In the era of big data, how to apply data?

In recent years, big data has continuously penetrated into all walks of life in the world, affecting our food, clothing, housing and transportation. For example, when shopping online, we often find that e-commerce portals recommend goods to us, and these goods are often what we need recently. This is because the relevant data of users' online behavior trajectory will be collected and recorded, and the items that users may need will be recommended by using the recommendation system through big data analysis, so as to achieve the purpose of precise marketing. Let's briefly introduce several application scenarios of big data.

The application of big data in the medical industry Big data makes it easier to see a doctor. In the past, most of the treatment schemes for patients were carried out through the experience of doctors. Although excellent doctors can provide patients with good treatment plans, it is difficult to ensure that patients can receive the best treatment plan because of the different levels of doctors.

With the deep integration of big data in the medical industry, the big data platform has accumulated a large number of information resources such as cases, case reports, cure plans and drug reports. All common diseases and past cases are recorded, and doctors can give patients a high-quality and reasonable diagnosis and treatment plan through effective and continuous diagnosis and treatment records. This can not only improve the work efficiency of doctors, but also reduce the misdiagnosis rate, so that patients can get the best treatment in the shortest time. The applications of big data in the medical industry are listed as follows.

(1) Optimize the medical plan and provide the best treatment method.

Faced with a large number and variety of germs, viruses and tumor cells, it is also very difficult to determine the diagnosis and treatment of diseases. With the help of big data platform, we can collect the disease characteristics, cases and treatment plans of different patients, thus establishing a patient classification database in the medical industry. If the gene technology is mature in the future, patients can be classified according to the characteristics of gene sequence, and a patient classification database of medical industry can be established. When diagnosing patients, doctors can refer to the patient's disease characteristics, laboratory reports and inspection reports, and refer to the disease database to help patients diagnose diseases quickly and make clear the location of diseases. When making a treatment plan, doctors can obtain an effective treatment plan with similar genes, age, race and physical condition according to the genetic characteristics of patients, and make a treatment plan suitable for patients to help more people treat in time. At the same time, these data are also conducive to the pharmaceutical industry to develop more effective drugs and medical devices.

(2) Effectively prevent and predict diseases.

The simplest way to solve patients' diseases is to nip in the bud. Monitoring people's body data through big data, and collecting their health data and vital signs into databases and health files. It is a new trend of health service management in the future to promote integrated health services covering the whole life cycle of prevention, treatment, rehabilitation and health management through the application of big data analysis. Of course, this not only requires medical institutions to speed up the construction of big data, but also requires the masses to check regularly, update data in time, prevent and predict the occurrence of diseases through big data, and realize early treatment and early rehabilitation. Of course, with the continuous development of big data and its application in various fields, some large-scale influenza can also be predicted through big data.

The application of big data in the financial industry With the application of big data technology, more and more financial enterprises are beginning to devote themselves to the practice of big data application. A study by McKinsey shows that the financial industry ranks first in the value potential index of big data. The following are some typical applications of big data in the financial industry, as follows.

(1) precision marketing.

Under the impact of the Internet, banks urgently need to master more user information, and then build a 360-degree portrait of users, which can carry out personalized intelligent marketing such as precise marketing and real-time marketing for segmented customers.

(2) Risk control.

The application of big data platform can uniformly manage the internal multi-source heterogeneous data and external credit data of financial enterprises, and better improve the risk control system. Internal can ensure the integrity and security of data, and external can control user risks.

(3) Decision support.

Improve business decisions through big data analysis methods, and provide reliable data support for management, thus making business decisions more efficient, agile and accurate.

(4) Service innovation.

Through the application of big data, we can improve the interaction with customers, increase user stickiness, provide value-added services for individuals and governments, and continuously enhance the core competitiveness of financial enterprises.

(5) product innovation.

Through high-end data analysis and comprehensive data sharing, we can effectively connect various financial products such as banks, insurance, trusts and funds, so that financial enterprises can learn from other fields and create new financial products.

The application of big data in retail industry has a legendary story in American retail industry. A store sells diapers and beer side by side, and as a result, the sales of diapers and beer both increase! Why can two seemingly unrelated commodities get such amazing results together? Later, after analysis, it was found that most of these buyers were married men. These men bought diapers for their children and some beer for themselves. After discovering this secret, Wal-Mart stores boldly put beer next to diapers, which makes it more convenient for customers to buy and sales will naturally increase greatly.

The reason why we say "beer-diapers" is actually to tell you that tapping the potential value of big data is the core competitiveness of retail competition. The following are some innovative applications of big data in the retail industry, as follows.

(1) Accurately locate the retail industry market.

If an enterprise wants to enter or develop a regional retail market, it must first conduct project evaluation and feasibility analysis. Only through project evaluation and feasibility analysis can we finally decide whether it is suitable to enter or develop this market. What is the floating population in this area? What is the consumption level? What are the customers' consumption habits? What is the market's awareness of the product? What is the current market supply and demand situation, and so on? The massive information behind these problems constitutes the big data of market research in the retail industry, and the analysis of these big data is the market positioning process.

(2) Support industry revenue management.

The arrival of the era of big data provides a broader space for the development of enterprise revenue management. Demand forecasting, market segmentation and sensitivity analysis need a lot of data, while traditional data analysis mostly collects the historical data of the enterprise itself for forecasting and analysis, and often ignores the information data of the whole retail industry, so the forecasting results will inevitably be biased. In the process of revenue management, if enterprises can collect more retail industry data and understand more retail industry market information on the basis of their own data and rely on some automatic information collection software, it will promote the formulation of accurate revenue strategies and win higher revenue.

(3) tap new demand in retail industry.

As an enterprise in the retail industry, if we can collect the review data of the online retail industry, establish a large database of online reviews, and then use word segmentation, clustering, emotional analysis and other means to understand consumers' consumption behavior, value orientation, new consumption needs reflected in reviews and product quality problems of enterprises, so as to improve and innovate products, quantify product value, formulate reasonable prices and improve service quality, and obtain greater benefits from them.