In recent years, with the development of the digital economy, data has become one of the most important resources in the digital economy, and many products and services are provided in digital form. With the rapid development of the Internet, big data, cloud computing, artificial intelligence and other technologies, the digital economy has penetrated into all fields of production and life, and its development speed, radiation range and influence are unprecedented. Many big data applications are built on the secondary use or reuse of data, and this widespread sharing and reuse of data has a profound impact on the economy. Therefore, in business and economic activities where data is shared, exchanged, and reused, it is critical to properly measure the value of data. However, unlike traditional resources, the digital characteristics of data resources make it difficult to evaluate them accurately. The theory of data pricing is just to solve the above problems. Data pricing is a business and economic practice that includes the theory of determining the economic value of data and formulating corresponding pricing strategies. It involves assessing the use, scarcity, market demand, and quality of data. Data pricing theory is often used to provide theoretical support for transactions between data providers, data markets, and data consumers to ensure fair data exchange and economic sustainability of data. Data pricing methods vary by market, industry, and data type, including subscription models, pay-as-you-go, ad-supported, and licensing fees. The goal of data pricing is to meet the needs of data users while ensuring that the creators and owners of data can reasonably obtain economic returns. In order to delve into the different kinds and methods of data pricing, the Ming LI Data Pricing Research Interest Group welcomes students who are passionate about data pricing to participate, learn together and collaborate to explore key issues in the field.

Note: This interest group is supported by Associate Professor Wong Kaman of the School of Information Technology and Dean Du Xiaoyong of Ming Li College.

Part1 Research contents and objectives

The main objective of this Data Pricing Research Interest Group is to further explore relevant issues in the field of data pricing through literature review, case analysis, behavioral simulation and simulation methods, and to promote research and understanding in the field of data pricing. We will systematically review and analyze the relevant literature and existing theories in the field of data pricing in order to understand the latest progress and research direction in this field. In addition, we also plan to conduct empirical research and case analysis, and conduct in-depth research and analysis of data pricing cases in different industries and application scenarios by collecting pricing information related to data products in the real market.

We are committed to :

1. Review and sort out the existing research on data pricing methods and strategies, try to create an interface like Github to gather the literature, tools, algorithms and other related resources on data pricing that we have sorted out, and systematically study the types of different data pricing methods and strategies in order to better understand their operating principles and application scenarios.

2. Conducted research on the actual data trading market, interviewed and conducted field research on relevant enterprises, data exchanges and data trading platforms, gained an in-depth understanding of the relatively mature data product pricing strategies and methods in the current market, analyzed the current development status and application of data pricing in commercial fields, and gained an in-depth understanding of the data pricing needs and challenges of different industries and different types of enterprises. Explore the potential impact of data pricing on various relevant areas. In this regard, we can form corresponding reporting outputs.

3. Study and communicate with the school's experts and teachers engaged in the field of data pricing, so as to enhance members' understanding of data pricing, enhance relevant professional knowledge, and cultivate corresponding professional skills and literacy.

4. Compile and edit a compilation or book on data pricing based on research results.

Part2 Research plans and methods

In order to achieve our research objectives, we will adopt the following research plans and methods:

1. Literature review and theoretical analysis: We will systematically review and analyze relevant literature and existing theories in the field of data pricing to understand the latest progress and research direction in this field. Through the comparison and analysis of different data pricing theoretical models, we will reveal the essence and core factors of data pricing problems.

2. Behavioral experiment and simulation: Computer technology is used to simulate the process of data matching negotiation and pricing in the real data trading market, and corresponding behavioral experiments are carried out from the perspectives of data suppliers and demanders to explore different strategies and methods of data pricing.

3. Empirical Studies and case studies: We plan to conduct a series of empirical studies and case studies. Through in-depth research and analysis of data pricing cases and classifications in different industries and different application scenarios, we can deeply understand the practical application and effect of different data pricing methods. And if conditions permit, organize some field research and form corresponding reports

4. Regular seminars and discussion groups: We will hold regular seminars and panel discussions to promote interaction and knowledge exchange among members, and organize online or offline reports according to the actual situation, which will help to share research findings and inspire new research directions.

5. Field research and report preparation: Conduct field research on data exchanges, data trading platforms and relevant enterprises or governments, understand the actual situation related to data pricing, the needs of different subjects, and the main problems encountered in the process through interviews and questionnaires, and organize the preparation of corresponding research reports and cases.

Part3 Team member introduction

Chen Yucun

Graduate student of Applied Statistics, School of Statistics, class 2023. He studied at the School of Statistics, Renmin University of China. Proficient in Python, R and other languages, with a good discipline foundation and comprehensive ability, good at comprehensive application of knowledge to solve problems. I have participated in mathematical modeling and other related competitions for many times, and have experience in small projects. At the same time, I have good teamwork spirit, sense of responsibility and self-learning ability, and can quickly apply new knowledge.

Liu Bo

Graduate of the School of Information Science, Management Science and Engineering, Class of 2022. He graduated from School of Economics and Management, Harbin Institute of Technology. I am proficient in SQL and Python programming languages, and have certain professional knowledge and accomplishment of data science. With certain project experience, I have participated in projects related to smart city governance and public data authorization operation, and have a strong team spirit and social responsibility. At present, I mainly focus on data governance and data value, and have some understanding of large models, deep learning, statistics, etc.

Part4 Registration method

Please scan the QR code below to fill in the relevant information to participate in the registration.