Along with behaviour of consumers and enterprises, the big data of e-commerce generated in real time widely spread over e-commerce platform, social media, intelligent terminal, inner-enterprise system and other third-party service platform.There are various types of e-commerce data, containing not only trading information and basic information of consumers, product information and transaction information of enterprises but also reviews information, behavior information, social information and location information of consumers, etc. The impact of mobile intelligent terminal on e-commerce becomes bigger and bigger, whose characteristics such as mobility, convenience and privacy promote the rapid development of mobile e-commerce, and result in a large number of electronic business data. It will become the main competitiveness of e-commerce enterprises to excavate and create value for e-commerce data.

The new generation of information technology, such as big data, promotes the integration of cross-boundary data from various channels, and enterprises in the value chain to connect each other and form as a whole. Centering on consumer demand,the enterprises in different geographical distribution form a dynamic alliance and integrate each links such as research and development, production, operations, warehousing, logistics, services and so on as one to create, push differentiated products and services through collaborative operation and result in intelligent and rapid reaction mechanism.In the age of big data, enterprises realize new value creation through information opening and sharing, resource optimization and division of labor.

1. Mass Data

The e-commerce system that produces mass data which are growing faster and faster and results in slower data query and report generation, at low utilization rate. Is it garbage or value? How to manage and use it?

2. Data Cognition

The simple analytic statistics chart available in most of traditional (RP system, order system, operational system, supply chain system are in single data format, with poor flexibility, low interactivity, so that managers feel difficult with proper cognition of all data.

3. Management Decisions

During Management Decisions, key data fail to be quickly extracted from the underlying data to drive operation directions by data, and Management Decisions have to be made only by statistical statements provided by operation department, order department, supply chain department or from various discrete systems.

·The Overall Architecture Diagram is as follows:

The whole system covers 4 layers:

1. Data layer: integrate information system for e-commerce enterprise to break information isolated islands and realize data sharing.

2. Modeling layer: set up different analysis topic marts according to concerns about indicators in aspects of data decision-making, sales, and operations

3. Business layer: tease out e-commerce business indicators and push analysis results to display layer.

Around membership, analyze and explore data relating to users’ location, their associated source platforms, online-and-offline behavior, residence time and payment methods, etc., to optimize and promote decisions, and improve the ability to capture new users.

Around products, analyze and explore data from the view of a single product, products in single type, price, profit, etc., for improvement of core competitiveness of commodities and optimization of inventory management.

Around profits, analyze and explore data from the view of the customer unit price, effective capacity of management, area-effectiveness, profit on year-on-year basis, profit of link relative ratio, effect of sales promotion and so on to unhurriedly master fair pricing and develop a sales strategy.

Around services, analyze and explore data from the view of customer feedback, after-sales rate, supervisory control of return & refund, member update frequency and so on to help e-commerce enterprises to reasonably avoid risk and promote competitiveness of product quality.

Around flow, analyze and explore data from the view of users, members, profits, sales volume, etc., brought by various platforms to improve the traffic load and reasonably expand platform service providers.

Around supply chain management, data analysis is carried out for supply time, supply cycle, supply quality on each supply chain, so as to effectively select, judge and optimize supply chain.

4.Display layer: in rich and perfect charts by flexible method of interaction, analysis results are presented to all administrative staff playing various roles .

1. Basing on e-commerce data analysis platform supplied by Yonghong, decision-makers and management may have insight into the operational state of the whole enterprise.

2. Yonghong helps users to easily cope with the rapidly growing data of e-commerce platform and realize a second response to the calculation and presentation of any business on the basis of specific data.

3. All operating departments can carry out partial self-service analysis so as to meet their demand for real-time exploration and analysis.

4. Capability to quickly respond to new analysis needs and changes, so as to improve work efficiency.