The client is a comprehensive company approved by the China Securities Regulatory Commission (CSRC) to engage in futures business. It primarily engages in commodity futures brokerage, financial futures brokerage, futures investment consulting, asset management, and other businesses. It holds membership status at the Zhengzhou Commodity Exchange, Dalian Commodity Exchange, Shanghai Futures Exchange, China Financial Futures Exchange, Guangzhou Futures Exchange, and Shanghai International Energy Exchange Center. The company operates one risk management subsidiary and over twenty branches covering major cities and regions across China. As a vital component of the group's development strategy, the company is committed to promoting the development of the real economy and meeting national wealth management needs. Leveraging the strong resource advantages of its shareholders, it aims to build a derivatives investment bank and become a top-tier comprehensive derivatives service platform in China.
Project Background
With the rapid development of financial technology and the increasing complexity of the futures market, the securities and futures industry faces multiple challenges: operational efficiency urgently requires optimization, system stability needs stronger safeguards, operational costs must be further reduced, regulatory compliance requirements are becoming stricter, and there is a pressing need to enhance market competitiveness. Traditional IT operations models struggle to adapt to industry changes, manifested in: blurred team responsibility boundaries, reactive fault handling becoming the norm; inefficient cross-departmental collaboration with communication barriers; insufficient transparency in operations, leading to low satisfaction among business departments and end-users; low process standardization hindering continuous service quality improvement; and fragmented IT asset data lacking unified management. This results in inefficient resource allocation and fails to provide effective support for IT digital transformation planning decisions.
Project Solution
Adopting an implementation path of "Research – Design – Pilot – Promotion," combining industry best practices with technological innovation, we will advance the construction of the operations data governance system in phases:
1. Current State Research & Requirements Analysis:
- Conduct research within the existing IT operations data framework to map our department's data management status, pain points, and requirements.
- Analyze the sources, flow paths, and application scenarios of asset, alert, and metric data to identify root causes of data quality issues.
2. Data Model & Standard Construction:
- Reference securities and futures industry data governance frameworks (e.g., SDOM) and learn from fintech institutions' practices to ensure scientific rigor and implementability of the data model.
- Design a comprehensive data model covering assets, alerts, and metrics, defining field specifications, coding rules, and relationships.
- Establish standardized processes for data collection, storage, cleansing, and application, and build a data quality assessment index system.
3. Data Governance System Design:
- Propose a data governance organizational structure with defined responsibilities, clear process standards, and regular management mechanisms.
- Develop a data standard implementation plan, validate model feasibility through pilot applications, and iteratively optimize the standard system.
4. Data Governance Pilot Implementation:
- Establish an IT Asset Data Management System to enable data modeling, automated collection, data verification, and statistical analysis.
- Integrate data governance capabilities into the operations management system. Build consumption scenarios, develop use cases, and verify the effectiveness of the operations data governance framework and daily governance activities.