The focus of big data application is data source, productization and value creation; Uneven distribution of data resources makes it easier for big data applications to make breakthroughs in data-intensive fields; It is necessary to reform the improper industry management mode and promote the application of big data in various industries.
Big data application costs are high. At present, at the national level, the State Council has issued the Action Plan for Promoting Big Data Development; At the local level, take big data as the strategic engine of regional development; At the enterprise level, all kinds of big data concept companies are in the ascendant and flourishing. We only pay attention to the application of big data, where the data comes from, how to use it, and who pays for the results. These are the three key points of data source, productization and value creation. A good big data application may be technically complex, but its business model should be simple, direct and effective. We are also concerned about whether there are some "data-intensive" industries or fields, and big data applications may be easier to develop. In terms of industrial policy, we pay attention to the emerging format of big data. Will the practices that have been tried and tested in the past, such as giving land, money and projects, continue to be effective?
Three Key Points of Big Data Application
The State Council's Action Plan for Promoting Big Data Development (hereinafter referred to as "Big Data Plan") defines big data as "a new generation of information technology and service formats", endows big data with strategic functions of "promoting economic transformation and development", "reshaping national competitive advantage" and "improving government governance capacity", and defines data as "national basic strategic resources". In terms of application, the Outline of Big Data puts forward many development directions in the field of public affairs, such as scientific macro-control, precise government governance, convenient business services, efficient security and universal people's livelihood services; At the industrial level, it is mainly divided into industrial big data, emerging industry big data, agricultural and rural big data, innovative big data, big data product system and big data industry chain. These directions are only the potential and space for big data applications. Whether it can be applied depends on whether there are feasible models and actual effects. Whether in the public domain or industrial domain, big data applications are inseparable from data sources, processing technologies and methods, and value creation models, which are the focus of our attention. To sum up, the following three seemingly simple but critical questions need to be answered. (I) Where does the data come from Regarding the source of data, it is generally believed that the Internet and the Internet of Things are the basis for generating and carrying big data. Internet companies are natural big data companies, accumulating and continuously generating massive amounts of data in their core business areas such as search, social networking, media and trading. Internet of things devices collect data all the time, and the number of devices and data is increasing day by day. As a big data gold mine, these two types of data resources are constantly producing various applications. The successful experience of big data abroad is mostly a classic case of the application of such data resources. Some enterprises have accumulated a lot of data in their business, such as real estate transactions, commodity prices, consumption information of specific groups and so on. Strictly speaking, these data resources are not big data, but for commercial applications, they are the most accessible and easy to handle, and they are also common application resources in China at present. There is also a kind of data resources held by Chinese government departments, which are generally considered to be of good quality and high value, but the degree of openness is low. "Big Data Outline" takes public * * * data interconnection and openness * * * as the direction of efforts, and believes that big data technology can achieve this goal. In fact, for a long time, the information and data between government departments have been closed and separated from each other. This is a governance issue, not a technical issue. The desire to open public data to the society is very beautiful, and I am afraid it will be out of reach for some time. In terms of data resources, the application of "small data" and "data" in China is not sufficient. It is not optimistic to try to step into the era of big data and take the opportunity to solve the problems that could not be solved in the previous informatization process. In addition, because the business of Internet companies in China is mainly in China, their big data resources are not global. Where data comes from is our primary concern in evaluating big data applications. First, it depends on whether the application really has data support, whether the data resources are sustainable, whether the source channels are controllable, and whether there are hidden dangers in data security and privacy protection. Second, it depends on the quality of data resources of this application, whether it is "rich in ore" or "poor in ore", and whether the actual effect of this application can be guaranteed. For the data resources from its own business, it has good controllability, and the data quality is generally guaranteed, but the data coverage may be limited and other resource channels are needed. For the data crawled from the internet, technical ability is the key, which requires both the ability to obtain a large enough amount and the ability to filter out useful content. For data obtained from third parties, special attention should be paid to the stability of data transactions. Where the data comes from is the starting point for analyzing big data applications. If an application does not have a reliable data source, no matter how good and superb the data analysis technology is, there is no tree without roots. (2) How to use data How to use data is our second concern in evaluating big data applications. Big data is just a means, it can't be all-inclusive and omnipotent. We focus on what big data can and cannot do. Now it seems that big data mainly has the following common functions. Tracking. The Internet and the Internet of Things are recording all the time, and big data can trace any record and form a real historical trajectory. Tracking is the starting point of many big data applications, including consumers' purchasing behavior, purchasing preferences, payment methods, search and browsing history, location information and so on. Identify. On the basis of comprehensively tracking various factors, accurate recognition is realized through positioning, comparison and screening, especially for voice, image and video, which greatly enriches the analyzable content and obtains more accurate results. Portraits form a more three-dimensional depiction and a more comprehensive understanding by tracking, identifying and matching different data sources of the same subject. Consumer portraits can accurately push advertisements and products; Corporate portraits can accurately judge credit and risk. Hint. On the basis of historical track, identification and portrait, predict the future trend and the possibility of recurrence, and give tips and early warnings when some indicators change unexpectedly or unexpectedly. In the past, there were also statistics-based forecasts. Big data greatly enriched the forecasting methods and had far-reaching significance for the establishment of risk control models. Match. Accurate tracking and identification in massive information, screening and comparison by correlation and proximity, etc. , to achieve product tying and supply-demand matching more efficiently. The matching function of big data is the basis of the new economic business model of the Internet, such as car rental, leasing and finance. Optimization. According to the given shortest distance and lowest cost principle, the path and resources are optimized through various algorithms. For enterprises, improve service level and internal efficiency; For the public sector, it saves public resources and improves public service capacity. At present, many seemingly complex applications can be subdivided into the above categories. For example, the "Big Data Precision Poverty Alleviation Project" implemented in Guizhou, from the perspective of big data application, can accurately screen and define poor households and identify poverty alleviation targets through identification and portrait; Through tracing and prompting, we can monitor and evaluate poverty alleviation funds, poverty alleviation behaviors and poverty alleviation effects; Through pairing optimization, we can better play the role of poverty alleviation resources. These functions are not all unique to big data, but big data far exceeds previous technologies and can be made stronger, more accurate, faster and better. (3) Who pays for the results is the third and final focus of our evaluation of big data applications. The reason is very simple. Applications that do not create value are not good applications. We are concerned about whether the application of big data has really improved our ability and performance. If you use big data for your own product design, marketing promotion and resource allocation, it depends on whether the competitiveness of the enterprise has improved and whether the final profit of the enterprise is higher than before. If you use big data to provide services to third parties, it depends on whether someone is willing to pay and is willing to pay continuously. But if it is used in the public sector, it depends on whether the payment value of the government or the public sector is worthwhile, not only from the perspective of investors, but also from the perspective of ordinary people. When we are faced with a big data application, just ask the above three questions-where does the data come from, how to use it, and who pays for the result, we can uncover many "disguises". Of course, if it can stand the above-mentioned "three questions about big data", it is not necessarily excellent, but it is not far from excellent big data applications. Looking for data-intensive fields Since big data is regarded as a resource, we should consider the distribution of resources. Generally speaking, the distribution of resources is extremely uneven, such as water, minerals, cultivated land, energy and other natural resources; The distribution of human resources and knowledge is more uneven. Does big data also have the problem of uneven distribution? Can the development of big data industry really overtake in corners? These problems deserve deep thinking. Different from the detectable natural resources, the distribution of data resources is difficult to locate and describe. The distribution of big data human resources can be used to indirectly reflect the differences between regions and industries, and which industries and regions are dense in big data human resources can be regarded as data-intensive. We screened the recruitment information published by the two major recruitment websites "Worry-Free Future" and "Zhaopin" since the second half of 20 14, and obtained that the relevant information published by the two websites in recent two years involved 227,000 enterprises and1007,000 jobs, and the data volume was really "large". Through the summary analysis by region and industry, the results show that the distribution of big data human resources is extremely uneven, with great differences between regions and industries. However, to be exact, recruitment websites reflect the demand for talents, rather than the distribution of human resources in a strict sense, and the two are closely related. Judging from the workplace of big data-related work, Beijing, Guangdong and Shanghai are highly dense, far ahead of other regions. Adding the three places together, the number of enterprises publishing recruitment information on the two websites accounts for 52.35% and 47.48%, and the number of positions accounts for 6 1.23% and 56.74%. It can be speculated that half of the human resources of big data are concentrated in these three places, which is highly consistent with our usual intuitive feelings. Beyond these three places, we are concerned about whether local governments attach importance to the big data industry and whether they regard big data as the engine of regional economic development, which may promote the concentration of human resources and may surpass other regions with similar economic development levels. Judging from the data reflection, at least we can't see such a result at present, which reveals that the human resource structure is the short board that needs to be made up for the development of big data industry in late-developing areas, and it is also the most difficult difficulty to overcome. It is much more difficult to change the composition of human resources in a place than to change the appearance of ground buildings, which requires either a long-term process or a unique system. Even in the same province, the distribution of big data human resources is extremely uneven. For example, in Guangdong, Shenzhen alone accounts for roughly half of the province. Plus Guangzhou, it can reach 90%. In other places, even if the economic strength is good, compared with Shenzhen and Guangzhou, it is far from big data human resources. This once again shows that the distribution of big data human resources is extremely uneven. Obviously, the foundation of developing big data industry in areas with dense human resources of big data is better than that in areas with poor human resources. From the perspective of city ranking, Shenzhen and Guangzhou in the north can be regarded as first-tier cities with intensive demand for big data human resources, while Hangzhou, Nanjing, Chengdu, Han and Xi 'an can be regarded as second-tier cities. The distribution of big data human resources is roughly consistent with the economic strength, vitality and even housing prices of cities. From the perspective of industry distribution, the demand for big data human resources is more uneven, mainly concentrated in the Internet, information technology and computer-related industries. This fully shows that big data is a part of the Internet or IT industry and a new development on the original basis. These industries are typical "data-intensive" industries and the cradle of the development of big data industry. Finance is another particularly important "data-intensive" field. The financial industry is not only a base for generating data, especially valuable data, but also a demander and application place for data analysis services. More importantly, the financial industry has sufficient payment capacity and will be an important battlefield for competition in the big data industry. A large amount of big data is radiated to various industries through its application in the financial field. In addition, there are telecommunications, professional services (such as consulting, human resources, accounting), education and training, film and television media, online games and so on. It is also a relatively data-intensive industry. "Big Data Outline" has planned broad prospects for big data applications in almost all industries and fields, but the distribution of data resources is extremely uneven, so big data applications in "data-intensive" fields are more likely to succeed in the market. What kind of industrial policy does big data need? What kind of industrial policies do big data applications need? From the application point of view, big data is not a brand-new industry, but is integrated with existing industries to transform, upgrade and replace existing models. What restricts the development of big data is often not big data itself, but the original problems in the industries and fields where big data is applied, such as industry supervision, administrative monopoly, and the inability of elements to flow freely. Therefore, relying on land, money and projects to promote the development of big data cannot solve the fundamental problem. From the perspective of big data application, it is necessary to reform the improper industry management mode and adjust the existing interest pattern to make big data application have the necessary conditions. Even within the enterprise, the application of big data is not only a technical problem, but also involves business process reengineering and management model reform, which is a test of enterprise management ability. "Data-intensive" industries such as finance, telecommunications, education, film and television media are not only areas with great potential for big data applications, but also key areas that urgently promote industry reform. On the other hand, the application of big data can also provide technical support for industry reform and achieve industry development goals with more effective technical routes.
The industrial policies needed for big data applications are actually policies that should be formulated by various industries under the market economy, such as liberalizing access, fair competition, reducing the burden on enterprises, eliminating discrimination in enterprise ownership, and eliminating discrimination in enterprise scale. Only in an open industrial environment can big data be effectively used in these industries. If a place wants to vigorously promote the application of big data in finance, medical care, education and other fields, the most effective policy is to carry out strong reforms in these industries.