Applying Big Data to telecom network architecture: Ericsson mobile broadband head

Derek Long, Ericsson India head – Communication Services & Mobile Broadband says big-data technology is an important part of the puzzle for Indian telecom oper­ators wanting to leverage value from the large volumes of data in their possession in a cost-efficient way.

Ericsson’s Derek Long says applying big-data technologies has the side effect of trans­ferring some complexity from the data­base to the application.

When does data get big? People, devices and things are constantly generating massive volumes of data. At work people create and consume data, as do children at home, students at school, as well as people and objects whether stationary or on the move.

Millions of devices and sensors take measurements from their surroundings, providing up-to-date readings over the entire globe – data to be instantly processed or to be stored for later use by countless different applications. When a new phenomenon comes about, it often takes the related indus­tries a while to agree on a common def­inition, and big data is no exception. However, the consensus seems to be that data gets big when it starts to stretch the limits of traditional technology.

The data available to operators through their networks presents them with an opportunity and a business-intel­ligence edge over other players. As is often the case, with opportunity comes challenge, and for big data this chal­lenge comprises the volume and diversi­ty of the data – and the fact that both are expected to grow substantially in the next few years. An IDC study found that by 2020, the world will generate 50 times the amount of data it did in 2011. The value of infor­mation in the data is significant, but the costs involved in obtaining it using cur­rent technology can be prohibitive, according to Derek Long, head – Communication Services & Mobile Broadband, Ericsson India.

Derek Long, Ericsson India head - Communication Services & Mobile Broadband

The nature of this data – big data – is also set to change; the size of single data sets, the variety of data types and the demand for real-time access to data are all on the rise. These factors lead to varying types of data being collected by the network and transmitted through it. Data sets are irregular and may be unstructured, they can contain ambiguities, they are time- and location-dependent, and are constantly being updated by capture equipment such as mobile devices, sensors and RFID readers.

In its simplest form, big-data technology encompasses the tools, processes and procedures to consolidate, verify, analyze, manage and visualize very large data sets with mainly non-relational but also relational repositories.

The emergent approach is a cost-effective one that can handle the 3 Vs of big data: Volume, Velocity and Variability. Big-data technologies are a new gen­eration of methods and architectures designed to extract value from mass­es of different data types through high-velocity capture, discovery and analysis. Complementing the telecom indus­try with big-data technologies could generate value and add innovation opportunities for operators and users across all industries, in public services and in private life.

The knowledge derived through analy­sis of data from smartphones and other devices connected to telecom networks is a valuable asset for telecom operators. The massive amounts of data analyzed OSS/BSS tools can help operators leverage the value of this knowledge. By further extending these tools to the communication embedded in manage­ment systems, requirements for user experience, business innovation and efficiency can be met.

The huge amounts of data generat­ed by the Networked Society, in which real-time communication is more criti­cal than it was in the past, can be used to a significant advantage in many areas of urban planning, such as effi­cient use of transportation, smart elec­tricity distribution and water supply. Efficient planning and control of trans­port and utilities requires analytics sup­port, which will, for example, forecast demand levels and consequently enable utility providers to deliver in a smart way – meeting demand with minimal waste. Figures for supply and demand can be constantly refined with real-time consumption rates, creating an ecosys­tem with reduced waste.

The value that can be derived from using big-data technologies depends on the use case and the data associated with it. Apart from volume and veloc­ity, the value that can be gained from the variability of data tends to be over­looked. Put simply, the less structured the data, the greater the requirement to apply big-data technologies.

Big-data technologies are usually engi­neered from the bottom up with two things in mind: scale and availability. Consequently, most solutions are dis­tributed in nature and introduce new programming models for working with large volumes of data. Because most of the legacy database mod­els cannot be effectively used for big data, the current approach to ensur­ing availability and partitioning needs to be revised.

Consequently, big-data technology is an important part of the puzzle for oper­ators wanting to leverage value from the large volumes of data in their possession in a cost-efficient way. Applying big-data technologies has the side effect of trans­ferring some complexity from the data­base to the application.

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