A leading domestic securities firm engaged in investment banking, wealth management and institutional trading. Headquartered in Shanghai, it has 5 professional subsidiaries, 37 branches and 342 securities business offices in China, with business networks across the country. It has maintained a leading position in the industry for many consecutive years, and is at the forefront of the industry in terms of innovation capability and risk management.
Project Background:
The client faced the following operations and maintenance data governance challenges:
- Serious data silo phenomenon: Securities trading, asset management, investment banking, and other business systems independently maintain operational and maintenance data without unified management, resulting in inconsistent data standards.
- Inadequate data quality control: The lack of effective data quality control mechanisms for the collection, storage, and processing of O&M data affects the accuracy of data analysis.
- Poor Data Correlation: The lack of effective correlation between operational and maintenance data, such as business systems, infrastructure, network devices, etc., affects the efficiency of problem localization and root cause analysis.
- Inadequate Data Value Mining: Massive O&M data lacks an effective analytical model, making it difficult to transform into business insights and decision support.
- High data security risk: The securities industry has strict data security requirements, and the existing O&M data management mechanism is difficult to meet regulatory compliance requirements.
- Low efficiency of data sharing: Poor flow of operations and maintenance data between business and technical departments affects the efficiency of cross-departmental collaboration.
- Inadequate data governance system: lack of unified O&M data governance framework, difficult to realize the full lifecycle management of O&M data.
Project program:
- Data governance platform functional framework construction:
- Build a complete operation and maintenance data governance platform, including core modules such as model management, interface service, quality policy, access control service, anomaly alert, quality analysis, and so on.
- Establish an operations and maintenance data lake to realize centralized storage and management of massive operations and maintenance data.
- Closed-loop data governance mechanism:
- Set standards: Formulate unified O&M data standards through the model management module.
- Set up access control: Use the Quality Policy module and the Access Control Service module to set up a data quality control mechanism.
- Control data: Perform comprehensive monitoring and analysis of operations and maintenance data with the Data Management module.
- Drive governance: Promote remediation and optimization of data issues with the exception management module.
- Inventory and incremental data management:
- Inventory Data Governance: Adopt quality policy + automatic access control mechanism to automatically identify data instance anomalies and promote source-side data transformation.
- Incremental Data Governance: Automatically identify processing model anomalies through processing model checking to ensure the accuracy and validity of the processing model.
- Data governance results:
- Automatically collects and manages 20,000+ lake storage index elements and 2,000,000+ lake storage index instances.
- Centralized management of baseline indicators, quality strategies, indicator mapping and other control information.
- Automatically identifies over 100,000 anomalous indicator instances and performs multi-dimensional statistical analysis.
- Provide multi-dimensional statistical analysis and ranking of anomaly types, indicators, applications, hosts, administrative groups, persons in charge, etc., providing a basis for data governance decisions.