2. The connotation of big data

(1) data type

(2) Technical methods

(3) Analysis and application

3. The characteristics of big data

Diversity: With the development of Internet and the increase of sensor types, there are more and more unprocessed semi-structured and unstructured data, such as web pages, pictures, audio, video and Weibo, which are mainly unstructured data, with a sharp increase in quantity and various types. Unstructured data is more complex than structured data, and the difficulty of data storage and processing increases.

Timeliness: Timeliness of big data means that it can be processed in time and within a certain range in the case of a huge amount of data, which is the most striking feature that distinguishes big data from traditional data mining. Only by real-time creation, real-time storage, real-time processing and real-time analysis of big data can we obtain high-value information in time and effectively.

Value orientation: It contains a lot of deep value, and the analysis, mining and utilization of big data will bring great commercial value.

4. The difference between big data and traditional data

5. The background of big data

1. Classification according to big data structure

2. Classify according to the way big data is acquired and processed.

Step 3 classify in other ways

1. Increase sales opportunities

0. Sources of Business Big Data

1. Customer

2. Market

3. Commodities

4. Supply chain

0. Source of data

2. Marketing and Precision Marketing

3. Customer relationship management

4. Enterprise operation management

5. Commercialization of data

0. Source of data

2. Payment pricing

3. Research and development

4. New business model

5. Public health

1. Marketing

2. Service

Step 3 operate

4. Risk control

Big data finance refers to the use of big data technology and big data platform to carry out financial activities and services, cloud computing and other information processing of big data and external data accumulated in the financial industry, and financial financing and innovative financial services combined with traditional finance.

1. Network demo

A large number of financial products and services are presented through the network.

2. Risk management has been adjusted.

The concept of risk management-financial analysis (the first source of repayment), mortgaged property or other guarantees (the second source of repayment) will be less important.

Risk pricing method-pay more attention to the authenticity of trading behavior and the credibility of credit through data.

Evaluation of customers-all-round, three-dimensional/vivid.

The main means of risk management-customer identification and classification based on data mining.

3. Reduce information asymmetry

4. Improve the efficiency of financial services

Provide the right products to the right consumers in the right way at the right time and place.

5. Expand the service boundary of financial enterprises.

Due to the improvement of efficiency, its operating cost will inevitably be reduced, which is most suitable for expanding the scale of operation.

Financial practitioners will serve more individuals.

6. The product is controllable and acceptable.

For consumers, the income or cost of financial products and the liquidity of products presented through networking are acceptable, and their risks are controllable.

7. inclusive finance

The high efficiency and expanded service boundary of big data finance have greatly expanded the object and scope of financial services, and financial services are more grounded.

1. Quick loan, precise marketing and personalized service.

Based on a large number of long-term credit and capital flow big data, credit scores are calculated at any time, and online payment is used for real-time lending according to loan demand and credit scores.

2. Large customer base and low operating cost.

Big data finance is based on big data cloud computing, mainly based on the automatic calculation of big data, which does not require a lot of labor, has low cost, integrates fragmented demand and supply, and expands the service field to more small and medium-sized enterprises and small and medium-sized customers.

3. Scientific decision-making and effective risk control

The credit score is estimated according to the default rate of trading lending behavior and other related indicators, and the risk assessment model is made by distributed computing, which solves the problems of credit distribution, risk assessment, authorization implementation, fraud identification and so on, and effectively reduces the non-performing loan rate.

Based on the online transaction information formed on the e-commerce platform and the financial big data formed by online payment, advanced technologies such as cloud computing are used to process and analyze the data to form a credit or order financing model.

The typical representative is Ali Small Loan. Based on big data such as transaction data of e-commerce platform, user transaction and interaction information of social network, shopping behavior habits, etc., the accumulated credit data of online merchants on e-commerce platform is formed by real-time calculation and analysis of scores through cloud computing. Through the network credit rating system, financial risk calculation model and risk control system constructed by e-commerce, order loans or credit loans are issued to online merchants in real time. For example, Ali Small Loan can provide loans in a few minutes.

Enterprises use their upstream and downstream industrial chains (raw material suppliers, manufacturers, distributors and retailers) to fully integrate supply chain resources and customer resources and provide financial services.

JD.COM Mall and Suning.cn are typical representatives of supply chain finance.

In the supply chain finance model, the e-commerce platform only provides big data finance as an information intermediary, and does not bear financing risks and prevent risks. -Channel vendors are core enterprises.