What challenges does enterprise big data management face?
What challenges does enterprise big data management face?
The four major challenges are explained in detail. At present, the domestic industrial data circulation system is still imperfect.
Although there are many data trading centers active in the market, the legal compliance of data circulation has not received due attention, and the current laws do not address many issues in data circulation.
To be clear, many jobs are still carried out in an industry self-disciplined model.
The demand for data circulation in industrial enterprises is increasing day by day, and the sharing and opening of standardized data cannot be delayed.
In recent years, with the continuous advancement of informatization and the gradual deepening of industrial Internet applications, the data of industrial enterprises has shown an explosive growth trend.
The role of industrial data analysis and mining in promoting enterprises to reduce costs, increase efficiency, and enhance competitiveness has become increasingly prominent.
At the end of 2018, the China Academy of Information and Communications Technology and the Industrial Internet Industry Alliance conducted a round of research on the current situation of data management in chine's industrial enterprises.
The survey results show that the data management work of industrial enterprises lags behind other industries, and the data foundation is far from being able to support the needs of intelligence.
At the same time, problems such as industrial data security risks and circulation also need to be solved urgently.
Informatization in the industrial field started relatively late, and industrial data is more complex, involving multiple links such as R&D, production, management, operation and maintenance, and services. Therefore, the advancement of data management is also relatively lagging behind.
With the continuous deepening of the development of the Industrial Internet, the importance of strengthening big data management in the industrial field has become increasingly prominent.
Similar to other industries, the focus of big data management industrial is to ensure data quality and security, promote data interoperability, and provide high-quality, highly reliable basic data resources for industrial intelligence.
The survey shows that the data resources of industrial enterprises in chine are generally not large, and 66% of the enterprises have a total data volume of less than 20TB, which is less than one-tenth of the daily incremental data volume of a provincial-level telecom operator; the management methods are relatively backward, 51% of enterprises are still using documents or more primitive methods for data management; data silos are common, and enterprises lack a comprehensive understanding of their available data assets.
No matter in terms of management means or technical means, it is far from supporting the risks and complexity of data management after the development and application of the Industrial Internet.
1. Big data management: The implementation of data management is lagging behind
The importance and understanding of data is the premise of big data management.
With the popularization of informatization in industrial enterprises and the rapid development of the industrial Internet, industrial enterprises are gradually increasing their awareness of the importance of data management, but the actual implementation situation is worrying.
The survey shows that 98.6% of enterprises feel that big data management is worth investing in, and 77% of them firmly believe that big data management is important, and believe that big data management is a long-term process that will bring value to the enterprise.
However, only 32.4% of enterprises have carried out big fdata management related work, and nearly half of the enterprises have not planned dedicated human investment.
2.Big data management The introduction of centralization brings new security risks
With the extensive use of sensors and the collection of data brought by emerging technologies such as cloud computing and big data, industrial data and applications have shown a rapid growth trend, and the storage and management of enterprise data is facing new challenges.
When industrial enterprises choose to locally deploy industrial Internet platforms or cloud service providers for data and business hosting, the primary considerations are cost and security.
According to 2018 statistics, reducing infrastructure investment and meeting the rapid expansion of resources are important reasons for enterprises to choose public cloud, but the biggest concern is still public cloud security; more than 50% of enterprises will choose public cloud for security and availability.
Control the choice of private cloud. Industrial enterprises pay special attention to security, especially when they look at the issue of data migration to the cloud, they still maintain a cautious attitude.
Following the trend of data fusion and interconnection, the traditional method of physical isolation and data closure to ensure security is no longer suitable for the data environment in the new era.
However, the trend towards centralization of data to platforms creates new potential security risks. In July 2018, sensitive documents of more than 100 manufacturing companies, including General Motors, Fiat, Chrysler, Ford, etc., were at risk of being leaked due to vulnerabilities in the storage servers of their partner suppliers.
The frequent occurrence of cloud service security incidents and the fact that the industry has not yet formed a complete data asset pricing and compensation mechanism has caused industrial enterprises to have a grudge against data migration to the cloud.
3.Big data management Strong demand for circulation but great resistance
The value of the Industrial Internet is reflected in the sharing of data across fields and industries.
With the development of data application requirements and big data technology, industrial enterprises have increased demand for data cooperation.
According to the survey, more than half of enterprises indicated that they need to use external data or provide data externally.
The growth of big data management flow demand will bring many problems and challenges, such as data quality, data pricing and data flow compliance.
During the field visits, a large number of enterprises expressed that they have great concerns about data cooperation between enterprises due to the lack of clear legal and regulatory guarantees and the support of mature technical solutions.
At present, there has been a period of research on data circulation at home and abroad.
4.Big data The data base is still weak
The concept of big data management was born with the application of database technology in various industries in the 1980s, in order to store and access data in computer systems more efficiently.
In the process of development and evolution of big data management, a number of core management functions including data quality, data security, data standards, data interoperability, data sharing and circulation have been formed, and the availability and ease of use of data are realized in a systematic way. , so as to better utilize the value of data.
Since 2000, industries with a high degree of informatization such as banking and telecommunications have faced increasingly large and complex data assets. business analysis and decision support needs.
What challenges does enterprise big data management face?
The four major challenges are explained in detail. At present, the new generation of information and communication technology represented by big data is accelerating the integration with traditional industries, giving birth to new technologies and applications such as industrial big data, medical big data, financial big data, and agricultural big data.
At the same time, big data is also helping the development of artificial intelligence technology.
It can be said that big data is empowering all walks of life and bringing more convenience to our production and life.
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