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Data Architecture Consulting

We plan from all aspects of data flow and continuously enhance and empower banks' data capacity building, including the ability of data collection, data integration and storage, data asset management, data development, and data service sharing.

Industry Background
In 2021, the People's Bank of China issued the Financial Industry Data Capability Building Guidelines, which divided the capability domains from data strategy, data governance, data architecture, data specification, data protection, data quality, data application, and data life cycle management, clarified the relevant capability items, and put forward the construction objectives and ideas for each capability item. Data architecture capability is one of the more important aspects, and it is also a more important task in the digital transformation of the financial industry. At the same time, with the development of the global economy and technology, the importance of data as the basis of the system and business can be imagined, so data capability is a global demand.
Consulting Introduction
GienTech data architecture consulting is a set of end-to-end solutions accumulated from years of consulting and implementation of financial and banking data platform, data asset management and data services. We plan from all aspects of data flow and continuously enhance and empower banks' data capacity building, including the ability of data collection, data integration and storage, data asset management, data development, and data service sharing.
  • Data acquisition and exchange capability | 1.Fully consider the growth of user scale and data scale, and prepare for the accumulation of data assets. 2.Can be adapted to a variety of data sources, a variety of methods to collect the full amount, throughout the user's product life cycle. 3.Collect enough comprehensive attributes, dimensions, and metrics to make the accumulated data assets more high-quality. 4.Improve the timeliness of data collection, thus providing timeliness of subsequent data applications.
    Data acquisition and exchange capability | 1.Fully consider the growth of user scale and data scale, and prepare for the accumulation of data assets. 2.Can be adapted to a variety of data sources, a variety of methods to collect the full amount, throughout the user's product life cycle. 3.Collect enough comprehensive attributes, dimensions, and metrics to make the accumulated data assets more high-quality. 4.Improve the timeliness of data collection, thus providing timeliness of subsequent data applications.
  • Data integration storage capacity | 1. It can access, convert, write or cache multiple sources of data inside and outside the enterprise, breaking the island and realizing the physical convergence of data. 2. Ability to combine subject domains/business functions to enable cross-functional insights from big data, including integrated decision-making capabilities across multiple subject domains. 3. Realize unified storage after internal and external data cleaning and integration.
    Data integration storage capacity | 1. It can access, convert, write or cache multiple sources of data inside and outside the enterprise, breaking the island and realizing the physical convergence of data. 2. Ability to combine subject domains/business functions to enable cross-functional insights from big data, including integrated decision-making capabilities across multiple subject domains. 3. Realize unified storage after internal and external data cleaning and integration.
  • Data asset management capabilities | 1. The data asset catalog forms a complete map of enterprise assets, and based on the data asset catalog you can identify data management responsibilities and resolve data issues disputes. 2. Clarifies data ownership attribution to data subjects. 3. Realize the security and sharing of data assets through comprehensive governance of multiple areas such as metadata management, data standards management, data quality management, and data security management.
    Data asset management capabilities | 1. The data asset catalog forms a complete map of enterprise assets, and based on the data asset catalog you can identify data management responsibilities and resolve data issues disputes. 2. Clarifies data ownership attribution to data subjects. 3. Realize the security and sharing of data assets through comprehensive governance of multiple areas such as metadata management, data standards management, data quality management, and data security management.
  • Data R&D management capabilities | 1. Provide project management/requirement management capabilities. 2. Provide offline data development, real-time data development and ETL development capabilities. 3. Provide unified management capabilities for data models.
    Data R&D management capabilities | 1. Provide project management/requirement management capabilities. 2. Provide offline data development, real-time data development and ETL development capabilities. 3. Provide unified management capabilities for data models.
  • Scheduling O&M capabilities | 1. Provide task scheduling function during data development. 2. Provide data monitoring and resource monitoring functions in the data operation and maintenance phase.
    Scheduling O&M capabilities | 1. Provide task scheduling function during data development. 2. Provide data monitoring and resource monitoring functions in the data operation and maintenance phase.
  • Data service sharing capabilities | 1. Provide diverse service approaches for data users, and develop different data service strategies according to the roles and needs of users. 2. The main modes of service provided include: online data services, data service subscription, dataset and data API services, etc.
    Data service sharing capabilities | 1. Provide diverse service approaches for data users, and develop different data service strategies according to the roles and needs of users. 2. The main modes of service provided include: online data services, data service subscription, dataset and data API services, etc.
Our Advantages
The best team

GienTech is a member of the national team of financial credit creation. GienTech big data team is one of the largest and strongest consulting service teams in the field of data in China.

Provide integrated services

Based on more than 20 years of precipitation in the field of data in the financial industry, we provide integrated services of consulting, product and implementation.

Realistic solutions

Our solutions are based on the assessment of enterprise data architecture and data status, combined with industry benchmarking and DCMM assessment, to develop a data architecture consulting solution that meets the long-term data strategy of the enterprise.

Case Studies
A financial institution data architecture planning

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