Big Data: Global access to large data age specialization operation is the trend

Global access to large data age

Oracle predicts that global data volumes will increase by more than 40% in the next 10, with 2020 global data volumes approaching 45ZB (45,000,000PB), 44 times times more than 2009 years. There is a huge gap between the fast-growing volume of data and the existing analytical capabilities, and the development of large data-processing technologies can effectively bridge the gap and enable data value to be better mined and exploited. According to the 2013 Gartner Emerging technology hyper curve, large data technology has grown rapidly and is still on the rise, nearing the peak of the desired expansion period (Peak of inflated expectation).

Data from Wikibon show that in the 2012 global data market, hardware revenue, services revenue, software revenues of 4.543 billion U.S. dollars, 4.391 billion U.S. dollars and 2.295 billion U.S. dollars, accounting for 41%, 39% and 20% respectively, 2017 Global Big Data market is expected to reach 48.5 billion U.S. dollars.

The data from CCW survey show that in the 2012 large data market, the government (14.9%), the Internet (14.9%), Telecommunications (10.6%), Financial (10.6%) industry is large, occupy half of the market share. 2016 market size is expected to reach 9.39 billion yuan.

Telecom operators to upgrade their professional capabilities in the face of challenges

Telecom operators should deal with the three major challenges in management, technology and application of large data. The first is big data management challenges, such as how to effectively organize and manage large data, how to protect data security and user privacy, and how large data protects data quality. Secondly, large data technology architecture challenges, such as large data storage, network, etc. put forward higher requirements, multi-source heterogeneous large-scale data collection and integration face challenges. Finally, large data application challenges, such as how to explore data value from massive data, existing data application patterns have been difficult to adapt to the needs of large data continuous optimization.

In the large data implementation strategy, telecom operators should be good at cooperating, make full use of the internal and external data, in traffic management, network optimization, data services and personalized services to explore breakthroughs, the introduction of valuable data services. For example, the core's internal data includes operators ' unique to own a very high value of the data (POS data, member purchase records, etc.), you can analyze the user's behavior to improve operations internally, by selling to exchange for value, such as providing marketing services for enterprises, and other companies unique to their own highly valuable data ( Other company service member information, Twitter tweets, etc., you can use the purchase strategy.

Telecom operators have increased their efforts to explore large data businesses, such as Orange, BT, Verizon, Telefonica, NTT, T, Sprint, and Canada, to use their own user information to enhance service capabilities, or to launch marketing services, Government public service. The strategy of NTT's extensive cooperation with third parties and even the purchase of data from Twitter for service is worth learning. Establishing a professional department or company will become an effective means for telecom operators to upgrade their professional level of data service, such as Telefonica and Verizon set up a professional department to carry out large data business and provide marketing services to enterprises; New Zealand Telecom even established an independent large data company in 2014 Qrious, hoping to use this platform to Jina and analyze the data.