What is the inevitable connection between the mobile phone industry and big data?

The rapid development of mobile Internet accelerates the implementation of big data applications

2015.1.4

McKinsey, a world-renowned business consulting organization, has long said: "Data has penetrated into today's Every industry and business functional area has become an important factor of production. "In terms of clothing, food, housing, transportation, etc. that are closely related to us, the changes in lifestyles caused by big data are obvious, and the real application of big data from theory to practice. It's just the beginning.

The rapid development of the mobile Internet has accelerated the implementation of big data applications

The data era is coming, and everything around us is being defined by data. Every day, people generate billions of massive amounts of information through the use of computers, mobile phones, GPS and other devices. This interactive information fundamentally changes the original appearance of the world.

With the growth of the number of mobile users, applications based on mobile Internet big data have attracted much attention. According to the latest statistics from CNNIC, as of October 2014, the number of mobile phone users nationwide reached 1.122 billion. The number of smartphones in stock increased rapidly from 200 million units in 2013 to 360 million units in 2014. There are currently about 10 mobile applications with over 100 million users, including WeChat, Kuaidi Taxi, Taobao Mobile, Baidu Maps, Sogou Input Method, PPTV, Amap and Moji Weather, etc. They all make good use of big data. It can extract effective information based on the analysis and sorting of user data, thus becoming a pioneer in APP big data applications.

Moji Weather APP is a typical representative of big data applications. Its product Air Fruit can be easily set up using Wi-Fi. Air Fruit has a cool design and a friendly interface. It can make voice announcements, light up the display with a wave of the hand, and switch data with a wave of the hand. It has obvious advantages over other similar mobile APPs in terms of user data storage, analysis and utilization.

The large-scale use of Kuaidi Taxi has increased the application of big data in mobile APPs. Kuaidi Dache is a representative of the taxi-hailing software and mobile payment market. It has gained access to the big data and O2O markets. The software can be used to accumulate data on users' taxi habits, taxi routes, etc., and then analyze it, and then overlay map services, life information services and other content to achieve an intelligent service model to increase customer stickiness, thereby forming cooperation with merchants and consumers to achieve profitability.

While mobile Internet big data applications are developing rapidly, there are still two issues that need to be faced squarely:

First, users have doubts about privacy and security issues, resulting in the openness and integrity of data. The degree is insufficient, thus limiting the development of big data applications. Nowadays, mobile phones have become the first terminal, Internet center and personal information center. People have handed over their communication, social interaction, entertainment, life, business and privacy to smartphones and their various applications. According to DCCI's third quarter report, as of October 2014, 66.9 smartphone mobile applications were capturing users' private data, of which as many as 34.5 mobile applications had "privacy deviance" behavior. Call records, text message records, and address books are private data. Three high-risk areas for information leakage. How to ensure the appropriate scale of data collection while ensuring the integrity of services is a challenge for enterprises.

Second, there are technical barriers to data analysis and exploration, and it is impossible to "count" them to their full potential, resulting in the loss of a lot of potentially valuable data. Mobile applications based on big data have become very widespread, and user data is left on many mobile terminals. The data released by IHS Screen Digest for the third quarter of 2014 showed that 48.2% of the data was wasted, and the effective value of the data was not truly extracted. There is still room for improvement in data analysis and mining.

Operators test the waters of big data operations

Nowadays, the huge commercial value of big data has emerged. According to statistics, the current market size of big data is around US$5.1 billion, and by 2017, this figure is expected to rise to US$53 billion. With the explosion of data, how to mine this data also faces dual technical and commercial challenges.

As producers of data, telecom operators have abundant big data resources. These resource advantages are unmatched by other enterprises, and they have huge potential for value mining. With such a high-quality data foundation, operators will make great achievements at multiple levels such as enterprises, industries, and society.

Looking at the global market, the development of big data for telecom operators is still in its infancy. Massive data has not brought them considerable income. How to rely on big data to avoid the crisis of dumb pipes is a key issue for global operations. Regarding the topic of business, the three major domestic operators have also begun to actively deploy.

He Hongling, project manager of the Business Support System Department of China Mobile Communications Group Corporation, once revealed that China Mobile currently adds 8T of structured data every day, 400T of new log data every day, and processes 10T of data every day. Times this, the data queried every day is 100 times this. He believes: "Data has become the first competitiveness of enterprises today. Data can be included in the balance sheet. Enterprises should try their best to collect and organize data, save data as much as possible, and list data as the core asset of the enterprise."

It is understood that China Mobile has also carried out some active exploration and practice in the specific research and development, industrial cooperation and external application of big data. In 2014, China Mobile established the Suzhou R&D Center and built a R&D team and operations team of 3,000 to 4,000 people, aiming to further improve the cloud computing and big data product system and form world-class cloud computing and big data service capabilities as soon as possible.

China Telecom has organized and compiled a plan for deepening reforms in the field of big data, and clearly wants to establish a big data operation system with "overall management and two-level operations". China Telecom's Marketing Department has organized and carried out pilot projects such as big data RTB precision advertising business and migrant population monitoring business in scenic spots, with remarkable results. In the next step, China Telecom will focus on Internet user behavior analysis reports, big data financial credit risk prevention business, big data regional insight business and other research, organize and carry out pilot work, open up business processes, verify business models, and explore the application of telecom big data in key industries. In-depth application and value mining have achieved certain social and economic benefits.

China Unicom also uses the big data traffic of its wireless pipes and uses Hadoop and Spark big data mining algorithms to build a mobility insight solution to extract valuable information from network data. Create more industries, new government services and new applications. For example, Shanghai Unicom appropriately separates existing and incremental operations, uses big data technology to truly pay attention to and understand users, and continues to explore and enhance the value of existing customers. Analyze the network areas that need to be optimized through the spatio-temporal data of users who have left the grid, and combine it with ROI to analyze the areas that need optimization most and the high-value users who frequently enter poor quality areas and have not left the grid. In addition, in the customer collection area, focus on identifying where customers come from. where to consider whether to target advertising to that location.