In order to serve the real economy, promote the comprehensive deepening of the capital market reformation, realize high-quality development of the securities industry, condense the wisdom of industry research, and promote industry research exchanges, the Securities Association of China organized and carried out key research topics selection for 2022. A total of 476 project declarations were received, out of which, 183 were established key research projects.
During the selection, 59 outstanding project reports were finally selected by experts after an independent, objective and impartial review process by academic, regulatory and industry experts after 3 months of research. The "Research on Data Governance Construction Practice in the Intelligent Full Life Cycle of Securities Industry" jointly submitted by Sunline, Tianfeng Securities and Datablau was selected as one of the outstanding research topics, and it is also the only selected topic in the field of data governance.
Being able to stand out among nearly 500 submissions, the topic submitted by Sunline, Tianfeng Securities and Datablau undoubtedly highlights the ability and professional level of the research in the field of data governance construction.
1. Background of the subject
Compared to banking and digital industries, data foundation in the securities industry is relatively weak, and data governance still faces many challenges like difficulties to carry out data governance for a longer timeframe due to lacking system, limitations in governance implementation due to low number of combined theory and implementation cases, disconnection of data research and development due to post-event rectification and inefficient data governance, as well as problems related to upgrade due to large data governance workload. Although data governance is heavily invested in, but its business perception is not obvious and building a comprehensive and effective industry data governance system is full of challenges.
2. Abstract of the project
Based on the current situation of the data governance system construction in the securities industry, Sunline, Tianfeng Securities and Datablau have established a sound methodology for the full-life cycle intelligent data governance of the securities industry with the goal of realizing data assets value, taking the data governance methodology of DAMA and other domestic and foreign data governance organizations as theoretical guidance, adopting the excellent cases of banks and the Internet as reference, and based on the practical achievements of the securities industry, combined with the data governance and experience in data life cycle management of Tianfeng Securities, the research have developed a data asset management platform for the securities industry that can realize full life cycle data governance.
3. Topic ideas
The issues revolve around data processing and precision control, data governance R&D integration, data asset management empowerment, and data asset value assessment. The four directions carried out in the key research aims to provide reference for interbank data governance and empower the high-quality development of the capital market and securities industry.
Data process and precision control: Covers the entire data production, collection, transmission, consumption and destruction processes, scientific data life cycle control that conforms to the characteristics of the securities industry is formulated and the control process to the platform is implemented to achieve accurate control.
Data governance R&D integration: Embed data asset management platform and data modeling platform into (or apply to) the application and data R&D processes, transforming cumbersome and manual to intelligent and automated processing, improving post-event rectification to pre-event standardization and in-event control, ultimately improving R&D quality and achieving governance goals.
Data asset management and empowerment: Integrate data standards, data quality, metadata management and other functions to improve the efficiency of stock data management and control, improving data governance and R&D efficiency with the help of machine learning algorithms while establishing a data asset catalog, supporting business personnel data asset query and search, and ultimately empowering business.
Establish a quantitative system for data asset value assessment: Data asset evaluation is carried out from the aspects of data value and data quality through quantitative methods, providing reference for data application and guidance for IT construction investment.
Digital transformation has become the strategic consensus of many securities companies, and data governance is the only way forward to digital transformation. However, data governance is not a project that can be achieved overnight. A team of data governance talents with business knowledge, data thinking, and professional skills is the strong foundation and the intelligent data governance platform is an indispensable tool. In the future, Sunline will continue to explore artificial intelligence technology, improve the intelligence level of the data governance platform, and empower the securities industry to build a comprehensive and effective industry data governance system.