In-Depth | Construction of Data Element Market, Empowering the Perfection of Digital Cities.

How to build the overall architecture of digital cities?

Dr.Liu Ligong：

The underlying foundation is to establish a data resource system and cultivate fertile ground for data   assets, which mainly includes the following construction paths:

Firstly, it is necessary to establish a super intelligent storage system. In this system, different storage   media such as Blu-ray discs, magnetic disks, mechanical hard drives, and solid state drives are integrated, taking advantage of the unique characteristics of optical,   electrical, and magnetic media in terms of data read quality and efficiency; it also provides unified storage space, allowing these three types of media to be   managed together. Therefore, after data enters the system, it can be categorized into hot, warm, and cold types and distributed across different media, thus completing the overall operation of the distributed unified   storage system. I believe this will be an inevitable architecture for data storage in the construction of digital   cities. In this sense, many manufacturers in our country have already put it into practical operation, which not only optimizes data governance at the storage level but also accumulates corresponding data assets. However, considering the current international situation and China's development status, apart from achieving   technological breakthroughs, we must also possess our own independent capabilities.

Secondly, proactive support from the government side is essential. First of all, after recognizing the role of the government cloud platform as a foundation, we need to focus on the information innovation capability of the government cloud. For example, our country has issued relevant policies requiring that by 2027, all   government clouds will be information innovation clouds. So how can we achieve the goal of information innovation? We can develop a unified cloud management platform based on the super storage system, gradually   cloudify data governance capabilities, and turn our data resources into assets that can be safely utilized. Next   is the capability of archiving government data. Of course, in terms of government data, much of it needs to be archived for long-term preservation, which is closely related to the previously mentioned super intelligent storage system. Therefore, in the process of building the government cloud foundation and cultivating fertile ground for data assets, we must achieve green and long-term data storage, cloudified governance capabilities, and efficient archiving capabilities for cold data—all of which are essential conditions for building the cloud foundation.

So what do we do on top of data storage and governance? What we need to do is to build government data and data from all sectors of society into data asset packages, and on this basis, cultivate the ability to mine the value of data so that it can support our insight into urban vital signs and intelligent decision-making   management. For example, in urban management, citizens' data is currently distributed across various   government departments. To achieve convergence or interoperability of these data, we can use this capability to form a lifelong thematic data asset package for each person; similarly, we can gather and interoperate   enterprise-related data to form a 360-degree thematic data asset package for enterprises, which can also serve as a hub for our city's smart management. Additionally, we can use large model technology to categorize various government documents and materials to form a government GPT, generating various   auxiliary results through dialogue from the perspectives of the government, enterprises, and residents. For   instance, as a resident, if I want to inquire about how to handle certain matters, what documents to bring   when handling affairs, or what conditions need to be met, all these can be resolved using large model   technology, allowing ordinary people to fully enjoy the fruits of digitalization while making government   services more efficient. At the data application level, we can also combine tools such as text analysis, video   algorithms, and large model technology to develop services in application scenarios such as industrial   development, talent attraction, policy realization, and enterprise services. For example, in some cities, parking has   always been a major problem. Can we use an offline terminal plus online platform business model, cooperate with various C-end travel service platforms, develop a shared app with reminders, and build a function that actively pushes idle parking space information to make full use of urban spatial resources through digital means? Furthermore, traditional investment attraction methods used by local governments often rely on incentives such as funds, land, and talent introduction. However, we can also attractinvestments through data. Data-driven investment attraction includes two aspects: one is analyzing manufacturers suitable for the local conditions of our city through data, and the other is analyzing how to provide better services to resident enterprises through data. For example, cameras can be installed on buses at tourist attractions to conduct slow live broadcasts, thereby attracting more visitors to our tourist spots.   These are various scenarios for mining the value of government data.

Dr.Liu Ligong：

Firstly, it involves the establishment of a digital urban management and service system. This includes   familiar systems such as integrated network management and online services that many cities have implemented, which aim to achieve unified management and service delivery through the integration of government   systems. For instance, Jinniu District in Chengdu has built an entire urban vital signs system based on "public   data," encompassing 52 primary indicators, 122 secondary indicators, and 164 tertiary indicators, generating   179 early warning indicators. This setup provides real-time perception and monitoring capabilities for urban   risks and district- level urban operations, effectively integrating management and services. Additionally,   traffic data has become a full-chain product that can be traded, with the focus shifting from information   system construction to data asset operation and trading. Examples include the TOC platform by the Ministry of Transport and Beijing's Transportation Commission, as well as projects at Daxing Airport and Jilin   Expressway, all of which reflect the empowerment of digital transportation within the digital urban   management service system.

Secondly, there is the construction of a data element market. Although many cities have begun exploring   ways to turn data into assets for financing, loans, and even external investments, a unified model for the data element market system has yet to emerge. The path forward for advancing the construction of the data   element market, as I summarize here, involves the development of "4 Centers + 5 Platforms."

The first is the Registration and Certification Center, which must clarify the rights to hold, process, and   operate data to avoid disputes during data transactions. This is the starting point for building the entire data   asset framework. Based on current construction experience, especially in the data registration module, it is   essential to carry out the six main registration types: initial registration, permission registration, transfer   registration, change registration, cancellation registration, and objection registration. In addition,   comprehensive legal regulations are necessary to ensure proper certification. The second is the Asset   Assessment Center, which should pay attention to the Ministry of Finance's interim provisions on the   inclusion of enterprise data assets in financial statements, as well as subsequent pilot actions taken by various regions. For example, the Henan Provincial Department of Industry and Information Technology organized 16 enterprises across the province and provided data asset assessment services in collaboration with legal and asset evaluation institutions. I believe this year is just the beginning, and with the official implementation of dataasset inclusion next year, the work on data asset assessment will become more scientific and precise. The third is the Operation Service Center, which focuses on data services and products to gather, govern, and manage data assets. Currently, the core issue is security, which is also a critical concern for the subsequent open   sharing of government data. Efforts in federated computing, privacy computing, multi-party trusted computing, and data security laboratories are underway to address this. Finally, there is the Circulation and Trading Center, where data exchanges are being established nationwide to facilitate the integration of   data, scenarios, and algorithms, empowering industries and scenes with data. The five platforms that run   through these centers are the Data Registration and Certification Platform, Data Asset Assessment   Platform, Data Security Mining Laboratory, Data Monetization Trading Platform, and Data Security   Supervision Platform.

It is important to emphasize that policy construction and theoretical research carried out at these levels are   extremely important, so the ability to innovate theoretical models is crucial. Through theoretical research, we   can explore more scientifically in terms of policy guidance, transaction paradigms, and ecosystem construction, innovating the systems, standards, and models of the data element market to support its normal, orderly   circulation and application.

What are the final results of data elements to help digital city construction?

Dr.Liu Ligong：

The fruitful results of data elements in the construction of digital cities are manifested in various aspects of   daily life, and I will not elaborate on each one by one. Instead, I will introduce several typical scenarios.

First is the government side in terms of industry. The construction of a data element system allows for the   continuous accumulation of industrial data assets, empowering industries with data, and conducting asset   evaluations on industrial data. Eventually, this leads to the inclusion of assets in financial statements, utilizing   the outcomes of data asset inclusion for financing and investment. This is an aspect that requires significant   effort in the development of digital cities in terms of industrial growth. I believe the most crucial economic   aspect of digital city construction is promoting more efficient industrial development through digitalization.   Another area is state-owned assets, where effective regulation is achieved through the supervision of state-owned enterprises to reduce their operational risks and enhance efficiency. How is this effective regulation   mainly achieved? Through data. In line with policies issued by the Ministry of Finance, I think the next step   could combine state-owned asset supervision with the preservation and appreciation of state-owned data assets. Since we have real-time and comprehensive data on enterprises during the data supervision process, we can effectively regulate them and achieve the inclusion of data assets in financial statements, ensuring the preservation and appreciation of state-owned enterprise data assets. In terms of the market, we can create an integrated and intensive intelligent regulatory platform for provincial, municipal, and district levels. For   example, we can upgrade the "two random, one open" regulatory model to "targeted regulation" using   various video algorithm models. We can also establish more data models, such as combining regulatory data of enterprises with data from platforms like Dianping, to provide consumers with safer and higher quality   services. Then there's the digital transformation of enterprises, which involves providing operational services   for enterprise data assets through digital transformation in management, business, and architecture,   supporting the digital transformation process of groups and their subsidiaries over the long term. Additionally,there's the cultivation of big data talent. If a place wants to develop the digital economy and build a digital   city, the most important support is the backing of digital talent. Only with a complete digital talent cultivation system can we better support the digitalization of local enterprises and the development of digital industries. Currently, we can explore an "industry-academia-research integration" model, combining research institutions, universities, and industry laboratories to cultivate digital talents at all levels locally, thereby promoting both   local industrial development and high-quality employment environments. Finally, there is the aspect of benefiting the people. When it comes to digitalization, we often think of the digitalization of urban   management and industrial development, but it is also very important that ordinary people can enjoy the   fruits of digitalization. We can unify, store, manage, and authorize various data generated by citizens during   service transactions (including but not limited to education, employment, entertainment, etc.) through a   citizen data space and business linkage system. This establishes a connection between individuals and   government administrative services, enabling precise delivery of administrative services to effectively address personal needs and laying a solid foundation for the operational authorization of personal data. To give a   simple example, as I mentioned earlier, the city can provide a free integrated storage space based on a super   storage system, offering each person 5GB of free storage space to permanently, freely, and sustainably save   authorized data, creating a personal exclusive data asset space to ensure some solid protection for the   operational earnings of personal data. In this way, if the government purchases a storage space, it can explore a path that lets every resident feel the results of digitalization, thereby bridging the digital divide and making   the fruits of digitalization inclusively available to citizens. We can also return data contained in the above   storage space, including personal identity, education, social security, medical care, and living status, to let   citizens live a life aware of the attributes, content, and value of their data assets, promoting the willingness of   individuals and families to actively upload their personal and family data, aiding in family tradition   construction and inheritance. Of course, there is much more to digital benefits for the people, but I will not   elaborate further here.

Dr.Liu Ligong：

I believe the future trends and directions for data elements to empower the construction of digital cities   mainly include the following five aspects:

Firstly, in the process of digital city development, the foundation of data assets will be continuously reinforced.The volume of data aggregation and storage will increase, and data governance will be elevated to a more   important position, thus realizing the transition of data from a resource to an asset.

Secondly, the construction of the data element market will gradually become standardized and regulated. As   you know, the current construction of the data element market is fragmented, which can be described as being in a 'Warring States' period. However, I believe that soon we will standardize the construction according to   national unified requirements, just like the land market, breaking away from the current fragmented situation.

Thirdly, there will be an enhancement in the level of government management and service digitalization. For   example, we usually go to the municipal service center for centralized processing of "one-stop" services. Although this concentrates business processing within a geographic area, it often leads to overcrowding. However, taking the example of the 12306 online ticketing system, which significantly reduced the flow of people   in train ticket halls, if we can build more scientific online government service systems, we can achieve the goal of benefiting the public and facilitating precise and intelligent integrated management services. This will also   conveniently allow the government to grasp the situation in real time and make rapid decisions based on dataanalysis or the results of large model processing.

Fourthly, the people will more fully share the fruits of digitalization. Regardless of region, gender, age, and   other external factors, everyone will be able to enjoy the benefits of digitalization. In the future, I believe the   digital divide will gradually narrow.

Fifthly, industry development will reach a new level due to digitalization. Whether it is attracting investment orthe actual development of businesses after they have been attracted to the local area, they should be able to   enjoy the convenience brought by digitalization. Businesses will know where is the most suitable place for   them, where they can quickly establish themselves, and what digital means they can use to reduce costs,   increase efficiency, and control risks. These, I believe, are the future trends and directions of digital   development.

In the construction of smart cities, how do data elements help enhance the scientific and precise nature of government decision-making?

Dr.Liu Ligong：

Regarding this question, I can cite a few scenarios to illustrate.

First, take the construction of smart communities, for example. For a smart community, data such as water andelectricity usage, spending records, or other metrics of special population groups within the jurisdiction may   indicate changes in their current states and service needs. For instance, if there are anomalies in the water or

electricity usage data of an elderly person living alone, these anomalies might signal issues like health   problems. At such times, we can leverage data for efficient analysis to provide timely services to the elderly.   Additionally, for populations that require education in the community or those who need to be relieved from   other penalties through community services, we can use video and surveillance data to enable government   and community managers to monitor their behavior accurately. By replacing manpower with model analysis,   we can address potential safety hazards. In terms of transportation, we can install dense networks of cameras on roads to monitor traffic conditions in real-time, achieving intelligent control of traffic lights based on flow rather than fixed timings. Furthermore, in healthcare and public safety, as everyone knows, the three-year pandemic has also somewhat accelerated the application of digitalization in urban management.

In the construction process of smart cities, how can we ensure the quality and accuracy of data elements?

Dr.Liu Ligong：

First and foremost is the data governance I mentioned earlier, which is a core component to ensuring the   quality and accuracy of data. This can be achieved through a series of operations such as data fingerprinting,   lineage tracking, and architectural design for foundational safeguards. For example, to check if there are any   discrepancies in the data from different commissions and offices, we can perform various data comparisons to verify its accuracy. It's also important to focus on the construction of data standardization, using the same   indicator to ensure consistency in primary metadata. Moreover, regarding the even more critical aspect of dataelement quality verification, I believe it lies in the foundational knowledge. Currently, data governance is   mainly achieved through the construction of several major databases, such as the raw data repository, data   standards repository, data theme repository, data topic repository, and up to the data knowledge repository.   By establishing these repositories and setting corresponding verification rules within them, we can ensure thatwe conduct the necessary tests on the quality of data elements and improve their accuracy.

What are the differences and connections between data elements, data assets, and data resources?

Dr.Liu Ligong：

Firstly, data elements refer to data that participate in social production and business activities and can bring   economic benefits to the owner or user. Data assets are data resources with a specific theme that can be   legally owned or controlled, measured monetarily, and bring direct or indirect economic benefits. Data   resources, on the other hand, are processed data of certain value. The definitions of data elements, assets, andresources might still be somewhat vague to many, so let me explain further. For instance, if we possess certain data and know it has high value but have not yet processed it—it's still raw material—that would be considered a data resource. When we process these data resources, turning them into semi-finished or finished products that can be used in our production and business activities to generate economic benefits,   they become data assets. As for the distinction between data elements and data assets, I believe there isn't a   clear boundary between the two.

Dr.Liu Ligong：

At the technical level, various computing technologies that are currently being developed, such as privacy   computing, federated computing, anonymization computing, and related security lab technologies, can be   used to support the resolution of this issue. At the conceptual level, I believe that although there is a   contradictory process between the two, it is also a mutually reinforcing one. It is precisely because of the   emergence of security issues during data collection, collision, and computation that it has triggered reflection among practitioners and the public. This has led to the continuous development of information security   technologies, promoting the perfection and sustainable development of the data ecosystem.

Dr.Liu Ligong：

To address this issue, I believe that the first consideration should not be a technical problem but a reform of   the management mechanism. We need to establish a city operations management center or an integrated   urban management center to coordinate and handle cross-departmental, cross-regional, and cross-level business operations. The next step is the technical aspect, where we need to consider how to achieve   automatic and intelligent allocation processing and workflow monitoring in data flow, event flow, and businessflow. This means realizing the integration of networked data to break down information silos and overcome   the issues of fragmented information and discontinuity in event handling caused by the construction of   dispersed systems. At the same time, it's necessary to clarify interdepartmental ownership and transfer   processes during the requirements analysis and system architecture phases to avoid problems such as   business stagnation and redundant reporting due to unclear responsibilities between departments. For   example, in the case of a fire, data such as regional population data, hazardous materials data, and online   surveillance data, which belong to different departments, must have clear responsibilities. All these data   should converge at our command center and be managed by the city operations management center or the   integrated urban management center for human resource dispatching and activation of emergency response   plans to achieve precise and scientific business collaboration.