|Topic:||Telecommunications and the Big Data explosion:|
Network Operators positioned to generate new revenue streams due to exponential growth of data volume and real-time data analysis
Carl Grivner is responsible for directing Pacnet’s global strategy and leading the company on its next stage of growth.
Carl has over 23 years of international executive and leadership experience including 12 years at the level of CEO at major telecommunications companies in North and Central America, Europe and Asia.
Prior to Pacnet, Carl was CEO of XO Communications, a leading telecommunications company in the US, where he transformed the business from a local exchange carrier to a national carrier. He also held several executive level positions in the telecommunications and information technology industry.
Carl holds a BA from Lycoming College in Pennsylvania.
Big data application traffic involves bulk transfer, data segregation and control messages which are latency-sensitive. While cloud solutions are agile enough to be quickly provisioned on multiple virtual servers, care should be taken with multi-tenant environments to avoid latency issues. There are numerous benefits to cloud-based storage, however, as it enables the full capabilities of data profiling and rapid information retrieval while providing scalability.
The age of Big Data
The telecommunications industry today is shifting its focus to capitalize on the growing trend of Big Data capture and analysis. Big Data Carriers are taking a deeper look at the unstructured, stored customer data each maintains while simultaneously delivering high-speed transport, real-time processing and secure data storage. Composed of social media data, machine-generated communications and transactional activity, the data is integrated with CRM data for analysis of patterns and actionable insights into consumer behavior. It is estimated that 1.8 Petabytes of information is carried by the Internet each day to 2.5 billion Internet users and 6.57 billion mobile subscribers worldwide. An even greater amount of traffic is on the horizon as users connect multiple mobile platforms along with cars, smart homes, smart machines and other devices to enable remote management and execution of daily tasks in the age of the Internet of Things. By 2015, 15 billion fixed and mobile network devices and machine-to-machine (M2M) connections are expected to be operating on the Internet with 24.8 gigabytes of information being consumed per user per month . While much of the today’s data is gathered in disconnected information silos, telecom organizations are beginning to realize new revenue streams by developing data analysis tools, which convert unstructured data into business intelligence and knowledge management systems benefitting themselves, customers and third parties.
For years, telecom has provided the big pipes and network infrastructure needed for high-speed transport and storage of massive amounts of voice, data and video across the Internet. While data analysis has not been a key focus in the past, more carriers are positioning themselves beyond simply transporting and transmitting information to capturing, transferring, storing, sharing and analyzing large and complex sets of data in real-time to exploit the financial benefits of Big Data.
Social media and its impact on Big Data matters. In 2013, 77 percent of Fortune 500 companies have Twitter accounts, while 70 percent have Facebook fan pages, 69 percent have YouTube channels and 34 percent have corporate blogs . Carriers must have the scalable capacity and bandwidth to carry the growing volume of data and video over the Internet as well as the analytics tools to secure valuable market information within the endless stream.
Big Data started out small in 2001 around the beginning of the digital age when analyst Doug Laney of the former META Group (now Gartner) looked at anticipated data growth in three dimensions based on volume, velocity and variety. In his research, Laney realized new forms of computing would be necessary to provide insights into the enormous amount of data being gathered. By 2005, Apache Hadoop was launched as an open-sourced software platform for processing and storage based on a distributed software framework. Today, Apache serves a global community of users and supporters and continues to add new components and projects to the platform.
As the open-sourced platform has gained momentum, other companies, including a number of telecom providers, have begun developing software-based tools to measure and analyze Big Data. The increase in new applications makes sense in light of reported results of a 2013 Gartner survey of 720 worldwide customers , which determined 100 percent of its customers had invested, were investing or planned to invest in Big Data technology within the next two years. Investment in Big Data analytics is no longer limited to media and communications and has expanded to encompass banking, transportation, insurance, healthcare and other industries. Applying analytics to social media engagement allows companies to make better market decisions about product innovation and growth strategies, build new revenue streams and improve the customer experience.
Internet of Things grows in scope
As more computing devices become connected to the Internet and each other, the Internet of Things has grown in scope. Today, there is a need for metrics to extract and analyze trending data for M2M communication, smart machines, smart buildings and its set of connected devices such as security, network infrastructure, lighting, HVAC, and utility allocation, multiple mobile devices, and predictive machines designed to improve productivity. The Internet of Things creates increased demand for an end-to-end solution combining carrier transport, Internet services, storage capacity and real-time access and analysis tools to formulate actionable data (Analytics on Demand or AOD), information strategy, and information management. Furthermore, the sheer size of the data sets and the degree of analytics complexity lends itself well to the elasticity of cloud storage versus on-site storage.
Science continues to take center stage in the need for viable tools to process Big Data, as simulated research in meteorology, biology and other fields cannot occur within a reasonable time period with data sets in the exabyte range. In Europe, a business-science partnership called the Helix Nebula has been created to strategize ways to develop a cloud computing platform that meets the needs of science. Leading research centers CERN, EMBL and ESA will provide capacity and security to enable innovation in the study of the Large Hadron Collidor, molecular biology and earth observation.
In Chongqing China, a city growing at an annual rate of 12%, the Chongqing Mayor’s International Economic Advisory Council (CMIEAC) has decided to exploit the value of big data and cloud computing by forming an agglomeration of new service offerings in e-commerce, Internet service and mobile applications. In Chongqing, the cloud computing industry has already taken shape, and the city’s terminal operations are driving new growth in software and information services. Long-range planning calls for improvements to all sectors including electrical generation, human resources, professional industrial parks and enterprise and market policy support. With market demand projected to reach US$350 billion by 2017, Chongqing is positioning itself to take full advantage of anticipated demand by building a big data ecosystem and industry base through comprehensive urban management of applications in virtualization, cloud computing platforms, reliable storage, data processing capabilities, information security, and the capacity for data mining and analysis.
Big data application traffic involves bulk transfer, data segregation and control messages which are latency-sensitive. While cloud solutions are agile enough to be quickly provisioned on multiple virtual servers, care should be taken with multi-tenant environments to avoid latency issues. There are numerous benefits to cloud-based storage, however, as it enables the full capabilities of data profiling and rapid information retrieval while providing scalability. Runtime configuration needs to be more flexible and frequent to analyze big data in the virtual environment, as additional demands are placed on the Software-Defined Networking (SDN) controller to enable fast updates across the network. One way to leverage the SDN is to create application-aware networking for information access at the application level. Application performance can be improved by leveraging optical switches to create increased bandwidth and reduced energy consumption. Traffic demand, circuit use and interdependencies should also be considered when optimizing the network.
Big Data analysis becomes more complex
As more start-up and software companies begin to develop analytics tools, expect more user-friendly applications to surface including analysis dashboards and cognitive applications to make it easier for users to understand data. Organizations will see Big Data as a must have for organizational growth and will look at outside information such as social media engagement as well as internal sources. New applications will become more efficient and offer insights into supply chain management. Other opportunities may exist in the development of processing components designed to analyze streams in real-time to improve customer service and the decision-making process. Smart machines will prompt the development of contextual awareness, enabling the development of new business models in response to the ability to track behavior and product movements.
Telecommunications positioned for rapid growth
Mobile bandwidth demand will spur new growth in the global fiber optic components market, which is expected to reach US$80.6 billion by 2018 . Carriers are positioned for the next big growth surge in Internet connectivity requirements by offering high reliability, security, limitless capacity, low latency, extensive connectivity and cost-effectiveness. High capacity transport upgrades offering fast transport and real-time access to data centers, carrier hotels and colocation centers will become more frequent due to demand surrounding Big Data information transfers. These are key factors in the evolution of Big Data analytics by enabling rapid provisioning of virtual private networks (VPNs) to handle analytics solutions and user queries. Also, application-aware SDNs work well with data blocks by creating a scalable system, lower delivery costs, rapid provisioning of data, fast routing and low latency. Network as a Service (NaaS) is another helpful tool as it makes the network more flexible by scaling bandwidth-on-demand across the network rapidly.
Big Data opens the door to new revenue streams
Smart phone data traffic is expected to increase 50 times its current rate in two years . Carriers that want to take advantage of the explosion in data traffic would benefit from incorporating Big Data analytics into network planning. Big Data offers network operators the means to maximize revenue potential for individual subscribers by providing increased visibility into bandwidth allocation and network capacity and performance. Carriers gain a competitive advantage by analyzing and understanding usage patterns, network performance data, device information, site data and other information across the network. New insight into the entire business improves efficiency and performance and reduces customer churn. A clear understanding of customer usage patterns and needs improves the customer experience and increases wallet share. Collected data could also be used to create customer profiles and preferences to set in motion trigger actions to improve customer service.
Because carriers already store customer data, a data analysis tool can be added as a service offering to help customers identify patterns to improve business strategies, thereby creating an end-to-end solution to improve retention as well as generate a new revenue stream. The tool could also be used internally to study client behavior patterns to increase sales while segmenting customers for more effective marketing campaigns. Identifiable information could be stripped from the collected data and sold to third parties seeking anonymous network statistics as well.
As more companies begin to see the value, opportunities and return on investment (ROI) made possible with Big Data analytics tools, telecom companies must position themselves to take full advantage of the opportunity by improving the way data is stored, measured and delivered. Big Data is here to stay, particularly due to the exponential growth of social media. Carriers who invest in improved transport capacity, fast analytics tools and enough processing power to filter large amounts of data, as well as responsive SDN Networks and Open Source Solutions, will gain the competitive advantage moving forward.