Academician of the Chinese Academy of Engineering and vice president of the Chinese Institute of Communications, Wu Hequan, describes "Big Data" as "a combination of data that does not have the means to capture, manage, and process the content in a permissible period of time with conventional software tools. " He also pointed out that the size of the big data itself standards are constantly changing, previously called massive data, now data than massive data is also a large number.

In short, "big data" is characterized by: the amount of data is very large, the variety of data, data growth rate accelerated, data from a variety of sources, the data must be processed, the data has a directionality. The vast ocean of "big data" is extremely useful, accomplishing the "impossible" tasks of the past.

In the U.S. President's Council of Advisors on Science and Technology submitted to the President and Congress in a report called "Planning for the Digital Future", clearly mentioned "how to collect, manage and analyze data is increasingly becoming a top priority of network technology research. Advanced data analytics based on machine learning and data mining will facilitate the transformation from data to knowledge and the leap from knowledge to action."

The difference between the era of "big data" and the data that existed in the past is that, due to the difference in the amount of data, the mining workload of "big data" has increased rapidly; in particular, the source of the data is broader, and through the exchange, integration, and research, it is possible to discover market trends, the needs of market participants, and the needs of market participants. Through the exchange, integration and research, it is possible to find out the market development trend and the needs of market participants, so that enterprises can find business opportunities for themselves, and create new value for enterprises when business opportunities are in hand. In contrast to the role of "Big Data", if existing data can provide similar help to the enterprise, only in the periphery, "Big Data" can really get to the heart of the matter. To do this, it is necessary to use simulations and complex calculations that are extremely fast to accommodate the volume of work that can be done in a time-bound manner.

Of course, "big data" can be false due to human error, misrepresentation, and operational error. Therefore, in order to maximize the accuracy of the data, a large number of mathematical models are required, and the conclusions of the analysis can be obtained intuitively. Among other things, the presence of multiple sources of data improves the completeness of the conclusions. By multi-source data, we mean that for the same thing, it is collected its multi-faceted, multi-latitude and multi-form recorded data. Especially when used for forecasting, it is also important to focus on historical data and compare the two to narrow the mapping gap between the past and future predictions.

In addition, the end result of "big data" should also attract enough attention. The recent outbreak of the U.S. "Prism Gate incident", on the surface, is the U.S. government's theft of intelligence. In fact, it reflects the problem of how and to whom "big data" should be displayed. In particular, "big data" can explore the human mind to a certain extent, it highlights its importance.

Thirty years ago, commercial banks used the traditional abacus accounting, bookkeeping records of various types of data, today, the computer operation, electronic data collection, and the resulting formation of massive data.

Compared to "big data", past data is too fragmented, insufficient continuity, single source, monotonous form, unable to express the customer's transaction behavior, transaction preferences and transaction habits and other personality characteristics, the bank is also unable to know the specific reasons why the customer likes or dislikes the bank's products, as well as satisfied with the bank's products and services. The bank is also unable to know why customers like or dislike banking products and whether they are satisfied with banking products and services. The big data can make up for these shortcomings.

The core competitiveness of a commercial bank is externally reflected in its market share and comprehensive evaluation by the market; internally, it is the maximization of shareholders' interests and employees' satisfaction. To realize the core competitiveness, the source is the market and customers. "Big Data" can play an important leading role in developing the two sources. The Economist wrote in a report that "In the past, these data were stored in different systems, such as financial systems, human resources systems and customer management systems, which were never connected to each other. Now these systems are connected to each other, and through the technique of 'data mining,' a complete picture of business operations can be obtained, which is called: consistent truth."

The main ways in which "big data" can be expected to help commercial banks in the future are, first, to know their customers in a radically different way than they did in the past. "Big Data" not only allows banks to grasp the customer's present, but also to understand the customer's history, and to make short- and medium-term predictions through the exchange and mapping of data.

Second, it allows for multi-channel interactions with customers and a comprehensive assessment of customer satisfaction with the bank's own products and services. Commercial banks through their own and public **** information aggregation channels to grasp the data, to analyze, help to improve and enhance the product range and quality of service, in the first time to fight for the initiative.

Third, "big data" has become one of the main means of commercial bank competition, and its completeness and accuracy will determine the outcome of commercial bank competition. "Big Data" has become a real "double-edged sword" in the competition, and both competitors can utilize the data to formulate competitive strategies.

Fourth, commercial bank marketing tools are based on "big data" to carry out targeted sales.

Fifth, commercial bank risk management has changed dramatically. Commercial bank risk management model is inseparable from the data. The diversity and richness of data in "big data" can make up for the shortcomings of insufficient data in the past, and ultimately bring a leap in management methods.

Sixth, diverse financial models compete with traditional commercial banks. The first step is to make sure that you have a good understanding of how to use the Internet and how to use the Internet. In a sense, it is also a sign of the "big data" era, new ways to save transaction costs will continue to emerge.

In the era of "big data", commercial banks should actively do a good job of responding to the work.

First of all, the large amount of data generated by commercial banks in their daily operations is an important part of the formation of the whole society's "big data", so it is necessary to make the right disposition of data control, data processing and data results reflection.

First, the data control should be in accordance with the standardized collection, unified processing, timely completion, and hierarchical access. Insist on the collection of accurate data, the results can be visualized, so that the applicability of the data greatly improved; secondly, the data processing must be scientific, in accordance with the rules, in particular, to eliminate the phenomenon of false, second best; thirdly, the results of the processing, in accordance with the provisions of the display, and strictly in accordance with national laws and regulations for the use of, to avoid the impact of the commercial bank reputation risk matters.

Secondly, commercial banks need to invest a lot of resources to adapt to the needs of "big data" technology. In this regard, the investment of resources must be quite forward-looking, and take into account the current reality. In the transition period, we try to maximize the use of resources as much as possible.

Finally, commercial banks should attach great importance to the manpower reserve to adapt to the "big data" technology. The U.S. has predicted that in order to adapt to the "big data" era, the U.S. will need 600,000 people with data analytics expertise, and understand the industry knowledge of the composite talent. This kind of talent only after the university training is far from enough, but also need a wealth of practical experience. China's commercial banks on the reserve of such talent is quite insufficient, grasp the human resources to prepare more urgent.

In addition, the "big data" era will lead to changes in the way society transacts business, such as commercial banks, most of which do not need to experience the greater impact of the service industry, practitioners and physical outlets will tend to reduce. The increase and decrease of the contradiction is becoming more and more obvious, we should strategically focus on the early layout.

Commercial banks have to be high on system construction. In the future, the commercial bank's products and funds provided mainly by the data flow to realize. Similarly, the trend of virtualization of services will allow more services to be undertaken by the network. This on the one hand, commercial banks need to use the social network, on the other hand, its own system construction must also be matched with this, a strong system is the future of commercial banks business management tool.

Similarly, commercial banks should focus on the use of social media data to expand channels to obtain customer information. Learn to use all types of media, not only for customer service, but also for the optimization of the commercial bank's own image. Actively participate in the various ways of operation formed by network tools, and study the integration of commercial banking work objectives in the way of operation. Really make the media, network tools to maintain and expand customer bridge and important channel.