The Path of Data Assets to Enhance the Efficiency of Internal

Management in Enterprises

Wang Han

The Department of Accounting, Tongling University, Tongling, 244061, China

Keywords: Data Assets, Enterprise Management, Management Efficiency, Decision Support, Process Optimization.

Abstract: With the arrival of the big data era, data assets have become an important strategic resource for enterprises.

This paper discusses the path of data assets to improve the internal management efficiency of enterprises.

Through literature review and case study analysis, the study finds that data assets enhance enterprise

management efficiency mainly through the following paths: first, optimizing the decision-making process,

providing data support for management and improving decision-making accuracy; second, improving

business processes to achieve precision marketing and personalized services; third, enhancing risk

management and control, and identifying and warning of potential risks in a timely manner; and fourth,

facilitating resource integration to achieve cross-departmental collaboration and information sharing. This

study also explores the challenges facing data asset management, such as data quality, security and privacy,

and talent shortage, and proposes corresponding countermeasures. The results of the study have important

theoretical and practical significance for how enterprises can give full play to the value of data assets and

improve internal management efficiency.

1 INTRODUCTION

With the deep development of the digital economy

era, data is gradually becoming a strategic resource

as important as traditional material and financial

assets. The large amount of data generated in the

daily operation of enterprises contains rich value,

and the good management and utilization of these

data assets plays a decisive role in the core

competitiveness of enterprises. Nowadays, data is no

longer a by-product of enterprise operations, but an

important asset that can bring practical benefits. The

rapid development of information technology has

greatly improved the ability of enterprises to acquire,

store and process data, which opens up new ways for

enterprises to improve their internal management

efficiency.

Domestic and foreign academics and industries

gradually pay attention to the significance of data

asset management for enterprise development, but

mainly focus on the value brought by data assets in

the table, while the research on how data assets can

systematically improve the efficiency of enterprise

management is still relatively small (Tang and Tang,

2025). This paper collates the application of data

assets in enterprise management, analyses the role of

data asset management in improving the efficiency of

internal operation and management of enterprises,

and discusses the difficulties and solutions that may

be encountered, so as to provide theoretical basis and

practical help for enterprises to play the value of data

assets.

2 CURRENT STATUS OF DATA

ASSETS IN ENTERPRISE

MANAGEMENT

2.1 Definition and Characterization of

Data Assets

Data assets are data resources that are legally owned

or controlled by a specific subject, can be measured

in monetary terms, and can bring economic or social

benefits. Different from traditional assets data assets

have many unique features, data assets are non-

competitive, the same data can be used by multiple

individuals or departments at the same time, and will

not reduce its value, in addition, data assets are also

non-exclusionary, it is difficult to completely limit

the use of people, which brings difficulties in

264

Han, W.

The Path of Data Assets to Enhance the Efﬁciency of Internal Management in Enterprises.

DOI: 10.5220/0013842500004719

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on E-commerce and Modern Logistics (ICEML 2025), pages 264-269

ISBN: 978-989-758-775-7

defining the property rights of the data, and at the

same time, the return on the data assets tends to have

an incremental, with the growth of the volume of data

and the frequency of use of the enhancement, its

marginal value may gradually increase (Dai and

Zhao, 2025).

The value of data assets is influenced by usage

scenarios and analytical capabilities, with obvious

contextual dependencies. Accuracy, completeness,

consistency and timeliness are key characteristics that

high-quality data assets need to have, and are core

aspects of assessing their quality. In enterprise

management, understanding these characteristics can

help enterprises design a scientific data governance

framework to fully unlock the potential value of data.

2.2 The Evolution of Enterprise Data

Asset Management

From the 1960s to the 1980s, enterprise data asset

management was at an early stage, and the main task

was to collect and store basic data. At that time, data

was only an accessory to the daily business process

of the enterprise, and the management method relied

on file systems and early databases, and lacked the

idea of systematic management and utilization.

Entering the 1990s to the beginning of the 21st

century, enterprises entered the data warehouse

stage, a period in which dispersed business data were

integrated and utilized, a centralized data warehouse

system was gradually established, and data became

an important basis for enterprise decision-making.

The arrival of the big data era has led to an

important breakthrough in data asset management.

Enterprises are no longer focusing solely on

structured data, but are also beginning to tap the

potential value of semi-structured and unstructured

data, and the fields of data governance, metadata

management and data quality management are

gradually maturing, and the overall framework of

data asset management is becoming more complete.

The advancement of artificial intelligence and

machine learning technology has pushed data asset

management towards the intelligent stage, and data

is regarded as the core strategic resource of

enterprises, and its evaluation and value mining have

become the focus of management work, and data-

driven decision-making has been emphasized, and

has gradually evolved into the main mode of

enterprise management (Zhang and Li, 2025).

2.3 Application Areas of Data Assets in

Business Management

Data assets play a key role in many areas of

enterprise management. In strategic decision-

making, business leaders use market data, competitor

analysis and internal performance data to plan future

development paths. Through data mining, new

market opportunities can be found and potential risks

can be identified. In terms of marketing

management, companies use customer data to

conduct accurate analysis and build user profiles to

achieve personalized product recommendations and

marketing strategies. This not only improves the

marketing conversion rate and customer satisfaction,

but also optimizes the allocation of resources so that

the return on investment is improved (Liu et al.,

2022).

By analyzing production and supply chain data,

enterprises can optimize production planning and

refine inventory management in the area of

operations management, thereby reducing operating

costs and improving resource utilization efficiency.

In the area of financial management, data assets

drive automation of financial processes, provide

real-time financial analysis and forecasting, enable

more accurate budget management, and strengthen

cost control. Employee data is also used for talent

assessment and performance management to

optimize human resource allocation, thereby

improving employee satisfaction and productivity.

Data assets play an important role in areas such

as risk management, customer relationship

management, and product development. Data

applications in different areas drive each other to

improve the overall management efficiency of the

enterprise, and through cross-departmental data

integration, enterprise managers can obtain a more

comprehensive view of the business, eliminate

information silos, and promote collaborative

management within the enterprise.

3 THE MAIN PATH FOR DATA

ASSETS TO IMPROVE THE

EFFICIENCY OF INTERNAL

MANAGEMENT IN AN

ORGANIZATION

3.1 Optimizing the Decision-Making

Process

Data assets can improve the decision-making process

of enterprises in many ways, and enhance the

efficiency and quality of decision-making. Modern

management emphasizes judgment based on facts,

and data assets provide managers with a large

The Path of Data Assets to Enhance the Efﬁciency of Internal Management in Enterprises

265

number of reliable factual support, reducing the

subjectivity brought about by relying on intuition

and experience, and experience-oriented decision-

making has been transformed into data-oriented

decision-making. By building a data-centered

decision-making system, companies make the

decision-making process more transparent and easier

to track, while also reducing the impact of cognitive

bias on judgment (Xing and Zhang, 2024).

By applying data collection and analysis tools

and technologies, building a data ecosystem, helping

managers sort and filter the optimal information data

through data screening and adaptive information

processing, and integrating fragmented data, the

timeliness and accuracy of decision-making have

been significantly improved. Managers can quickly

respond to market changes and internal problems

with the help of real-time data analysis, establish

early warning and pre-planning mechanisms through

predictive analysis, propose scenario simulation and

hypothesis testing, and managers are able to assess

the possible outcomes of various decision-making

options without actual risk and select the best

solution (Dai and Zhao, 2025).

Complex data relationships become intuitive and

easy to understand through data visualization

techniques, and by building a shared data center,

managers with improved non-technical backgrounds

can also quickly grasp the core information and

trends in the data, which improves communication

efficiency and makes decision-making more

transparent, and after the enterprise establishes a data

asset sharing mechanism, barriers between

departments are broken down, and collaborative

cross-departmental decision-making is achieved,

which avoids waste of resources and decision-

making isolation, the overall decision-making

process is more efficient and unified (Yan et al.,

2025).

3.2 Automate Processes

Data-driven analysis allows companies to find

bottlenecks and redundancies in processes. With data

mining technology, managers can reveal potential

patterns and room for improvement in processes to

support process optimization, an approach that helps

companies clarify the direction in which they need to

adjust.

Process automation relies on data assets as its

foundation, which significantly improves the

operational efficiency of enterprises. Enterprises can

automate core business processes through the

rational application of data assets. For example, for a

company's financial business, budget management,

automated tax processing, filling in and generating

reports, and other financial tasks can be realized

through financial robots (especially those based on

RPA technology).In supply chain management, data

sharing and intelligent scheduling system (APS) can

realize automatic adjustment of production plan,

optimize inventory management, and realize

automated collaboration of the whole chain of

procurement, production and sales. These automated

means not only enhance processing speed, but also

reduce labor costs and the incidence of human error,

while also enhancing service consistency.

With the addition of machine learning and

artificial intelligence technologies, process

automation is entering a new phase of intelligence.

Adaptive processes can autonomously modify the

execution path based on real-time data and

environmental changes, thus realizing a more

flexible management approach. Enterprises build

end-to-end process monitoring systems, which are

used to track performance metrics in real time,

quickly identify and deal with anomalies, and ensure

processes are continuously optimized. At the same

time, process automation reduces employee time

spent on repetitive tasks, allowing them to devote

their energies to more creative and valuable work,

which, to a certain extent, promotes the

organization's ability to innovate.

3.3 Optimization of Resource

Allocation

Data assets play a key role in the optimal allocation

of enterprise resources and can help enterprises

maximize the use of resources. Enterprises can

manage inventory more accurately by analyzing

historical sales data, inventory turnover and market

demand forecasts, so as to not only prevent the waste

of funds and storage costs caused by overstocking,

but also reduce sales losses caused by understocking,

thus achieving the optimal allocation of inventory

resources.

In human resource management, the enterprise

based on business demand data changes, and

workload data analysis, the work ability of

employees, work habits and work efficiency data

records and analysis, flexible scheduling of staff

working hours, reasonable allocation of staff jobs,

improve the degree of job matching and employee

satisfaction, while automation enhancement also

replaces most of the repetitive manual operations, to

achieve rationalization of staff deployment and

maximize work efficiency. Maximize work

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efficiency. In addition, predictive manpower demand

analysis helps enterprises to formulate recruitment

and training plans in advance to prevent the problem

of insufficient or excess talent (Dai and Zhao, 2025).

Data asset management allows capital allocation

to be significantly improved. The increase in data

assets provides enterprises with intuitive and

effective investment information, realizes the

visualization of enterprise investment decisions, and

after analyzing the return on investment data of each

business unit and project, enterprises prioritize the

allocation of funds to areas with higher returns.

Enterprises can rely on data assets to realize the

dynamic control of the whole process, and accurately

monitor the whole cycle of capital allocation

(including the flow of funds, the efficiency of use

and the recovery status, etc.) through real-time

collection, intelligent analysis and closed-loop

feedback mechanism. Based on the real-time risk-

return assessment model, the enterprise can

dynamically optimize the asset allocation strategy

and intelligently adjust the investment direction of

funds, thus continuously improving the level of

investment returns. This data-driven capital

management model maximizes resource allocation

efficiency and investment returns. Finally,

enterprises can analyze the performance of asset

allocation through financial data analysis and

compare and analyze it with the original set of

expectations, so that managers can quickly identify

problems and wastefulness in the use of resources

and take measures to improve them, thus improving

the efficiency of capital utilization (Yuan et al.,

2024).

3.4 Enhancing Risk Management

Enterprise risk management has new methods and

tools thanks to data assets, and the ability to identify,

assess and respond to risks is significantly improved.

By analyzing historical data and external market

information, companies can build better risk

identification mechanisms and detect potential risks

in business, markets, credit and operations in

advance. By setting early warning indicators and

monitoring unusual behavior, companies can take

action to prevent risks before they become crises.

Risk assessment models are data-driven, making

it feasible to quantify risk, so that enterprises can

more accurately determine the likelihood of risk and

its potential impact, and allocate risk management

resources appropriately. For example, enterprises

can use credit scoring models to analyze the

repayment ability of customers, so that they can

formulate different credit policies, and analyze the

transaction patterns through fraud detection

algorithms, so that they can quickly detect suspicious

activities and reduce financial losses..

Enterprises are able to continuously monitor risks

through real-time data monitoring systems, achieve

predictive maintenance through real-time equipment

monitoring systems, and optimize the process flow

using production data collection and analysis

technology, thus effectively reducing the production

defect rate and continuously improving product

quality, and this ability allows risk management to

shift from passive response to active prevention (Qu

et al., 2025 ).In terms of cybersecurity, data analytics

technology can detect abnormal access behavior and

potential threats, and in supply chain management,

real-time monitoring of supplier performance and

market dynamics can reduce the possibility of supply

disruption.

Data assets can help enterprises build a more

comprehensive emergency response mechanism, and

enterprises can analyze the possible impact of

various types of risk events and design

corresponding solutions through scenario simulation

and stress testing. The establishment of a risk event

database can summarize experience and lessons

learned, further optimize the risk management

system, and enhance the enterprise's risk resistance

and recovery level (Liu et al., 2022).

4 CHALLENGES AND

COUNTERMEASURES IN

APPLYING DATA ASSETS TO

ENTERPRISE INTERNAL

MANAGEMENT

4.1 Data Quality and Consistency

Issues

In the internal management of enterprises, the

application of data assets encounters major

difficulties in data quality and consistency, and the

phenomenon of incomplete, inaccurate and untimely

data exists widely, and the causes of these problems

are complex: there are many sources of data in

enterprises, and the collection standards are not

uniform, and the phenomenon of silos between

information systems is obvious, which leads to the

obstruction of data circulation and integration; at the

same time, the lack of efficient data quality

management mechanism in enterprises, and the

The Path of Data Assets to Enhance the Efﬁciency of Internal Management in Enterprises

267

accumulation and proliferation of erroneous data in

the system. Data accumulates and spreads in the

system.、

Enterprises can take some measures to cope with

these challenges, the establishment of unified data

standards and specifications is the first step, clear

data collection, processing and storage of operational

processes, at the same time, the need to build a

comprehensive data quality management system,

covering data cleansing, validation, as well as

monitoring and so on, in addition, the use of

advanced data integration technology is also very

critical to eliminate the data barriers between the

systems, to achieve a smooth flow of data. Intelligent

data verification and correction can also be carried

out with the help of artificial intelligence and

machine learning technology, thus enhancing the

automation of data processing (Chen, et al., 2024).

4.2 Data Security and Privacy

Protection

Data security and privacy protection when

enterprises apply data assets has become an

important challenge, and IBM's Cost of a Data

Breach Report 2024 shows that the average cost of a

data breach for global enterprises is $4.88 million, an

increase of 10% compared to 2023. As the digital

transformation of enterprises advances, internal

management involves an increasing amount of

sensitive data, such as personal employee

information, customer data, trade secrets, and

financial information. The wider the scope of data

sharing, the greater the likelihood of risk exposure.

In addition, increasingly stringent data protection

regulations in various countries, such as the EU's

GDPR and China's Data Security Law, are placing

higher demands on compliance with enterprise

application data.

To meet these challenges, enterprises need to

establish a multi-level data security protection

system, implement a data classification and grading

system, and take differentiated protection measures

for data of different sensitivities; at the same time,

they need to strengthen data access control and rights

management to ensure that only authorized

personnel can use data within the prescribed scope,

and they need to apply encryption technology, data

desensitization and anonymization to reduce the

likelihood of data leakage (Hu, 2024).

4.3 Capacity-Building for Data

Governance and Management

The key bottleneck in the effective utilization of data

assets by enterprises lies in the lack of data

governance and management capabilities, with

widespread problems including unclear data

ownership, lack of data life cycle management

processes and gaps in data asset value assessment

systems. In addition, the shortage of data

management professionals has also become a real

problem for enterprises, and these problems make it

difficult for enterprises to fully explore the value of

data due to the ineffective application of data assets.

Enterprises must take the construction of data

governance and management capabilities as a

strategic task, to establish a complete data

governance organizational structure, to clarify the

responsibilities of the Chief Data Officer (CDO), and

to form a data governance department linking

various departments to enhance the enterprise's data

management work (He et al., 2024). At the same

time, it is necessary to develop a comprehensive data

policy and standard system, covering data

classification, metadata management, master data

management, and data quality management. In

addition, a data asset management platform should

be built to realize the visualization of data assets and

their full life cycle management, and attention

should also be paid to the cultivation of data talents

and the construction of data culture to enhance the

data literacy of all employees (Feng and Dong,

2024).

5 CONCLUSION

Enterprises enhance internal management efficiency

and cultivate data-based oriented decision-making,

data assets have become key strategic resources to

help enterprises make decision optimization, process

improvement, resource integration, and risk control.

As the era of digital intelligence continues to

develop, the role of data assets in enterprise

management will be more important, and also

provides a broader space for the release of the value

of enterprise data assets, at the same time, we also

need to see the dilemma of the further utilization of

data assets, not only lies in the issue of data

governance and security, but also lies in the further

management and development of how data assets.

Data assets not only as assets in the enterprise to

play value, artificial intelligence, RPA, cloud

computing, the application of these technologies will

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268

further explore the utilization of data assets to

enhance the efficiency of internal management of the

enterprise value, those who can effectively use the

data to meet the challenges, give full play to the

potential of the data assets of the enterprise will be in

the future to occupy an advantageous position.

REFERENCES

Tang, Z. D., Tang, Z., 2025. Do data assets create value

for your company?--The example of Company F.

Financial Newsletter, (04), 104-109+122.

Dai, F., Zhao, X., 2025. Mechanisms and Paths of Value

Creation of Enterprise Data Assets: Based on Decision

Making Perspective. Financial Newsletter, (08), 3-10.

Zhang, E. D., Li, Z., 2025. Paths and Mechanisms of

Artificial Intelligence Digital Technology Innovation

Driving Enterprise Data Asset Growth. China

Circulation Economy, 39(02), 100-114.

Liu, Q. L., Zhang, Y, Lei, Y. Y., Chen, G. J., 2022.

Research on the Process, Logic and Mechanism of

Digital Enabling Enterprise Innovation. Scientific

Research, 40(01), 150-159.

Xing, T. C., Zhang, Y., 2024. How Data Assets Enable

Manufacturing Business Financing Capabilities.

Journal of Shanxi University of Finance and

Economics, 46(08), 59-71.

Yang, J. F., Wang, C. X., Zhang, Y. C., 2025. Enterprise

data resources into the table: typical scenarios and

optimization solutions. Certified Public Accountant,

China, (02),110-116.

Yuan, Z. M., Yin, Q., YU, X., 2024. How Data Assets

Enable High-Quality Enterprise Development - An

Optimization Mechanism for Traditional Factors of

Production. Western Forum, 34(03), 54-73.

Qu, J. S., Liu, X.W., Li, Y. S., Meng, X. B., 2025.

Research on data asset accounting and management

issues under new quality productivity. Friends of

Accounting, (08), 150-155.

Chen, X. S., Li, H. F., Liu, H. D., 2024. Data Asset

Disclosure and Corporate Strategic Choices. Industrial

Economic Research, (06), 100-113.

Hu,W., 2024. Assetization of Enterprise Data Resources:

Theoretical Mechanisms, Practical Foundations and

Policy Options. Financial Newsletter, (03), 13-

19+157.

He,Y., Chen, L. L., Du,Y.G., 2024. Can Data Assetization

Ease Financing Constraints for "Specialized,

Specialized and New" SMEs?. China's Industrial

Economy, (08), 154-173.

Feng, Z. B., Dong, Z. J., 2024. Observations and

reflections on advancing data assets to the table. Price

Theory and Practice, (06), 16-21+224.

The Path of Data Assets to Enhance the Efﬁciency of Internal Management in Enterprises

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