The wave of economic globalization has swept the world, and the information industry has increasingly become the dominance of the modern economy, and is transitioning to the era of the digital economy. Let digital participate in decision-making is the value of enterprise informatization construction. With the continuous development of international market integration, information technology integration and information resource networking, enterprises must adapt to the fierce global market competition, take the initiative to respond quickly to the market, win the initiative in market competition, and only speed up enterprise information The process of digitization is not enough, information digitization and intelligence are needed .
In the era of "everything is number", many things can be measured by data. Therefore, when the whole world is digitized, its inherent laws may be recognized and insightful through data management and analysis. Data has become a means for human beings to recognize and change the world, and data is constantly changing people's way of life, economic laws and business models, and even driving innovation and change in the entire economy. Data asset management will play an increasingly important role
However, at present, many enterprises do not realize the value of information digitization in the process of informatization construction, and there are some common problems on the road of informatization construction. affect the survival of the business. These questions include:
1. Data islands with systems as units, resulting in information asymmetry
At present, many enterprises have built application systems suitable for their own development, which are used for the daily management and operation of enterprises. However, due to various reasons, these systems have not been comprehensively planned and implemented, resulting in the existence of system data in their respective systems, and data sharing and intercommunication cannot be performed. As a result, the data is only in the data islands of its own system, which can easily lead to inaccurate and asymmetric information.
2. The system-based data management cockpit is not comprehensive, making it impossible for the leadership to make decisions
At present, many enterprises have built relevant information systems to provide data support for enterprise development decisions. However, due to various reasons, these systems have not been comprehensively planned and implemented, resulting in the existence of system data in their own systems. The so-called data-based statistics are only limited to the statistical analysis of the internal data of the system. The data dimension of statistics is only one-sided and cannot be Data interoperability cannot fully exploit the value of data. Since the basic data for statistical analysis of data only comes from the system's own data, to a certain extent, the data analyzed is not comprehensive, which leads to statistical deviations in the data, which cannot provide more comprehensive and accurate information value for the decision-making level, thus forming a basic Unusable mismanagement of the cockpit can seriously affect business survival.
For example: the sales system analyzes that a product sells well and requires vigorously speeding up production; the production system analyzes that there are not enough personnel and does not have the production capacity; the financial system warns that no payment is received or that there is excess cost, and the human resources system analyzes that there are a large number of personnel, With production conditions and so on. If these useful and valuable data cannot be analyzed quickly and comprehensively, decision-makers are prone to obtain incorrect statistics. In this way, enterprises are either likely to miss business opportunities, or fail to invest due to errors in risk assessment, which will bring business risks to the enterprise, and in severe cases can affect the operation of the enterprise.
Diapers and Beer ( Classic Big Data Mining Application Case )
A story about diapers and beer. The giant commercial retail chain, Wal Mart, has one of the largest data warehouse systems in the world. In order to accurately understand the purchasing habits of customers in its stores, Walmart analyzes the shopping basket association rules of its customers, so as to know which products customers often buy together. The detailed raw transaction data of all its stores is collected in Wal-Mart's huge data warehouse. On the basis of these raw transaction data, Wal-Mart uses data mining tools to analyze and mine the data. A surprising and unexpected result appeared: "Beer is the most purchased commodity with diapers"! This is the result of analyzing historical data with data mining technology, which reflects the inherent laws of the data. So is this result in line with reality? Is it a useful knowledge? Is it useful?
In order to verify this result, Wal-Mart sent market researchers and analysts to investigate and analyze this result. After a lot of actual investigation and analysis, they revealed a behavioral pattern of American consumers hidden behind "diapers and beer":
In the United States, going to the supermarket to buy baby diapers is an after-work routine for some young fathers, and 30% to 40% of them also buy some beer for themselves. The reason for this phenomenon is: American wives often tell their husbands not to forget to buy diapers for their children after get off work, and husbands bring back their favorite beer after buying diapers. Another situation is when husbands suddenly remember their responsibilities while shopping for beer and go shopping for diapers. Since there are so many opportunities for diapers to be bought with beer, Walmart has put diapers and beer side-by-side in all of their stores, and the result is an increase in both diapers and beer sales. According to conventional thinking, diapers and beer have nothing to do with each other. If it hadn't used data mining technology to mine and analyze a large amount of transaction data, it would have been impossible for Walmart to discover this valuable law in the data.
The simplest application: supplier-related systems need to enter supplier management staff information, production systems need to enter production staff information, sales systems need to enter sales staff information, CRM systems need to enter employee information responsible for related customers, financial systems need to enter Enter all employee information. And this information was originally in the enterprise OA system. This can easily cause problems such as repeated data entry, inaccurate data, and data asymmetry, resulting in unreliable data. There may be errors in entering the name, ID number, gender, etc.
The traditional method is to solve the problem through batch export and import, manual addition or system reconstruction, which involves huge costs and risks. To realize the connection of the data interfaces of each system, it only takes 1 hour to complete the connection of each interface with this product at the fastest. To complete the data integration of each system, it is only necessary to adjust the data interface of the original system.
3. Manage data for the purpose of managing data, increasing the burden on enterprises
Because enterprise informatization has not achieved overall and comprehensive statistical analysis, and has not allowed existing data to generate value, it has become the management of data for the purpose of managing data, thereby increasing the burden on the enterprise, affecting the productivity of employees, and failing to achieve true enterprise digitization. The first allegiance of chemical construction is deviated from each other.
Take personnel information management as an example:
Enterprise production system, company OA, sales system, CRM customer relationship system, etc. need to be managed. The relevant person in charge needs to enter and manage multiple times, which greatly reduces work efficiency and data accuracy. At the same time, these data cannot achieve cross-system statistics
, the real value of the data is not realized! Manage data for the sake of managing data
4. Cross-system and cross-business data statistical analysis requires customized development.
Since most of the existing business systems of most enterprises are independent of each other, the data is also independent. In order for the management to obtain comprehensive and accurate statistical services and find important data support for important decisions, the enterprise management cockpit must break the data island of system-based units and collect data from different regions, applications, systems and environments. useful data. It is basically impossible to rebuild the existing system of the enterprise, so it is necessary to carry out a secondary upgrade of the existing system, and collect data from heterogeneous systems through customized interface development or export and import manual methods, which is tantamount to increasing the burden on the enterprise. There will also be a large number of enterprises who will pay for informatization statistics, just to support data for accurate decision-making and help enterprises make major decisions.
Industrial Internet - Big Data Exchange Channels for Industrial Enterprises - Data Channel Products:
Intelligent Four Axes (Tong, Rong, Upgrade, Wisdom), solve the problems of data islands, data untrustworthiness, low efficiency, unreliable data, etc., and build an enterprise-level big data center The platform helps enterprises to intelligentize information and data.
1. Data islands with systems as units, resulting in information asymmetry
At present, many enterprises have built application systems suitable for their own development, which are used for the daily management and operation of enterprises. However, due to various reasons, these systems have not been comprehensively planned and implemented, resulting in the existence of system data in their respective systems, and data sharing and intercommunication cannot be performed. As a result, the data is only in the data islands of its own system, which can easily lead to inaccurate and asymmetric information.
The simplest application: supplier-related systems need to enter supplier management staff information, production systems need to enter production staff information, sales systems need to enter sales staff information, CRM systems need to enter employee information responsible for related customers, financial systems need to enter Enter all employee information. And this information was originally in the enterprise OA system. This can easily cause problems such as repeated data entry, inaccurate data, and data asymmetry, resulting in unreliable data. There may be errors in entering the name, ID number, gender, etc.
The traditional method is to solve the problem through batch export and import, manual addition or system reconstruction, which involves huge costs and risks. To realize the connection of the data interfaces of each system, it only takes 1 hour to complete the connection of each interface with this product at the fastest. To complete the data integration of each system, it is only necessary to adjust the data interface of the original system.
This product
1. Open up the meridians
At present, many enterprises have built application systems suitable for their own development, which are used for the daily management and operation of enterprises. However, due to various reasons, these systems have not been comprehensively planned and implemented, resulting in the existence of system data in their respective systems, and data sharing and intercommunication cannot be performed. As a result, the data is only in the data islands of its own system, which can easily lead to inaccurate and asymmetric information.
The simplest application: supplier-related systems need to enter supplier management staff information, production systems need to enter production staff information, sales systems need to enter sales staff information, CRM systems need to enter employee information responsible for related customers, financial systems need to enter Enter all employee information. And this information was originally in the enterprise OA system. This can easily cause problems such as repeated data entry, inaccurate data, and data asymmetry, resulting in unreliable data. There may be errors in entering the name, ID number, gender, etc.
The traditional method is to solve the problem through batch export and import, manual addition or system reconstruction, which involves huge costs and risks. To realize the connection of the data interfaces of each system, it only takes 1 hour to complete the connection of each interface with this product at the fastest. To complete the data integration of each system, it is only necessary to adjust the data interface of the original system.
Second, the integration
Solution: Data authority and relevance issues. The data is originally related, but the data between the systems is not related, unknown, etc., so that the data can be integrated
For example, employee attendance systems, OA, CRM, ERP, sales systems, etc., all have employee information. Traditional informatization construction is used for each, which can easily cause problems such as inconsistency of employee information. Errors such as name, ID, etc. At the same time, it also occurs that the employee no longer exists, but there are still problems in other systems. If the person in charge is the person in charge of an area, there are dozens of people below, and the employee information is managed by the human resources department of the head office, it may be that the person in charge assigns tasks to him without knowing that the employee is gone , which is very easy to bring business risks to enterprises, which is not conducive to the development of large enterprises.
3. Improve internal strength
Solution: Problems such as multiple data entry, incorrect entry, low efficiency, etc.
Once entered, a shared interface is provided externally, and other systems only need to import references and licenses. For example, OA manages employee information, and other CRM, ERP, sales systems, etc. only need to use it to view employee information and assign tasks, without worrying about employee information errors.
Four,
Since most of the existing business systems of most enterprises are independent of each other and the data is also independent, the management cockpit of the existing system of the enterprise only analyzes the information of the system itself, and the data itself is not comprehensive, and the statistical results are obtained. will deviate from reality.
If the management wants to obtain comprehensive and accurate statistical services and find important data support for important decision-making, the enterprise management cockpit must break the data island of system-based units, from the existing different regions, different applications, different systems, different Collect useful data in the environment.
To achieve this demand, it is basically impossible for enterprises to rebuild existing systems. It is necessary to upgrade the existing system a second time, and collect data from heterogeneous systems through custom interface development or export and import manual methods, which is tantamount to increasing the burden on enterprises. , just to support data for accurate decision-making and help enterprises make major decisions.
