#01 The importance of the digital economy
With the end of the two sessions, as data people, we can see the high-frequency emergence of the digital economy in the spirit of the conference, and among the priorities of this year's economic and social development work, there is a plan to accelerate the construction of a modern industrial system, which mentions the digital economy and the digital transformation of enterprises.
It can be seen that the digital economy plays a pivotal role in modernization. The decision to establish the National Data Bureau in the State Council's institutional reform plan can also show the country's future investment and determination in the data economy.
#02 What is the digital economy
Digital economy, as a relatively broad concept, any economic form that directly or indirectly uses data to guide resources to play a role and promote the development of productive forces can be included in its scope. At the technical level, it includes emerging technologies such as big data, cloud computing, Internet of Things, blockchain, artificial intelligence, and 5G communication. At the application level, "new retail" and "new manufacturing" are typical representatives.
Therefore, the digital economy referred to here does not only refer to big data-related industries, but is a relatively broad concept. Whether it's the recently popular AIGC (AI Generated Content) product ChatGPT; or VR/AR; Whether it is 5G big data Internet, smart city, game interactive entertainment; Whether it is intelligent manufacturing, intelligent hardware, and digital publishing, it can be counted as the category of digital economy. Simply put, economic growth activities generated through digital information technology.
It can also be seen from the recent actions of exchanges on the digital economy that the digital economy covers a relatively wide range.
#03 Prospects for the digital economy
In 2022, the year-on-year growth rates of mainland GDP in a single quarter were 4.8%, 0.4%, 3.9% and 2.9%, respectively, while the information transmission, software and information technology services, which are the core industries of the digital economy, reached 10.8%, 7.6%, 7.9% and 10% respectively.
According to data from the Ministry of Industry and Information Technology, from 2012 to 2021, the scale of the mainland's digital economy increased from 11 trillion yuan to 45.5 trillion yuan, ranking second in the world for many years, and the proportion of digital economy in GDP increased from 21.6% to 39.8%. On March 11, the China Digital Finance Cooperation Forum held the 4th closed-door seminar on the analysis of the digital economy situation, and released the original research results of the forum, "2022 Mainland Digital Economy Financial Situation Analysis Report", which estimated that the scale of the digital economy may exceed 50 trillion yuan in 2022, accounting for about 41% of GDP.
According to the Global Digital Economy White Paper released by the China Academy of Information and Communications Technology, the digital economy of Germany, the United Kingdom and the United States dominates the national economy in terms of proportion, accounting for more than 60% of GDP in 2020. It can be expected that the digital economy will maintain rapid growth for a long time in the future.
However, there are also many problems in the field of digital economy that deserve in-depth consideration and solving.
First, does the scale effect of the digital economy necessarily lead to monopoly?
Second, how to strike a balance between the efficiency of big data analysis and the protection of personal privacy?
Third, will digital platforms facilitate or curb economic innovation?
Fourth, how can the digital economy better help the mainland realize its vision of common prosperity?
#04 Opportunities and challenges of big data in the digital economy
To put it bluntly, in data governance, big data plays the role of the basic chassis, in fact, it is storage, calculation, management, and use. There are four broad areas involved:
4.1 Data center construction under ultra-large scale
With the rapid development of the digital economy, the scale of data is growing exponentially, and how traditional big data can meet the needs of massive scale and complex format of big data storage has become a problem. The data center industry has evolved over the decades, from data rooms to data centers to today's cloud data centers. With the rapid development of new technologies such as artificial intelligence, cloud computing, big data, and 5G, data centers are entering the next golden era, and while ushering in a surge in market demand, they are also facing problems such as difficult access to construction resources, long construction period, high energy consumption, and many challenges in architecture flexibility and operation and maintenance. How to build a data center with low cost, good utilization and high performance has become a hot topic.
With the globalization of economy and trade, digital economy products will inevitably have foreign users, and the globalization of data centers has become a trend.
4.2 Efficient processing of big data
With the exponential growth of big data, data dynamic tilt, sparse association, complex application, high processing cost and poor timeliness of traditional big data processing architecture, a number of big data processing technologies such as Spark, Flink, etc. have emerged in recent years. In order to cope with efficient queries, a number of excellent OLAP columnar databases, federated queries, data lakes, data warehouses, lake warehouses and other big data concepts are also in full swing.
Big data processing not only solves the problem of statistical analysis of data, but also often has to deal with various data processing needs such as pictures, videos, and algorithm models. Separation of storage and computing, and serviceless computing have also become a trend in the context of the globalization of data nodes and the exponential expansion of data.
4.3 Data Governance
How to ensure data security and quality, how to ensure data privacy and how to establish data standards in big data processing has become a top priority topic, and has also become the main bottleneck restricting the development of big data.
Only by making data orderly and compliant can it provide useful value for the above industrial applications. Imagine that your personal phone address can be obtained at random on the Internet, driverless cars get the wrong road data, the application is down at every turn or finds inconsistent data, then how to talk about data application.
According to the "Digital China Development Report (2021)", from 2017 to 2021, the mainland data production increased from 2.3ZB to 6.6ZB, and the proportion of data production in 2021 was 9.9% in the world, ranking second in the world. Therefore, cultivating the data element market, improving the supply capacity of data elements, building a data governance system, giving full play to the advantages of massive data and rich application scenarios, and ensuring the high-quality development of the digital economy are a major issue in China's modernization construction. Regarding data governance, the author has explained in detail in the previous "Hierarchy of Requirements for Data Governance", explaining its importance in big data processing.
4.4 Data application and digital intelligence
Data storage, computing and governance, in the end, is to generate value for data, data application and mining to make data generate value, is the core of the data economy. Key data application areas include:
- internet
- Research field
- Marketing field
- Financial sector
- Telecommunications
- Smart manufacturing
"Digitalization" and "digital intelligence" are two completely different concepts, "digitalization" is a technical concept, and "digital intelligence" belongs to the application of digital technology, digital intelligence mainly includes data mining and artificial intelligence.
Data mining is a computer-aided technique used for analysis to process and explore large data sets. With data mining tools and methods, organizations can discover hidden patterns and relationships in their data. Data mining transforms raw data into practical knowledge. Companies use this knowledge to solve problems, analyze the future impact of business decisions, and improve profit margins.
Artificial intelligence is a very challenging science, artificial intelligence is a very broad science, it consists of different fields, such as machine learning, computer vision, etc., in general, one of the main goals of artificial intelligence research is to enable machines to perform some complex tasks that usually require human intelligence. From last year's Web 3.0 to this year's ChatGPT, we can see that everyone is constantly exploring digital intelligence. In short, making data generate value can be called a data economy.
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