In recent years, around big data governance A subject and its related issues, there have been many practices and research and exploration work in the world. Such as the introduction of policies and regulations to promote data sharing and openness, data security and privacy protection at the national level, data management capability assessment and improvement for enterprise organizations, methods and technologies for data quality assurance, and technical specifications for data interoperability And standards, etc. Then, how should China better protect personal privacy in the construction of big data governance system and create a good environment for the development of big data industry?
The report of the 19th National Congress of the Communist Party of China puts forward: “Promoting the deep integration of the Internet, big data, artificial intelligence and the real economy.” The comprehensive implementation of the national big data strategy, the construction of the digital economy, and the construction of digital China in China Next, reviewing the history of big data development, the status quo of cognitive big data development, and grasping the trend of big data development are necessary to clarify the key directions and development points of China’s big data development.
First, the status of data as a basic strategic resource is increasingly prominent
The concept of “big data” starts from the field of computing and is rapidly applied to science and business. In the field, its great value and potential have been widely recognized by the whole society. A large number of enterprises, open source funds and venture capital have entered the field of big data, and the big data industry ecology has gradually formed at this time.
Overview of the development of the big data industry ecology at home and abroad, it is not difficult to find that its development focus has evolved over time.
About 2012 to 2013, the most influential technologies and products in the big data arena focused on basic technologies and infrastructure such as data aggregation, storage, and processing.
After 2014 to 2015, after the pre-development, a number of solutions for big data management and processing for specific application scenarios have been formed, and the data-driven artificial intelligence has made breakthrough progress. The enthusiasm for people to analyze data, extract information from data, knowledge and intelligence, data analysis methods, technologies and products and related companies have become the most active part of the big data ecosystem at this stage.
In 2016, although big data technology is still far from mature, the system has gradually become more complete, and the integration with traditional industries and industries is becoming more and more close. The big data applications and related enterprises for the industry and the field are developing rapidly. Become a new focus and the big data ecosystem is more mature.
After 2017, with the deepening of the application of big data in various industry fields, data has become increasingly prominent as a basic strategic resource, data validation, data quality, data security, privacy protection, and circulation control. Issues such as sharing and opening up are increasingly receiving high attention and inspiring deep thinking.
Academia and industry are aware that big data on the one hand brings a series of challenges to the existing information technology system, requires R&D investment and innovation development, on the other hand, it needs to create big data. The good environment for the healthy and orderly development of the industry, for this reason, the concept of big data governance has attracted attention and become a new hotspot of the big data industry ecosystem.
II. Big Data The three main problems of governance exist
Throughout the current research and practice at home and abroad, the author believes that there are still three major problems in big data governance:
First, the use of big data governance concepts is relatively narrow. Research and practice are mostly based on enterprise organizations, and only consider the issues related to big data governance from the perspective of an organization. The scope of big data governance is obviously not enough within the organization. Multi-source data aggregation and deep integration of data across organizations and cross-domains are the premise of demonstrating the value of big data. Data breaks through the need for organizational boundaries to flow. With the gradual improvement of data opening and circulation technologies and channels, data flow and application across organizations have taken place and are showing an increasingly common trend. Undoubtedly, this will be a problem involving multiple levels within and across industries, within and across regions, nationally and globally. The big data governance of enterprise organizations is inseparable from the norms and self-discipline of the industry, the “upper law” of the state and even the country. Inter-agreement or agreement, multi-level synergy can constitute the basic guarantee of big data ecological construction.
The second is that the existing research practice has not reached a consensus on the understanding of the content of big data governance. Different people combined with the characteristics of big data, from the different perspectives of enterprise business and management process design, organizational information governance planning, organizational data management and application, give different definitions of big data governance. For example, some people position big data governance as an organizational strategy or procedure that describes how data is useful throughout its lifecycle and its economic management. Some people are positioned to the data availability, availability, integrity, and security of the enterprise. Measures and their overall management, some people focus on the development of data optimization, privacy protection and data realization policies related to big data… The consensus will be reached.
The third is that the research practice related to big data governance has multiple clues, and the relevance, integrity and consistency are insufficient. For example, national-level policies and regulations and legal development are less included in the perspective of big data governance; the status of data as an asset has not been established through laws and regulations, and it is difficult to effectively manage and apply; big data management There are many technologies and products available, but there is still a lack of a comprehensive multi-level management system and an efficient management mechanism. How to organically combine technologies and standards, and establish a good big data sharing and open environment still needs further exploration; in addition to continuous improvement and development of related technologies In addition to the challenges of various new types of attacks, the enterprise security system, the industry self-regulatory mechanism and the mandatory means determined by the state through the law have yet to be improved; without systematic design, it may lead to the expansion and extension of existing systems to achieve data governance. Problems such as fragmentation and lack of consistency.
In the process of building a digital economy, building digital China and a network power, big data is not only a key element of the new economy, but also about national security and national comprehensive competitiveness. The size of a country’s big data, the activity of its data, and its ability to understand and use data will determine its insight, influence, and dominance over the world. Therefore, big data governance must jump out of the boundaries of individual organizations and consider it comprehensively and systematically from the perspective of creating a national big data industry development environment.
Three, Big Data Governance system construction requires the joint efforts of countries, industries and organizations
Based on existing research and practice, the author believes that the construction of big data governance system involves three levels of countries, industries and organizations, including assets. Four aspects of status establishment, management system, sharing openness, security and privacy protection, need to provide support from the system regulation, standard norms, application practices and supporting technologies.
National level: It is necessary to clarify the status of data assets at the level of laws and regulations, and lay the foundation for data validation, circulation, trading and protection; need to balance the status quo and development To build a good data management and coordination system and corresponding management mechanism suitable for the national conditions; to develop policies, regulations and standards that promote data sharing and openness, to achieve data sharing among government departments, to regulate data circulation and transactions among market entities, and to build a government The leading data open platform promotes the integration of government data and industry data; laws and regulations for data security and privacy protection are required to ensure data security for countries, organizations and individuals.
Industry level: Industry big data governance should fully consider the common interests and long-term development of enterprises in the industry under the relevant national laws and regulations, and build corresponding industry big data. Governance rules. It is necessary to establish an organization that regulates industry data management, and develop data management control within the industry; it is necessary to formulate rules and technical specifications for data sharing and openness within the industry, build an industry data sharing exchange platform, and provide data services for enterprises in the industry to promote industry The integration of data applications; the need to develop an industry data security system to ensure data security, rights and trade secrets of each member of the industry.
Organization level: It is necessary to determine the data as its core assets through internal regulations of the organization to facilitate effective management and application; to establish adaptive data resources, value realization, quality assurance, etc. Aspects of organizational structure and process specifications to enhance the company’s ability to manage data throughout its life cycle; need to promote data sharing between internal departments of the enterprise, and strengthen external data circulation and transactions, fully revitalize the value of data; need to combine the “superordinate method” and Its own management and technical measures protect the company’s own data security and customer data security and privacy information.
In this framework of big data governance system, three levels are interrelated and supported. The national level establishes the “upper law” for big data governance, and guides and supervises the big data governance of industries and organizations; the industry level forms industry associations or alliances on the voluntary principle through the model of industry autonomy, as a bridge between government and enterprises. Under the guidance of national laws and policies, formulate and implement rules and regulations and various standards, supervise the behavior of enterprises, and communicate the common needs of enterprises to the government; organizations in the framework of national and industry big data governance, for themselves The characteristics of determining big data governance goals, optimizing the management of big data resources, and maximizing the benefits from big data.