Big Data enterprise application scenarios

The feature of the big data era is to use big data and big data technology to create value. The big data enterprise application scenario is to introduce the application of big data in the industry and how to enhance the commercial value of big data. The application scenarios of big data will increase enterprises' attention to big data, and encourage enterprises to invest more energy in big data and use big data to seek benefits for enterprises, use big data for the benefit of human society. Today, let's start with big data in enterprise applications:

1) Medical Industry

The medical industry has a large number of cases, pathology reports, cure plans, Drug Reports, and so on. If the data can be organized and applied, it will greatly help doctors and patients. For example, a hospital in Suzhou has the following challenges:

1) It is difficult to integrate large amounts of data. hospitals have ERP systems, electronic medical records, inventory, and drug collection systems. It is difficult to integrate data from various platforms and systems to form isolated islands.

2) management cannot perceive the input and output of departments, and data accumulation lacks mining, unbalanced input and output ratios of departments, and it is difficult to monitor KPI indicators. The big data magic mirror processing solution is: customized analysis and mining, business intelligence implementation, hadoop data warehouse, and so on. The final result is the successful integration of massive data, the potential value of data, and decision-making. Multiple mining dashboards such as KPI statistics, financial analysis, and department Rio are successfully implemented to effectively support the decision-making support of the management layer.

2) Financial Industry

Big Data has a wide range of applications in the financial industry. Typical cases include the Agricultural Bank. as the management of the e-bank department of Suzhou Agricultural Bank, I want to know the number of deposits of enterprises of different scales, however, this analysis does not support the latitude of an existing report. Therefore, you can only read a large number of reports to manually judge the report. With the magic mirror analysis, you only need to drag the corresponding dimensions and measurements to get the desired analysis results. Precision Marketing, service tracking, performance appraisal and other aspects of help.

Another typical case is AIA. In the face of a vast list of customers, each insurance clerk calls 200 calls a day to find two or three target customers. With the magic mirror analysis, on average, you can call 10 phone calls every day to find a target customer. The magic mirror allows insurance personnel to find the target customer. As the management of insurance companies, if you want to know the average premiums of people of different ages for different types of insurance, you need to view the traditional reports: age stage-average premium, different types of insurance-average premium, age stage-different stages, three reports, not only time-consuming and laborious, but not intuitive enough. Magic mirror provides various intelligent analysis functions such as customer mining, precise advertising, secondary development, strategic guidance, and national analysis.

3) E-commerce

E-commerce is the first industry to use big data for Precision Marketing. In addition to Precision Marketing, e-commerce can prepare goods for customers in advance based on their consumption habits, and use convenience stores as goods tracking points, deliver the goods to the door within 15 minutes after the customer places an order to Improve the customer experience. No. 1 is China's first online supermarket. It ranks top 3 in China's e-commerce market and was acquired by Walmart in 2012. The customer challenge faced by No. 1 stores is that they do not know the reasons for the loss of customers, their loyalty, and how to convert massive data into commercial values, through the Intelligent Analysis of big data Magic mirror, we can successfully locate customers with high loyalty, develop precise marketing strategies, and analyze and predict the customer's purchasing behaviors and habits.

4) Retail Industry

The big data application in the retail industry has two aspects: one is that the retail industry can understand the customer's consumption preferences and trends, carry out precision marketing of goods, and reduce marketing costs. The other layer is based on the customer's purchase of products, to provide customers with other products that may be purchased, increase sales, also belongs to the scope of Precision Marketing. In addition, the retail industry can grasp future consumption trends through big data, which is conducive to the purchase management of Hot-selling goods and the handling of seasonal goods. The data in the retail industry is very valuable to product manufacturers. retailer data information will help them effectively use resources and reduce overcapacity. manufacturers can produce according to actual needs based on Retailer information, reduce Unnecessary production waste.

The big data era has not suddenly appeared. In fact, over the past few decades, mathematical analysis has already moved into the financial industry. Harry, the Nobel economics prize winner. markitz, William. sharp, Robert. nagel uses the knowledge of metering economics and financial market data to establish a mathematical model to predict the relationship between product benefits in financial markets and risk fluctuations. The big data era opens another era in which human society uses the value of data.

Big Data enterprise application scenarios