, down time, efficiency, etc.). Using Hadoop makes this real time analysis easy to perform and store.
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Product innovation is one of the key factors for any big telecom company to attain and maintain its competitive advantage. It is important to analyze the usage history and forecast the next generation of products which the customers are likely to expect and be ready with them as soon as the demand arises. These require complex analysis on terabytes of data and sort them according to customer demographics, geography, profession and a number of other factors.
Using Apache Hadoop has made Telecom data analytics possible in a secure, reliable and efficient manner.
Today’s telecom customer has got a number of service providers to choose from and the switching costs have come down drastically, which means the telecom companies, especially the giant dot of Telecoms, need to keep a careful watch on the performance of their network. The network bottlenecks have to be identified and resolved within a matter of minutes for a company to retain its customer base and attract new customers.
Using Hadoop gives them the ability to dig through Petabytes of data and extract meaningful information in a matter of seconds.
Companies such as Telefonica, China Mobile and Verizon crunch their big data through Hadoop to grow and maintain their services. "The ability to identify, gather, store and share large amounts of data is one of the true benefits of cloud computing," said Verizon CMO John Harrobin. "Providing those capabilities on top of our high-performing, secure and scalable architecture strengthens the Verizon Cloud offering for our clients." Telecommunication sectors are speedily hiring Hadoop developers and it is best to be aware of the Hadoop developer roles and responsibilities in this industry.
So, if you relate to the Telecom sector, in any capacity, learning Hadoop will always be a huge advantage, especially in the current scenario dominated by the boom of the Telecom data analytics in the telecommunication industry.
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