5 thoughts on “What industries can big data be applied to”
Rosemary
1) The first category is the Internet and marketing industry. The Internet industry is the closest industry from consumers, and at the same time, it has a lot of real -time data. Business data is the basic element of its enterprise operation. Therefore, the degree of big data in the Internet industry is the highest. The marketing industry, which is accompanied by the Internet industry, is an industry analysis around Internet user behavior, thinking that consumers provide personalized marketing services as their main goals. 2) The second largest category is an industry with a relatively high level of informatization. The industries such as finance, telecommunications. They carried out information construction earlier, the informatization of internal business systems was relatively complete, with a lot of historical accumulation of internal data, and some deep -level analytical applications. The service stage. 3) The third category is the government and public utilization industries. The degree of informationization and dataization of different departments is large. For example, there are already many big data application cases in the transportation industry, but some industries are still in the stage of data collection and accumulation. The government will be the key to the rapid development of the entire big data industry in the future. The opening of government and public data can make government data online faster, thereby stimulating the big development of big data applications. 4) The fourth category is manufacturing, logistics, medical care, agriculture and other industries. The level of their big data application is still in the initial stage, but the C2B model driven by consumers in the future will force the big data application process of these industries to gradually accelerate.
Data first generate in different types, including non -structured data, semi -structured data and structured data. Big data obtains any original data and processs it into structured data. The company uses their past and current data to predict the future. Big data helps companies gain profits, expand business activities worldwide, and provide big data. It not only predicts future returns, but also helps predict future issues and trends. It helps enterprises to make major decisions.
1
manufacturing
 n product and service. The conventional and continuous tasks of the manufacturing industry generate a lot of data. Use industrial big data to improve the level of manufacturing, including product failure diagnosis and prediction, analysis process process, improvement of production processes, optimizing the energy consumption of production processes, analysis and optimization of industrial supply chain, production plans and scheduling.
Big data can help manufacturers reduce costs and waste, and help them create high -quality products in a shorter time. Big data allows manufacturers to predict future needs. Based on this, they can produce and supply in time, and eventually bring higher profits.
2
finance
 n three major data in high -frequency trading, social emotion analysis and credit risk analysis The field of financial innovation played a significant role. Under the environment of macroeconomic structure adjustment and gradually market -oriented, domestic financial institutions mainly show issues such as narrowing profit margins, urgent adjustment of business positioning, and core debt loss. Big data technology is an important means to help financial institutions to dig in depth, find market positioning, clarify the direction of resource allocation, and promote business innovation.
3
The retail catering industry

The retail catering industry is an industry that has the final contact with customers. It must record the customer’s data -including the taste, preferences and biological data of customers -in order to keep close to them. Customer relationship is one of the important ways to play a key role in business development. Using big data to achieve catering O2O models to completely change the traditional catering operation method. Big data provides accurate information to solve complex problems, which are related to the retail industry. We can clearly know what to do and when to use big data.
4

The inherent medical system has been fragmented, and the subversion has been like the fire of Liaoyuan. Send it when you touch it. Hundreds of thousands of startups have been involved in this field, allowing people to become “their own healthy owners”, so as to replace it as a supplement or simply for traditional medical care. The health department uses its patient records, the treatment plan, and the past to treat different diseases, which will help the department to improve treatment or provide better medical assistance to patients.
Big data can be applied to all walks of life, analyzing and organizing the huge data collected by people to achieve effective use of information. For example of this major, for example, looking for the main effect gene related to milk production at the genetic level of cow, we can first scan the full genome of the cow, although we have obtained all phenotype information and genetic information, but due to the huge amount of data, This requires a big data technology to analyze and compare it to dig the main effect gene. There are many examples. In general, big data is a large, dynamic, and continuous data. By using the use of new systems, new tools, and new models, we can obtain insight and new value. In the past, in the face of huge data, we may be observed and visible, so we cannot understand the true essence of things, so that we can get wrong inferences in scientific work, and the advent of the era of big data will be displayed in front of us.
The applications of various data in various industries are already wide. Different business scenarios, different analysis models, the following list of the typical applications of data analysis and some practical application cases in several industries. 1, insurance industry: Customer analysis: customer group preferences, potential customer groups, market public relations impact on customers, whether appropriate products will be provided to potential customers; product analysis: The rationality of the product pricing base, the benefits of the product, etc.; Capture analysis: risk control, service quality, lawsuit law, risk rate, fraud prevention, etc. ; Risk analysis: product risk, customer risk, claim quality, product calculation, and leading calculations. [Analysis of cases] For the front -end analysis of claims, the following Dashboard can be used to build the compensation situation of the entire institution, and further analysis according to the region, time, and category. The two indicators of the insurance industry are the most concerned: estimating the deviation rate, the time -effectiveness of the case, and do a separate data monitoring and warning. 2, telecommunications industry: product analysis: the rationality of the pricing base of the product, the risk of high yield products, low yields or losses products, etc. The relationship between the customer group and its products, the impact of various personalized services on the customer, and the method of increasing the customer’s maintenance rate; as well as the phone bill behavior, the analysis of the arrears behavior, etc. Forecasting foundation, product, price calculation; Channel analysis: channel user preservation rate, stock income and user conditions, channel marketing effect analysis, etc. This Simulation: Simulation simulation of preferential policies. [Analysis case] . For example, the channel of a telecommunications will make a full -year analysis of the telecommunications client to understand the penetration rate and activity of users, and the benefits of daily activities. 3, manufacturing: Cost analysis: Provide cost analysis models, effectively analyze the impact of fixed, change cost, cost control, etc.; budget management: cost control, benefits calculation, etc Inventory plan: Best Order Plan, Inventory Control, etc.; Product analysis: market analysis and product development strategy, market analysis and marketing strategy, product benefits, etc.; Customer analysis: customer group classification, customer and product products Relations, effective customers maintain means. [Analysis of cases] In heavy industrial manufacturers use the establishment of a supplier platform to present the supplier’s orders, finance, and quality management data through the form of reports, and set the criteria for comparison in time to achieve timely time Control. 4, banking, securities industry: Customer analysis: identify high -income groups, the impact of market activities on customers, customers maintain means, etc.; Risks; Credit management: Definition of customer credit levels, control credit risk; The transaction analysis: analysis of transaction category, industry type, transaction time, trading amount, transaction amount, trading number and other indicators and masters The law of trading behavior; C deposits analysis: Understand the types of deposits, coins, related populations, and time distribution. [Analysis case] In terms of customer management, daily monitoring of customers’ satisfaction can understand the impact of market activities on customers in real time, and record the activities with excellent effects. The analysis of bank deposits and loans of banks, understand the impact of various deposits categories and time. This above is only a small part of a small part of various industries. It focuses on the visual analysis of the front end. There is no detailed explanation of the data sources and data processing behind it. As an analyst, it is more about business -oriented, paying attention to analysis ideas, and honeing data analysis experience in practical exercises.
There are three main parts of big data basic knowledge, namely mathematics, statistics, and computers, and at the same time assist sociology, economics, medicine and other disciplines. can go here to see
1) The first category is the Internet and marketing industry.
The Internet industry is the closest industry from consumers, and at the same time, it has a lot of real -time data. Business data is the basic element of its enterprise operation. Therefore, the degree of big data in the Internet industry is the highest. The marketing industry, which is accompanied by the Internet industry, is an industry analysis around Internet user behavior, thinking that consumers provide personalized marketing services as their main goals.
2) The second largest category is an industry with a relatively high level of informatization.
The industries such as finance, telecommunications. They carried out information construction earlier, the informatization of internal business systems was relatively complete, with a lot of historical accumulation of internal data, and some deep -level analytical applications. The service stage.
3) The third category is the government and public utilization industries.
The degree of informationization and dataization of different departments is large. For example, there are already many big data application cases in the transportation industry, but some industries are still in the stage of data collection and accumulation. The government will be the key to the rapid development of the entire big data industry in the future. The opening of government and public data can make government data online faster, thereby stimulating the big development of big data applications.
4) The fourth category is manufacturing, logistics, medical care, agriculture and other industries.
The level of their big data application is still in the initial stage, but the C2B model driven by consumers in the future will force the big data application process of these industries to gradually accelerate.
Data first generate in different types, including non -structured data, semi -structured data and structured data. Big data obtains any original data and processs it into structured data. The company uses their past and current data to predict the future. Big data helps companies gain profits, expand business activities worldwide, and provide big data. It not only predicts future returns, but also helps predict future issues and trends. It helps enterprises to make major decisions.
1
manufacturing
 n
product and service. The conventional and continuous tasks of the manufacturing industry generate a lot of data. Use industrial big data to improve the level of manufacturing, including product failure diagnosis and prediction, analysis process process, improvement of production processes, optimizing the energy consumption of production processes, analysis and optimization of industrial supply chain, production plans and scheduling.
Big data can help manufacturers reduce costs and waste, and help them create high -quality products in a shorter time. Big data allows manufacturers to predict future needs. Based on this, they can produce and supply in time, and eventually bring higher profits.
2
finance
 n
three major data in high -frequency trading, social emotion analysis and credit risk analysis The field of financial innovation played a significant role. Under the environment of macroeconomic structure adjustment and gradually market -oriented, domestic financial institutions mainly show issues such as narrowing profit margins, urgent adjustment of business positioning, and core debt loss. Big data technology is an important means to help financial institutions to dig in depth, find market positioning, clarify the direction of resource allocation, and promote business innovation.
3
The retail catering industry

The retail catering industry is an industry that has the final contact with customers. It must record the customer’s data -including the taste, preferences and biological data of customers -in order to keep close to them. Customer relationship is one of the important ways to play a key role in business development. Using big data to achieve catering O2O models to completely change the traditional catering operation method. Big data provides accurate information to solve complex problems, which are related to the retail industry. We can clearly know what to do and when to use big data.
4

The inherent medical system has been fragmented, and the subversion has been like the fire of Liaoyuan. Send it when you touch it. Hundreds of thousands of startups have been involved in this field, allowing people to become “their own healthy owners”, so as to replace it as a supplement or simply for traditional medical care. The health department uses its patient records, the treatment plan, and the past to treat different diseases, which will help the department to improve treatment or provide better medical assistance to patients.
Big data can be applied to all walks of life, analyzing and organizing the huge data collected by people to achieve effective use of information. For example of this major, for example, looking for the main effect gene related to milk production at the genetic level of cow, we can first scan the full genome of the cow, although we have obtained all phenotype information and genetic information, but due to the huge amount of data, This requires a big data technology to analyze and compare it to dig the main effect gene. There are many examples.
In general, big data is a large, dynamic, and continuous data. By using the use of new systems, new tools, and new models, we can obtain insight and new value. In the past, in the face of huge data, we may be observed and visible, so we cannot understand the true essence of things, so that we can get wrong inferences in scientific work, and the advent of the era of big data will be displayed in front of us.
The applications of various data in various industries are already wide. Different business scenarios, different analysis models, the following list of the typical applications of data analysis and some practical application cases in several industries.
1, insurance industry:
Customer analysis: customer group preferences, potential customer groups, market public relations impact on customers, whether appropriate products will be provided to potential customers;
product analysis: The rationality of the product pricing base, the benefits of the product, etc.;
Capture analysis: risk control, service quality, lawsuit law, risk rate, fraud prevention, etc. ;
Risk analysis: product risk, customer risk, claim quality, product calculation, and leading calculations.
[Analysis of cases]
For the front -end analysis of claims, the following Dashboard can be used to build the compensation situation of the entire institution, and further analysis according to the region, time, and category. The two indicators of the insurance industry are the most concerned: estimating the deviation rate, the time -effectiveness of the case, and do a separate data monitoring and warning.
2, telecommunications industry:
product analysis: the rationality of the pricing base of the product, the risk of high yield products, low yields or losses products, etc. The relationship between the customer group and its products, the impact of various personalized services on the customer, and the method of increasing the customer’s maintenance rate; as well as the phone bill behavior, the analysis of the arrears behavior, etc. Forecasting foundation, product, price calculation;
Channel analysis: channel user preservation rate, stock income and user conditions, channel marketing effect analysis, etc.
This Simulation: Simulation simulation of preferential policies.
[Analysis case]
. For example, the channel of a telecommunications will make a full -year analysis of the telecommunications client to understand the penetration rate and activity of users, and the benefits of daily activities.
3, manufacturing:
Cost analysis: Provide cost analysis models, effectively analyze the impact of fixed, change cost, cost control, etc.; budget management: cost control, benefits calculation, etc Inventory plan: Best Order Plan, Inventory Control, etc.;
Product analysis: market analysis and product development strategy, market analysis and marketing strategy, product benefits, etc.;
Customer analysis: customer group classification, customer and product products Relations, effective customers maintain means.
[Analysis of cases]
In heavy industrial manufacturers use the establishment of a supplier platform to present the supplier’s orders, finance, and quality management data through the form of reports, and set the criteria for comparison in time to achieve timely time Control.
4, banking, securities industry:
Customer analysis: identify high -income groups, the impact of market activities on customers, customers maintain means, etc.;
Risks;
Credit management: Definition of customer credit levels, control credit risk;
The transaction analysis: analysis of transaction category, industry type, transaction time, trading amount, transaction amount, trading number and other indicators and masters The law of trading behavior;
C deposits analysis: Understand the types of deposits, coins, related populations, and time distribution.
[Analysis case]
In terms of customer management, daily monitoring of customers’ satisfaction can understand the impact of market activities on customers in real time, and record the activities with excellent effects.
The analysis of bank deposits and loans of banks, understand the impact of various deposits categories and time.
This above is only a small part of a small part of various industries. It focuses on the visual analysis of the front end. There is no detailed explanation of the data sources and data processing behind it. As an analyst, it is more about business -oriented, paying attention to analysis ideas, and honeing data analysis experience in practical exercises.
There are three main parts of big data basic knowledge, namely mathematics, statistics, and computers, and at the same time assist sociology, economics, medicine and other disciplines.
can go here to see