The key points of big data applications are data sources, productization and value creation; uneven distribution of data resources, big data applications in data-intensive areas are more likely to get a breakthrough; must be inappropriate industry management model reform to promote the application of big data in the existing industries.
Big data is valuable in the application. Currently, at the national level, the State Council issued the "Outline of Action for the Promotion of Big Data Development"; at the local level, big data as a strategic engine for regional development; at the enterprise level, all kinds of big data concept companies in the ascendant, booming development. We are solely concerned about the application of big data, focusing on where the data comes from, how the data is used, and who pays for the results, that is, the three key points of data source, productization and value creation. A good big data application may be technically complex, but it should be simple, straightforward and workable in terms of business model. We are also concerned about the existence of a number of "data-intensive" industries or fields where big data applications may be easier to develop. In terms of industrial policy, we are concerned about big data as a new industry, the tried and true practices of the past, such as giving land, money and projects, will continue to be effective?
Three key points in the application of big data
The State Council's "Outline of Action for Promoting the Development of Big Data" (referred to as the "Outline of Big Data") positions big data as a "new-generation information technology and service industry", assigns it the strategic function of "promoting the development of economic restructuring," "reshaping the country's competitive advantage," "and enhancing the ability of government to govern," and defines data as a "basic national strategic resource. and defines data as a "fundamental national strategic resource". In terms of application, the Big Data Outline puts forward many development directions in the field of public ****, such as scientific macroeconomic regulation and control, precise government governance, convenient commercial services, efficient safety and security, and inclusive livelihood services; at the industrial level, it is mainly divided into industrial big data, big data for emerging industries, big data for agriculture and rural areas, big data for public innovation, as well as big data product systems and big data industry chains by industry sectors. industry chain. These directions, only the potential and space for the application of big data, can not be applied, can not play a role, but also depends on whether there is a viable model and practical results. Whether in the field of public **** or at the industrial level, the application of big data can not be separated from the source of data, processing technology and methods, and the mode of creating value, which is the focus of our attention. To summarize, the following three seemingly simple, but critical questions need to be answered. (a) Where does the data come from With regard to the source of data, it is generally recognized that the Internet and the Internet of Things are the bases for generating and carrying big data. Internet companies are born big data companies, in the search, social, media, transactions and other respective core business areas, accumulate and continue to generate massive amounts of data. IoT devices are collecting data every moment, and both the number of devices and the amount of data are increasing day by day. These two types of data resources, as big data gold mines, are continuously generating all kinds of applications. Most of the successful experiences about big data introduced abroad are classic cases of the application of such data resources. There are also some enterprises that have accumulated a lot of data in their business, such as real estate transactions, commodity prices, and consumption information of specific groups. Strictly speaking, these data resources are not considered big data, but for commercial applications, is the most accessible and relatively easy to process data resources, but also more common in the current domestic application of resources. In China, there is also a category of data resources held by government departments, which are generally considered to be of good quality and high value, but with a low degree of openness. The Big Data Outline takes public ****data interconnection and openness ****sharing as the direction of efforts, believing that big data technology can realize this goal. In fact, for a long time, information and data between government departments have been closed and fragmented from each other, which is a governance problem rather than a technical problem. Public **** data openness for the community is a very good wish, I am afraid that for a period of time is unattainable. In terms of data resources, the domestic "small data" "in the data" application is not sufficient, trying to step into the era of big data, and take the opportunity to solve the problems that have not been solved in the process of informatization in the early days, the prospects are not optimistic. In addition, since the business of Chinese Internet companies is mainly domestic, their big data resources are not global. Where does the data come from is our first concern in evaluating big data applications. One is to see whether the application is really supported by data, whether the data resources are sustainable, whether the source channels are controllable, and whether there are any hidden dangers in terms of data security and privacy protection. The second is to see how the quality of data resources for this application is, whether it is a "rich mine" or a "poor mine", and whether it can guarantee the effectiveness of this application. For data resources from its own business, it has better controllability and data quality is generally guaranteed, but the data coverage may be limited, and it is necessary to rely on other resource channels. For data crawled from the Internet, technical capability is key, both the ability to obtain a sufficiently large volume and the ability to filter out useful content. For data obtained from third parties, special attention needs to 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 source of data, even better and more sophisticated data analysis techniques are useless. (ii) How to use the data How to use the data is the second point of concern in our evaluation of big data applications. Big data is only a means to an end, and it can not be used for everything and for nothing. We are concerned about what big data can and cannot do, and now it seems that big data mainly has the following more commonly used functions. Tracking. The Internet and the Internet of Things are recording all the time, and big data can track and trace any record to form a true historical trajectory. Tracking is the starting point for many big data applications, including consumer buying behavior, purchase preferences, means of payment, search and browsing history, location information, and more. Identification. On the basis of comprehensive tracking of various factors, accurate recognition can be achieved through positioning, comparison, and screening, especially for voice, image, and video, so that the analyzable content is greatly enriched and the results obtained are more accurate. Portrait. Through the tracking, identification and matching of different data sources of the same subject, a more three-dimensional portrayal and a more comprehensive understanding is formed. For consumer portraits, advertisements and products can be accurately pushed; for enterprise portraits, credit and risk can be accurately judged. Tip. On the basis of historical trajectory, identification and portrait, predict future trends and the possibility of recurrence, and give hints and warnings when certain indicators show expected changes or over-expected changes. Previously, there were also statistically based predictions, and big data has greatly enriched the means of prediction, which is of profound significance in establishing risk control models. Matching. Accurate tracking and identification in massive information, using relevance, proximity, etc. to screen and compare, and more efficiently realize product tying and matching of supply and demand. The big data matching function is the basis for new business models of the Internet car dating, rental housing, finance and other *** enjoyment economy. Optimization. Optimize the allocation of paths, resources, etc. by various algorithms according to given principles such as shortest distance and lowest cost. For enterprises, to improve the level of service, enhance internal efficiency; for the public **** department, to save public **** resources, enhance public **** service capacity. Currently many seemingly complex applications, most can be subdivided into the above types. For example, Guizhou to implement the "Big Data Precision Poverty Alleviation Project", from the perspective of big data applications, through the identification, portrait, you can achieve accurate screening and definition of poor households, to find the target of poverty alleviation; through the tracking, prompting, poverty alleviation funds, poverty alleviation behavior and poverty alleviation effects of monitoring and evaluation; through the matching, optimization, you can better play the role of poverty alleviation resources. These functions are not all unique to big data, only that big data far exceeds previous technologies and can be made more powerful, more precise, faster and better. (C) Who pays for the resultsWho pays for the results is the third and final concern in our evaluation of big data applications. The reason is simple, the application that does not create value is not a good application. We are concerned about whether the application of big data actually enhances capabilities and improves performance. If big data is used for its own product design, marketing and promotion, and resource allocation, then we look at whether the competitiveness of the enterprise is improved, and whether the enterprise is ultimately more profitable than before. If big data is used to provide services for third parties, it depends on whether someone is willing to pay, willing to continue to pay. But if it is used in the field of public ****, but also to see the government or public **** department of the payment is worth it, not only from the perspective of the contributor to see whether it is worth it, but also from the perspective of the people to see whether it is worth it. When we face a big data application, just simply ask the above three questions - where the data from, how to use the data, the results of who pays the bill, you can uncover a lot of "camouflage". Of course, if you can withstand the above "big data three questions", is not necessarily considered excellent, but also from the excellent big data applications is not far away. Looking for data-intensive areas since big data is regarded as a resource, it is necessary to consider the problem of resource distribution. Generally speaking, the distribution of resources is extremely uneven, such as water, minerals, arable land, energy and other natural resources; the distribution of human resources and knowledge is even more uneven. Big data is also uneven distribution of the problem? Can the development of big data industry really bend the road to overtake the car? These questions deserve in-depth consideration. Unlike natural resources that can be detected, the distribution of data resources is difficult to locate and portray. However, the distribution of big data human resources can be used to indirectly reflect the differences between the application of big data in the region and industry, which industries and regions are intensive in big data human resources, these industries and regions can be regarded as data-intensive. We screened the recruitment information released by the two mainstream recruitment websites "MileagePlus" and "Wisdom Link Recruitment" since the second half of 2014, and obtained that the two websites had released relevant information involving 227,000 enterprises and 1,007,000 positions in the past two years***, which is indeed a large enough amount of data. Through the sub-region, sub-industry summary analysis, the results show that the distribution of big data human resources is extremely uneven, each region, each industry varies greatly. However, to be precise, through the recruitment website reflects the demand for talent, not strictly the distribution of human resources stock, but the two are closely related. From the point of view of the place of work for big data-related jobs, Beijing, Guangdong and Shanghai are highly dense, far ahead of other regions. Together, the three places accounted for 52.35% and 47.48% of the number of companies posting job information on the two sites, and 61.23% and 56.74% of the number of jobs. It can be presumed that half of Big Data HR is concentrated in these three places, which is highly consistent with our usual intuition. Beyond these three places, we are concerned whether local governments that emphasize the big data industry and use big data as an engine of regional economic development may promote the concentration of human resources, and may outperform other regions with similar levels of economic development as their own. As reflected in the data, at least for the time being, no such result can be seen, which reveals that the human resource structure is the most important shortcoming to be remedied and the most difficult difficulty to be overcome for the development of big data industry in late-developed regions. Changing the composition of human resources in a place is much more difficult than changing the architectural landscape on the ground, and either requires a long-term process or a unique system. Even within the same province, the distribution of big data human resources is extremely uneven. In Guangdong, for example, the city of Shenzhen alone accounts for roughly half of the province. Together with Guangzhou, it is surprising that it can reach 90%. Other places, even if the economic strength is not bad, but compared with Shenzhen and Guangzhou, in terms of big data human resources is far from it. This again shows that the distribution of big data human resources is extremely uneven. Obviously, the basis for developing big data industry in big data human resource-intensive areas is better than that in human resource-poor areas. In terms of city ranking, North, Shanghai, Shenzhen and Guangzhou can be regarded as first-tier cities with intensive demand for big data human resources, and Hangzhou, Nanjing, Chengdu, Wuhan and Xi'an can be regarded as second-tier cities. The distribution of big data human resources is roughly consistent with the city's economic strength, vigor and even the level of housing prices. In terms of industry distribution, the demand for big data human resources is even more unevenly distributed, mainly concentrated in the Internet, information technology and computer-related industries. This fully demonstrates that big data is part of the Internet or IT industry, and is a new development on the original foundation. These industries are typical "data-intensive" industries, which are the cradle of the development of the big data industry. Finance is another particularly important "data-intensive" area. The financial industry is not only the base for generating data, especially valuable data, but also the demand and application of data analysis services. More importantly, the financial industry has sufficient payment capacity and will be an important battleground for competition in the big data industry. Many big data are radiated to various industries through applications in the financial sector. In addition, telecommunications, professional services (such as consulting, human resources, accounting), education and training, film and media, online games, etc., are also relatively more data-intensive industries at present. "Big Data Outline" is almost comprehensive for all industries and fields are planned for the broad prospects of big data applications, but the distribution of data resources is extremely uneven, in the "data-intensive" areas of big data applications, the likelihood of market success is greater. What kind of industrial policy is needed for big data? What kind of industrial policy is needed for big data applications? From the application point of view, big data is not a brand new industry, but with the existing industrial integration, the existing model of transformation, upgrading and replacement. Constraints on the development of big data is often not the big data itself, but the big data applied to the industry and the field of the original problems, such as industry regulation, administrative monopoly, factors can not be free flow, and so on. Therefore, promoting the development of big data by giving land, posting money and launching projects will not solve the fundamental problems. It is necessary to reform the improper industry management model and adjust the established interest pattern from the perspective of big data application field, so that big data application has the necessary conditions. Even within the enterprise, the application of big data is not just a technical issue, but involves the reorganization of business processes and changes in the management model, which is a test of the management capabilities of the enterprise. Financial, telecommunication, education, film and media "data-intensive" industries are not only areas with great potential for big data application, but also key areas for urgent industrial reform. On the other hand, the application of big data can also provide technical support for industry reform, can be more effective technical route to achieve industry development goals.
The industrial policy needed for the application of big data is in fact the policy due to the development of various industries under the market economy, such as liberalization of access, fair competition, reducing the burden on enterprises, eliminating the discrimination of enterprise ownership, eliminating the discrimination of enterprise size, and so on. Only in an open industrial environment, big data can be effectively used in these industries. If a place wants to vigorously promote the use of big data in the fields of finance, health care, education, etc., the most useful policy is to carry out a strong reform of these industries.