Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 16, No. 3, 2024 

 

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Research on Bank Data Governance Organization 
Architecture Planning 

Jian Huang * 

Ph. D candidate, Claro M. Recto Academy of Advanced Studies, Lyceum of the Philippines University, Manila, Philippines 

* Corresponding author: Jian Huang (Email: yotta@163.com) 

 

Abstract: Banking service capacity building is a critically essential area of study and research because banks, as vital 
components of the financial system, are key drivers of economic development and industrial progress. In light of the continuous 

improvement of financial innovation and scientific and technological knowledge, bank data information regarding the current 

state of the commercial banking industry displays a geometric multiple growth pattern. The complex state of bank data is also  

brought about by these data, which serve as a reference for bank architecture and planning. Because of this, efficient data 

management is required to fully reap the benefits of data. In short, the bank's data governance work will further enhance the 

bank's service capabilities and achieve its comprehensive value improvement. 

Keywords: Big Data Era, Commercial Bank, Data Governance. 

 

1. Introduction 

A significant amount of data has been gathered by banking 

and financial organizations in recent years due to the quick 

growth of the industry. Banks are starting to recognize the 

importance of data for business development with the advent 

of big data, cloud computing, and artificial intelligence. As a 

result, one of their key responsibilities is to fully utilize the 

value of data and to implement good data governance. The 

Guidelines for Data Governance of Banking Financial 

Institutions (henceforth referred to as the Guidelines) were 

released by the CIRC on May 21, 2018. Their objectives are 

to provide guidance to banking financial institutions in 

strengthening data governance, enhancing data quality, 

maximizing the value of data, raising the bar for operation and 

management, and encouraging the shift of banking financial 

institutions from rapid development to high-quality 

development [2]. In accordance with the Guidelines, banking 

and financial institutions must: establish a multi-level, 

interconnected operation mechanism; define the roles and 

responsibilities of the board of directors, the board of 

supervisors, senior management, and pertinent departments; 

and create a data governance structure with a sound 

organizational structure and boundaries among 

responsibilities [3]. In addition to the data governance 

centralized management department, this "guidance" 

specifically stipulates the responsibilities, job allocation and 

data accountability mechanism of the business 

department, and emphasizes the obligations of the business 

department as the data owner [4]. 

After the issuance of the Guidelines, banks began to start 

data governance projects one after another. Large banks paid 

more attention to driving business value and data value 

enhancement through data governance, while small and 

medium-sized banks began to construct and perfect data 

governance system, organizational structure and relevant 

systems. A reasonable and efficient data governance 

organization can not only eliminate the gap between business 

and IT, but also provide guarantee in promoting the 

implementation of data standards, improving data quality and 

realizing data value. 

2. Data Governance Organizational 
Design Principles 

"Centralized functions, intensive organization, team 

specialization, and target responsibility" are the guiding 

principles of the data governance organization design. It also 

makes sure that the overall design scheme has "design 

foresight" and "scheme feasibility". The connectivity and 

collaboration between head office departments and branches 

will be realized by constructing communication bridges 

between science and technology and business departments. 

The following design principles must be adhered to by the 

particular design principles. 

(1) Function centralization: The organizational structure 

design should concentrate and rationally use existing 

resources, give full play to the aggregation effect of human 

resources, and break the situation of each department doing 

its own thing. 

(2) Organization intensification: rationally organize and 

distribute the work content of various departments and offices, 

realize the intensification of organizational forms, further 

clarify the responsibilities of data governance, and strengthen 

the assessment system. 

(3) Team specialization: cultivate and establish a data 

governance team with professional capabilities, build a 

professional organizational structure system and 

responsibility allocation model, and assign posts and 

responsibilities. 

(4) Goal responsibility: The design of data governance 

organizational structure should be operable, clarify the 

responsible party for relevant work management, and 

implement the relevant processes and operation links of data 

governance. 

(5) Design foresight: based on the advanced experience of 

data governance at home and abroad and the development 

trend of the big data era, the data governance organization 

design fully considers the requirements of the bank's future 

business development and technological change on the data 

governance organization. 

(6) Plan viability: Define the positioning and evolution 



 

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strategy of the data governance organization throughout the 

entire bank, taking into account the current state of the bank's 

data governance., divide the responsibility boundary with 

other departments, formulate clear post setting, responsibility 

scope and personnel allocation of data governance 

organization, and ensure the feasibility of scheme. 

3. Business Support Status of Bank's 
Current Data Governance 
Organizational Structure 

The problems faced by bank data governance are mainly 

data standards and data quality, which are mainly reflected in 

the inconsistency of data standards, data missing, heavy data 

cleaning workload in the process of statistics and analysis, 

inconsistent data statistical caliber, and difficult sharing of 

data across departments and business lines. 

In order to address the data governance issue supporting the 

current business, it is necessary to take into consideration the 

current business support problem., we believe that banks need 

to establish and implement a complete data governance 

system, including: 

(1) A special data governance organization needs to be 

established to clarify the organizational structure level and 

related responsibilities. The lack of professional and 

independent data management and control organization 

makes the motive force of data governance insufficient, and 

it is difficult to carry out data governance effectively. 

(2) Standardized data management processes need to be 

established. Create a set of procedures for data management 

and control. The centralized management department of data 

governance will be in charge of managing pertinent process 

specifications overall, encouraging bank-level process 

implementation, and achieving standardized data 

management for the entire bank. 

(3) It is necessary to strengthen the management and 

implementation of core areas of data governance. Establish 

management mechanisms in data governance related fields 

such as data standards, data accountability, data quality, 

metadata, data life cycle, data architecture and models, and 

data security, and designate relevant competent departments 

to be responsible for overall management. Technical 

assistance is handled by the technology department, and data 

management systems and procedures are implemented by the 

relevant business divisions as part of business development. 

Finally, Gather the strength of the whole bank to jointly 

complete the implementation of data governance. 

4. Gap Analysis of Data Governance 
Organizational Structure and 
Regulatory Requirements 

There is a gap between the existing state of bank data 

governance and the needs of the "Guidelines". The 

organizational framework of data governance must be 

improved as soon as practicable, including: 

4.1. In terms of organizational structure: 

(1) Establish a top-down, transparent organizational 

structure and role-sharing arrangement. Clearly define the 

roles and responsibilities of the senior management, board of 

directors, board of supervisors, and pertinent departments [5];  

(2) Research and set up the data governance committee as 

the highest decision-making body of the bank wide data 

governance, set up an independent data governance functional 

department for centralized management, take the lead in 

implementing the construction of the data governance system 

[6], coordinate the implementation of the data management 

operation mechanism, organize and promote the data to play 

a full role in the operation and management process, and set 

up full-time posts to meet the work needs in the data 

governance centralized management department, Set up full-

time or part-time positions in other relevant business 

departments. 

4.2. In terms of Data Governance 
Responsibilities 

(1) Business departments shall be responsible for data 

governance in their own business areas, manage data sources 

of business lines, ensure accurate recording and timely 

maintenance, implement data quality control mechanisms, 

and implement work requirements related to regulatory data 

[7]; 

(2) The Data Governance Committee is in charge of 

developing the data strategy, making sure it is implemented 

and revised successfully, and creating a thorough, efficient, 

and scientific data management system; the data governance 

centralized management department establishes a 

standardized plan covering all data and follows unified 

business norms and technical standards; 

(3) Departments of science and technology will constantly 

work to enhance the information system to support a variety 

of enterprises and management data; improve the supervision 

statistical system, continuously improve the automation 

degree of supervision data processing; strengthen data sharing 

to ensure data security; establish comprehensive and strict 

management process and filing system, clarify requirements 

such as filing handover and caliber sorting, strengthen data 

impact analysis, and formulate emergency plans; 

(4) Internal audit departments should bring bank data 

governance into the scope of supervision; 

(5) In terms of data quality control, the centralized 

management department of data governance shall establish 

data quality management system and rules, improve data 

quality control by introducing technical means; strengthen 

daily supervision and inspection, establish data quality 

assessment and evaluation system, and incorporate the 

assessment results into the performance assessment system of 

the institution to realize continuous improvement of data 

quality [8]; 

(6) Each business department shall strengthen data 

application in risk management, business operation and 

internal control, realize data-driven, improve management 

refinement, give full play to data value, and establish regular 

self-assessment and inspection mechanism [9]. 

4.3. In terms of Building Data Governance 
Talent Echelon 

(1) Fill in the missing basic data governance posts as soon 

as possible, including full-time personnel of data standard 

management post, data quality management post, data 

demand management post, metadata management post and 

data life cycle management post; 

(2) Transform part-time posts into full-time posts, 

including data architecture, data application and mining posts, 

to form full-time posts; 

(3) Strengthen the training and recruitment of business and 

science and technology compound data governance talents, 



 

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and enhance professional collaboration capabilities. 

5. Bank Data Organization Structure 
Specific Implementation Proposal 

5.1. Overall Architecture Construction Scheme 

As mentioned above, banks should follow the overall 

organizational structure building strategy of evolving from a 

composite data governance organizational model (that is, 

"data" centered with a hybrid cross departmental data 

governance organizational model) to an independent data 

governance organizational model. Data governance is 

essentially bank wide. In order to break the inherent barriers 

in cross departmental collaboration, better coordinate science 

and technology and business departments, and reduce 

implementation resistance, banks need to establish a stable 

decision-making body, a strong overall management 

department, and ensure that all relevant departments have 

clear division of labor and efficient collaboration to ensure the 

effective implementation of data governance. According to 

the requirements of the Guidelines, banking finaa permanent 

and continuous leadership organizationncial institutions 

should establish a professional team to meet the needs of data 

governance work, and set up full-time posts to meet the needs 

of work in the centralized management department of data 

governance, and set up full-time or part-time posts in other 

relevant business departments. 

The bank data governance work is still in its early stages of 

decision-making, so a permanent and ongoing leading 

organization is required to continuously participate in the data 

governance work's decision-making. Additionally, the 

leading data governance decision-making organization 

should be around for a long time in the long-term planning of 

the future. Data governance is a very collaborative effort of 

the Bank, including all lines and divisions, in terms of the 

management coordination layer. To guarantee cohesive 

administration and efficient data standardization across the 

Bank, a robust management department must assume the 

responsibility of organizing and resolving cross-departmental 

disputes. Data governance at the executive level encompasses 

both business and technological aspects, necessitating 

technical assistance from science and technology departments 

in addition to direction from business departments. As a result, 

it's critical to establish precise guidelines for the 

responsibilities and links between the business and science 

and technology departments, as well as the thorough 

involvement of different departments and the fair distribution 

of job duties. Each level of the organization management 

mode shall perform corresponding responsibilities, and the 

performance of responsibilities shall be based on 

corresponding authority, resources and personnel. Only by 

having consistent responsibilities and rights can the stability 

and feasibility of the data governance organizational structure 

be guaranteed and the driving force and execution force of the 

bank data governance work be increased [10].  

5.1.1. Decision-making Level Organization Construction 
Strategy 

In terms of setting up the data governance decision-making 

layer organization, two models can be considered, one is 

composed of the Information Technology Committee and the 

Data Governance Committee, and the other is that the Data 

Governance Committee independently assumes the decision-

making layer function. 

Given that the bank's data governance activity is in its early 

stages, there are numerous issues that require decision-

making in the implementation process of the future data 

governance system, and the requirements for decision-

making and approval efficiency are rather high; The bank has 

a foundation in the decision-making layer setting, and has 

established a data governance leading group to be responsible 

for decision-making work on data governance-related matters, 

so an independent data governance committee is set up in the 

executive management office as the data governance 

decision-making layer. 

5.1.2. Management Coordination Layer Organization 
Construction Strategy 

In terms of setting up the organization of data governance 

management coordination layer, one is to set up a data 

management center under the data governance working group, 

which is suitable for organizations with high participation and 

coordination ability of various departments; the other is to 

establish an independent data management center to assume 

the functions of working group and data governance 

committee office, which is suitable for organizations with low 

management maturity and uses the data governance 

department to carry out work. 

In view of the fact that the Bank has established a data 

governance working group composed of business backbones 

of relevant departments, with extensive resolutions and 

certain effectiveness in promoting and implementing data 

governance work, It is recommended to keep the data 

governance working group in place and create a data 

governance management coordination layer made up of the 

data management center and the working group. The entire 

administration of data standards, quality, requirements, 

accountability, etc. is the responsibility of the Data 

administration Center, which is the department in charge of 

data governance general management. as well as the 

preparation of relevant systems and management processes; 

the Data Governance Working Group is the deliberation and 

coordination organization of data governance, accountable 

for organizing and settling disagreements brought forth by the 

Data Management Center, as well as for considering issues 

filed by the Data Management Center. 

5.1.3. Executive Level Organization Construction 
Strategy 

Data governance covers a wide range and has a large 

impact, requiring the deep participation of science and 

technology departments and business departments. Therefore, 

the executive layer needsto establish a federal-style working 

mode to jointly carry out data governance implementation 

under the overall management of the data management center. 

The science and technology department undertakes the 

technical support and system construction of data governance, 

introduces advanced technical means, meets business needs, 

and provides technical support for the realization of data 

value. The business line department is responsible for data 

governance in the field under its jurisdiction, implementing 

the data quality control mechanism, and implementing work 

requirements related to regulatory data and internal business 

management data; In risk management, business operations, 

and internal control activities, data application scenarios 

should be actively explored to drive the continuous 

improvement of internal management and business 

development through data. 



 

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6. Summary and Outlook 

As mentioned in this report, the construction of bank data 

governance organization is divided into two stages, namely, 

short-term and long-term. In the near future, as an 

independent organization-data management center, it is set 

under the planning and finance department, focusing on the 

construction of data governance system, organization 

construction, talent cultivation, implementation of system 

process, etc., supplemented by data governance working 

group, jointly with science and technology departments and 

business departments to carry out data governance work in a 

planned way, so as to realize standardized management of all 

kinds of data throughout the Bank. In terms of department 

assessment, the head of the Planning and Finance Department 

needs to have assessment authority over the data management 

center. In the future, the data management center will evolve 

into a data management department, becoming an 

independent first level department that will coordinate the 

entire bank's data management and data value realization 

work. The department's positioning will gradually shift from 

focusing on "standardization" in the early stage to focusing on 

"value and application" in the later stage. The Future Data 

Management Department is a new driving force for "Business 

IT" to support decision-making across all business 

departments and senior management throughout the bank. 

6.1. The Difficulties and Challenges Faced 

In the process of organizational structure construction, we 

may face a series of difficulties and challenges. On the whole, 

the bank data governance work is in the initial stage, and there 

is little experience to learn from in the bank. The personnel of 

the data management center will work in a highly complex 

environment following the early stages of the center's 

construction and will go through an adjustment period. 

First of all, in terms of business operation process (such as 

data standard formulation process and data requirement 

management process), more trial work needs to be carried out, 

and it may occur that all departments submit all data problems 

to the data management center for solution, resulting in 

deviation of work focus. To prevent such circumstances, each 

job must have a clear understanding of the boundaries of its 

responsibilities. 

Secondly, with the continuous increase of data fields and 

data items involved in the data management center, the 

requirements for the quality and staffing of data management 

center personnel will continue to improve. If relevant 

departments (such as human resources department and data 

management center supervisor) fail to find problems in time, 

problems such as brain drain and poor work results may occur 

due to heavy work pressure of departments and insufficient 

personnel matching. 

Thirdly, in the construction of talent team, data 

management organization needs to build talent team with 

financial+IT compound ability. At present, the accumulation 

of compound talents in banks is weak, and large investment 

is needed in talent team construction, otherwise it will face 

the problem of recruitment difficulty. 

Finally, the cross-department collaboration problem. As the 

overall management department of data governance in the 

future, the data management organization needs to carry out 

cross-department collaboration with the executive layer of 

business department and the executive layer of science and 

technology line. The system process execution force and 

cross-department collaboration efficiency of each relevant 

organization bring certain risks to the development of data 

governance work. 

6.2. Suggestions for Dealing with Problems 

(1) Clear boundaries of departmental responsibilities and 

establishment of dispute resolution mechanisms 

Data management co-ordinates a department to undertake 

a lot of executive layer work is the problem that a lot of banks 

face commonly, also be the problem that should avoid 

emphatically. In order to avoid such phenomena, firstly, at the 

initial stage of department establishment, it is necessary to 

publicize and implement the department responsibilities, 

management processes and systems to the whole bank, 

convey the data governance responsibilities that each 

department should undertake, and timely follow up and adjust 

the boundary of department responsibilities according to the 

overall work needs of the bank. Secondly, induction training 

shall be provided to the employees of the data management 

center to ensure that they fully understand their post 

responsibilities. In the face of controversial matters, the data 

management center shall, on the one hand, strengthen the 

organizational ability of cross-department coordination, and 

on the other hand, from the perspective of decision-making 

efficiency, timely report the problems to the data governance 

working group and even the data governance committee for 

resolution. 

(2) Appointing people on merit, tilting salary system and 

differentiating career planning 

In terms of talent training and team building, it is suggested 

to give priority to internal training and selection of excellent 

business backbones and technical experts, implement post 

competition mechanism at the bank level, and make certain 

preferential consideration in salary treatment to attract all 

kinds of talents in the industry. At the same time, external 

recruitment methods are adopted to attract compound talents 

with technical development + financial background from peer 

institutions to supplement the backbone of the bank data 

governance talent team. With the improvement of data 

application in the company's strategic position in the future, it 

is necessary to consider the differences in career planning 

between technology talents and business personnel. It is 

recommended to optimize the bank's performance evaluation 

mechanism and address talent selection and retention issues. 

(3) Actively investigate the completeness of the 

organization and improve the talent guarantee system for data 

governance 

The director of HR Department and Data Management 

Center shall communicate with the staff of Data Management 

Center regularly (suggested frequency: at least every year, 

and the frequency can be increased according to the internal 

evaluation requirements of the bank), fully understand their 

responsibilities, problems faced in their work, evaluate 

whether the staffing of existing organizations meets the work 

needs, timely arrange to increase relevant posts and staffing, 

and improve the talent guarantee system. 

(4) Create a cross-departmental training program for talent 

in data governance and work to consistently enhance their 

professional aptitude 

The employees of the data management center should 

maintain their continuous learning ability in the business and 

technology fields. In addition to strengthening the cross 

disciplinary internal training organized by the data 

management center itself, and taking part in various training 



 

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programs for business and technology lines run by banks and 

outside organizations, in an effort to fully satisfy the needs of 

business and technology lines, the data management center 

can dispatch a certain number of backbone employees to the 

corresponding business each year A positive cycle of talent 

and a route for knowledge updates is created by the rotation 

of technology departments and the concurrent hiring of staff 

members from the business and technology departments for 

the data management center. This enables the sharing of both 

new and old roles. 

(5) Bring data governance into the scope of assessment and 

promote cross-department collaboration 

As the overall data governance management department, 

the future data management center (long-term data 

management department) recommends that data governance 

work be included in the assessment system to ensure the 

effective implementation of cross-departmental organization 

and coordination mechanisms and improve the efficiency of 

cross-departmental collaboration. 

In addition, the data governance work is at the level of the 

whole bank, and all departments and branches of the Bank 

should actively participate in it. On the premise of ensuring 

the effective implementation of personnel allocation and 

relevant responsibilities, the Bank needs to popularize the 

corporate culture of data governance in the bank by means of 

internal propaganda, assessment and incentive, finally realize 

the value of data application in marketing management, risk 

management and internal control, and help build a leading 

bank in science and technology. 

6.3. Data Governance Organizations Support 
Business Development, Financial 

Technology, And Big Data Applications 

To meet the demands of the rapidly developing fields of 

artificial intelligence, big data, financial science, technology, 

and other fields, combined with the strategic deployment of 

banks in customer marketing, risk management, operation 

efficiency improvement, etc., the future data governance 

organization provides guarantee in improving bank data 

management level, big data mining capability, high-level data 

application, etc. Industry research shows that at present, the 

big data application of financial institutions is mainly 

concentrated at the front-end business level, and the 

development of artificial intelligence faces significant data 

infrastructure problems. Traditional financial institutions are 

mostly exploring how to use technology to successfully 

transform. 

In the future, data governance organizations will enable 

banks to better embrace financial technology on the premise 

of solving data foundation problems for banks. Within the 

context of the data foundation, the positions established by 

the data governance organization with regard to standards and 

quality data will be in charge of organizing the development 

of the Bank's standards and quality guidelines and 

encouraging their use, further encouraging the Bank's degree 

of data management to be improved continuously, 

continuously optimizing the quality of the data, encouraging 

data integration and sharing, and safeguarding the 

advancement of big data and artificial intelligence for banks; 

In terms of big data mining, a dedicated position for data 

mining has been established throughout time. This position 

will be in charge of investigating and studying different data 

mining tools and algorithms, as well as utilizing a variety of 

data mining tools to complete data mining work for particular 

jobs. [11], and improving the R&D and practice capabilities 

of new data mining technology modes, models and algorithms 

of banks; In terms of high-level data applications such as 

artificial intelligence, data application posts have been set up 

to be responsible for the development of various application 

scenarios and scheme design, which will continuously expand 

the business application scope of bank artificial intelligence 

and big data applications. 

In the future, banks need to cultivate financial technology 

and big data mining talents internally, while actively 

attracting external talents to enhance their practical abilities 

in machine learning, data mining, intelligent risk control, and 

refined marketing, providing guarantees for the realization of 

bank data value. In addition, it emphasizes the allocation of 

technology + business compound professionals in data 

governance organizations, which conforms to the trend of 

talent selection in the financial industry and will form 

important talent assets of banks in the future. 

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