2022 top ten keywords of big data

The following is the 2022 top ten keywords of big data recommended by recordtrend.com. And this article belongs to the classification: big data.

The big data industry has witnessed a number of major changes in recent years. On the one hand, data factorization once again emphasizes the strategic position of data. On the other hand, laws, regulations and standards such as the data security law and the personal information protection law further clarify industry norms. At the same time, data technology innovation and application innovation are also evolving rapidly.

The “2022 big data industry summit”, directed by the China Academy of information and communications and the China Communications Standardization Association and hosted by the big data technology standards Promotion Committee of the China Communications Standardization Association, was recently held in Beijing. At the meeting, hebaohong, director of the cloud University Institute of the Chinese Academy of communications, released the “top ten keywords of 2022 big data”, summarizing and analyzing the latest development trends of the big data industry.

Key words 1: innovative database, optimizing the process of data Resourcing

As a core technology product supporting data storage and computing, database is undergoing rapid technological innovation in order to meet the relevant needs of data elements.

On the one hand, AI database and serverless cloud native database promote the value mining of data from “spring snow” to “xiariba people”. Using data is no longer a feature of data intensive industries such as finance and telecommunications, but has become a universal behavior of the whole society and industry. This leads to the process of data processing and utilization needs to be more civilian and efficient.

AI database has the ability of automatic operation and maintenance, intelligent development and so on. Serverless cloud native database has the ability of billing by volume, elastic expansion and so on, which can achieve cost reduction and efficiency increase in the process of data processing and utilization.

On the other hand, tamper proof database and fully confidential database support data to complete efficient right confirmation pricing and convenient compliance circulation. On the basis of efficient storage and calculation, the tamper proof database provides data tamper proof and operation tamper proof functions, so as to support the pricing of data confirmation, while the fully encrypted database can realize the efficient storage and calculation of data in the encrypted state, so as to support the compliant circulation of data. Both have become research hotspots of academic institutions and suppliers.

Key word 2: graph computing platform helps large-scale graph data Resourcing

Unlike traditional determinant data, graph data is a data model that efficiently describes entities, attributes, and relationships through point and edge models. In recent years, it has been widely used in enterprise intelligent marketing risk control and other necessary data applications.

With the deepening of the intelligent transformation of industry data, the proportion of graph data in the total data is also rising rapidly. Gartner predicts that the proportion of graph technology in data and analysis innovation will rise from 10% in 2021 to 80% by 2025.

With the increasing scale of graph data, the era of “big data” of graph data has been opened. The traditional relational database originated in the 1980s and the special graph database originated in about 2000 have been unable to support the efficient storage and calculation of large-scale graph data.

Through the abstract computing layer and integration layer, the graph computing platform enhances the compatibility and large-scale data computing ability based on the graph database, and realizes the efficient aggregation of graph data in a variety of storage media and the complex computing ability in the case of multi hop.

At present, the policy support in this field is increasing, the open source system is developing rapidly, and commercial products are emerging in endlessly, which quickly supports the value release of graph data, an important element type.

Key word 3: data center becomes the core engine for enterprises to mine the value of data elements

With the deepening of enterprise digital transformation, data related systems and organizations are becoming more complex and redundant, and barriers are gradually increasing.

In order to build a set of reusable data and analysis capabilities within the organization or enterprise, reduce the redundancy of the data itself and related technical architecture, and break through the barriers between different system data, the data center came into being.

Its theoretical system has gradually focused and clarified from the “contention of a hundred schools of thought” in the early stage of development, and has reached a consensus in the industry that the data center has built a backbone network between data resources and business value, and is “the core engine of enterprise digital intelligence transformation”.

In recent years, due to the continuous promotion of the digital transformation policy, the data center has developed rapidly, and Gartner marks it as the highest expected value in the maturity curve. The domestic supply side of this field has developed rapidly, suppliers have been constantly enriched, and relevant landing cases on the application side have increased rapidly.

Key word 4: dcmm standard implementation leads industry data governance

Dcmm is the first national standard in the field of data management in China, which provides an objective evaluation basis for enterprise data management, guides enterprises to systematically build a data management framework and continuously optimize data management capabilities.

After nearly three years of development, dcmm has been widely recognized, and the effectiveness of the implementation evaluation is accelerating. In the process of cultivating a unified market of data elements, dcmm standard implementation evaluation can improve the data capacity and data vitality of various market entities, bridge regional differences, align the level between industries, and expand the high-quality supply of data resources, so as to improve the circulation efficiency of data elements and guide the efficient accumulation and orderly accumulation of data resources.

In order to continuously promote the improvement of enterprise data management ability, the Ministry of industry and information technology issued the “national standard implementation work plan for enterprise data management”, supporting industrial subsidy policies across the country, and promoting the standard implementation evaluation in key regions and industries. It is estimated that by 2025, there will be more than 10000 standard implementation evaluation enterprises and more than 150000 publicity and training personnel.

Key word 5: Data valuation becomes the entry point of data capitalization

The exploration process of data valuation has developed with the development of enterprise digital transformation.

Gartner put forward the evaluation framework of information value in 2015, which is measured from six dimensions: information intrinsic value, information business value, information performance value, information cost value, information market value and information economic value. However, this framework mostly stays at the conceptual level, only clarifying the main impact factors, and does not propose specific measurement indicators and methods.

The comprehensive digital transformation of Chinese enterprises began roughly in 2015 and entered an explosive period after 2017. While recognizing the value of data, enterprises have invested huge human, material and financial resources. Therefore, there is an urgent need for a set of valuation indicators to clearly quantify the value of data and evaluate the effectiveness of digital transformation.

Since the beginning of 2021, some enterprises have successively carried out research and practice of data valuation. However, we should also recognize that data valuation is still in the early stage of development, and the valuation purpose and valuation framework need to be explored and verified in specific scenarios.

After analyzing the results of industry data valuation, we believe that data products can be used as valuation objects, and valuation is actually a measure of the indirect economic value of data contribution to business development, as well as the direct economic benefits obtained by trading data as commodities. Therefore, data valuation is a comprehensive work covering data management, data application, data trading, and AI modeling.

Key words 6: dataops defines a new mode of data development and Application

The concept of dataops was first proposed by foreign scholars in 2014, and then the industry gradually supplemented its connotation. In 2018, it was officially included in Gartner’s data management technology maturity curve, thus entering the international perspective.

In 2022, the China Academy of communications and communications officially led the construction of dataops standards, based on which to promote the diversified development of China’s big data industry. As a good medicine to help enterprises complete the transformation of digital intelligence, dataops is competing on the supply side and the demand side. Many manufacturers and enterprises have adopted the concept of dataops to build a new generation of data research and development tool platform and have achieved great results.

In terms of standardization, this year, the ICT academy led the joint efforts of more than 30 units in various industries to develop standards. The standard includes 7 modules and 25 links, aiming to promote the solid development of China’s data culture.

Key word 7: privacy computing all-in-one machine helps break the situation of data element circulation

This year is the first year of the application of privacy computing. The application of multiple scenarios has accelerated, and the all-in-one privacy computing machine has opened up a new path for applications.

First, as a special device integrating software and hardware, the software implementation scheme is enhanced by using hardware characteristics. Its three advantages of security reinforcement, performance acceleration and ease of use enhancement make the privacy computing all-in-one stand out from many engineering optimization schemes, reducing the technical threshold and comprehensive cost for users.

The second is that the technology of all-in-one machine is not unique, and all products bloom. It can be based on trusted hardware or encryption card, and use computing accelerator card or network accelerator card at the same time. It can also pre install application service scenario components, and the combination scheme is diversified. The combination of multiple hardware and multiple angles has become the development trend of the combination of software and hardware, and has emerged in the scenes of finance, government affairs, medicine and so on.

Third, there are various forms of products, and there is an urgent need for standardization. Many standards at home and abroad have taken the lead in standardizing technology R & D and application. However, it is also worth noting that it is not the only one that can break through the application bottleneck and expand the application scale. In the face of the huge demand for data security circulation, we still need to continue to explore more easy-to-use landing solutions.

Key word 8: data element policy from macro to implementation

At the beginning of this year, the special layout of data elements in the 14th five year plan for the digital economy and the reform plan for the market-oriented allocation of factors made the exploration in the field of data elements rise again, and the policy promotion and industrial practice are constantly deepening and innovating.

First, the top-level design has been gradually refined. The state has made arrangements for the development of data elements at the height of the national unified market, and made specific arrangements for the market access of data elements in Shenzhen demonstration zone. The construction of data infrastructure system is also accelerating.

Second, local regulations have been issued one after another. At present, 19 provinces and cities have announced relevant data regulations, which are based on promoting data utilization and industrial development, and focus on public data, so as to further stimulate the vitality of market players in combination with local realities and characteristics.

Third, the continuous innovation of trading mode, the optimization of the operating structure of local data exchanges, the formulation of trading rules in Guiyang, the construction of a digital commerce system in Shanghai, and the creation of an open source community in Shenzhen have provided more tangible support for data trading.

However, we are still far from the full release of the value of data elements, the consensus on data ownership and pricing has not been established, and problems such as data leakage and ultra vires abuse have exacerbated people’s mistrust. How to establish an effective rule system and regulatory mechanism, and how to use cutting-edge technology to solve difficult problems still need the joint efforts of all sectors of government, industry, University, research and application.

Key word 9: Data Security compliance has entered a new stage as a whole

With the promulgation and implementation of the two laws in 2021, data security supervision in all walks of life has been strengthened, and compliance work has entered a new stage.

First of all, in order to correctly understand the regulatory content and effectively implement the regulatory requirements, all walks of life have set off a wave of learning policies and regulations.

Secondly, as an important work in the field of data security, data classification and classification is also a necessary ability to achieve refined security management, which has also become the focus of this round of learning boom.

Thirdly, in order to promote the implementation of enterprise data security in this industry, some industry competent units started the supervision and submission work. Finally, in the supply side market, some enterprises began to develop compliance management tools to assist the demand side to realize the automation of regulatory response.

Key words 10: data classification and classification take the lead in data security governance

As the basic content of data security work, data classification and classification is a necessary prerequisite for the fine management of data security, which needs to be first implemented in the data security governance project. With the development trend in methodology consensus, industry refinement, tool development, etc., data classification and classification are also listed in the top ten keywords.

First of all, classification and grading, as one of the concepts explicitly mentioned in the data security law, has attracted research and discussion from local governments, industries and enterprises, and gradually formed a “seven step” methodological consensus from the establishment of organizational guarantee to the implementation of the corresponding level of data security control strategy.

Secondly, in order to guide the promotion and implementation of enterprise classification and grading work, all industries have formulated standards and specifications to clarify the principles, methods and definitions of classification and grading work, and further refine the relevant requirements.

Finally, automated classification tools or consulting services are booming in the data security provider market. According to the statistics of the “trusted digital security” evaluation system of the Chinese Academy of communications, the number of participating enterprises in the classification and grading tools or services increased from 4 in 2021 to 14 in 2022.

Summary of top ten keywords

The ten keywords in the 2022 big data field cover all aspects supporting the value release of data elements, such as policy, concept, safety, technology, etc. the rapid development of the content involved in these keywords further confirms that China’s data element market has gradually built a healthy development pattern of policy guidance, concept first, technical support, and safety escort in the process of rapid development.

This article is reproduced from: big data technical standards Promotion Committee

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