Inventory of the top 10 activities of data asset management!Optimize the management and application of enterprise data assets!

In today's digital age, data has become one of the most valuable assets of a business, and in the previous article, we gave a preliminary introduction to data asset management.

Previous articleWhat is data asset management?Discuss the difficulties and development status of data asset management in detail!

Effective data asset management can not only improve the quality of decision-making, operational efficiency, and innovation ability, but also bring far-reaching strategic value to enterprises. To fully exploit and utilize the potential of data assets, organizations need to systematically carry out a range of activity functions. The activity function is the basic management unit of data asset management. Data asset management includes 10 activity functions, such as data model management, data standard management, and data quality management, covering two stages: data resource management and data asset management. This article will provide a brief introduction to the top 10 data asset management functions, aiming to provide enterprises with a comprehensive framework to optimize the management and application of their data assets.

1. Data model management

A data model is a framework used in an information architecture to describe data elements and the relationships between them, providing a standardized blueprint for data storage, organization, and use. The data model is an integral part of database design, data processing, and data exchange by defining the type, format, attributes, and rules of interaction between data to help organizations ensure the consistency and accuracy of data, thereby supporting effective data management and analysis.

Data model management is a key function in data asset management, and it involves the process of creating, maintaining, and optimizing data models to ensure that a structured representation of data accurately reflects business concepts and logic. This process involves defining data elements, their attributes, and the relationships between data to support data consistency, integrity, and accessibility.

Key activities of data model management include:

• Data model plan: identify stakeholders in data model management, collect, define and analyze organizational data model requirements, determine and follow data model standards and requirements, and design enterprise-level data models (including subject domain data models, conceptual data models, and logical data models);
• Data model execution: Develop a physical data model with reference to the logical data model, keep the development process records, and review the model by the participants of the data model management according to the data model review criteria and test results, and release and launch the model after the review has no objection.
• Data model check: Determine the data model inspection standards and carry out data model checks regularly to ensure the consistency of the data model with the organization-level business architecture, data architecture and IT architecture;
• Data model improvement: According to the results of the data model inspection, convene the relevant stakeholders of data model management, clarify the data model optimization plan, and continuously improve the data model design method, model architecture, development technology, management process, maintenance mechanism, etc.

Together, these activities form the core of data model management, which helps organizations build and maintain an efficient, stable, and business-ready data model. Through effective data model management, organizations are able to improve data quality and facilitate data sharing and reuse, providing a solid foundation for data analysis, business decision-making, and system development.

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2. Data standard management

A data standard is a well-defined, widely accepted set of rules and conventions that ensure the consistency, accuracy, and comparability of data. They specify how data should be named, formatted, stored, and exchanged, including data types, formats, terminology, encoding, and classification. Data standards are designed to facilitate data interoperability between different systems and organizations, supporting the integration and sharing of data while reducing data redundancy and errors, and improving the quality and trustworthiness of data.

Data standards management is a systematic set of processes and practices designed to develop, implement, monitor, and maintain data standards to ensure the consistency, accuracy, and reliability of data within an organization. This process involves establishing policies and procedures for data standards, providing standardized control over data definitions, formats, terminology, and quality rules, and monitoring compliance with these standards through a data governance framework.

The core steps of data standards management can be summarized as:

• Plan data standard management: Identify the key personnel responsible for data standard management, collect requirements, investigate the current situation, and build an enterprise-level data standard classification system. At the same time, the plan and implementation blueprint for data standards management are formulated and published.
• Implement data standard management: Establish specific data standards by using the established data standard classification system. According to the responsibility distribution system of data asset management, a professional team is organized to review and formally promulgate data standards. At the same time, the application of data standards is implemented by using technology platforms and tools, which may include the design and development of data models and the verification of data quality.
• Supervise and inspect data standards management: Regularly review the applicability and coverage of data standards to ensure they meet current business needs. Use a technology platform to monitor the application of data standards and record the extent to which they are being implemented.
• Continuous improvement of data standards management: Develop a long-term plan for the maintenance and optimization of data standards, follow the established organizational structure and policy process, ensure that all parties involved work together, and continuously improve the efficiency and effectiveness of data standards management.

In the enterprise, following data standards is critical to achieving effective data governance, supporting compliance requirements, driving data-driven decision-making, and optimizing business processes. Data standards management provides a stable and trusted data environment that supports effective data analysis, reporting, decision-making, and facilitates data integration and sharing across departments and systems, while helping organizations meet compliance and regulatory requirements.

3. Data quality management

Data quality refers to the degree to which data meets specified requirements in a particular context, including accuracy, completeness, consistency, trustworthiness, timeliness, and accessibility of the data. High-quality data reliably supports decision-making processes, optimizes business operations, enhances the customer experience, and ensures compliance with regulatory requirements. The quality of data directly affects the reliability of data analysis and the value of enterprise data assets.

Data quality management is a set of strategies and practices designed to improve the accuracy, integrity, consistency, and trustworthiness of data. It includes defining data quality objectives, setting data quality standards, conducting data quality assessments, implementing data cleansing and monitoring, and continuously optimizing data management processes. With data quality management, organizations can ensure that data meets business needs and compliance requirements throughout its lifecycle, improving the reliability of data-driven decisions and supporting effective business operations and growth.

The core process of data quality management involves the following key activities:

• Plan data quality management: Assign a person responsible for data quality management and clarify internal and external requirements for data quality. Establish a data quality rule base and evaluation index system with reference to the data standard system, and formulate corresponding management strategies and plans.
• Implement data quality management: Use the technology platform to manage the internal and external requirements, rule bases, and evaluation systems of data quality. Define the scope of data quality management, including business, project, and data type, and implement data quality review and differentiation management.
• Monitor and analyze data quality: record the results of data quality audits, analyze the causes of problem data, and designate the person responsible for quality inspection. Compile data quality assessment reports and improvement recommendations, continuously monitor the operational processes and performance of data quality management, and evaluate data quality service levels.
• Continuous improvement of data quality management: Establish a knowledge base for data quality management, optimize management processes, and improve management efficiency. Set data quality service standards and continuously optimize data quality management strategies.

Data quality management is guided by the following principles:

• Governance at source: In the development stage of new business or IT systems, clarify data standards and quality rules to ensure a single source of data and confirm with data producers and users. This is usually applicable to incremental data scenarios that do not require high data timeliness or core business.
• Closed-loop management: Establish a positive cycle covering data quality requirements, problem identification, inspection, rectification and other links. Carry out quality control of the whole process of data collection, circulation, processing and use, continuously optimize the quality management plan and adjust the rule base according to business needs, and establish a measurement index system for data quality and management process to achieve continuous improvement of data quality management strategy.

Through these critical steps, organizations can establish a comprehensive, ongoing data quality management process that continuously improves the quality and value of their data.

4. Master data management

Master data refers to the data of key business entities that are frequently referenced, shared and have high value in the process of enterprise operations and decision-making, and these data items are usually used as core information and are relied on by multiple business systems and transaction processes, and they have high stability and low frequency of change. The accuracy and consistency of master data is critical to ensure smooth business processes, reliable data analysis, and effective decision-making.

Master Data Management (MDM) is a policy and process for creating and managing consistent, accurate, and trusted master data across an enterprise. Master data typically refers to critical data that is widely referenced throughout the organization, such as customer, product, supplier information, etc., and is the basis for business transactions and decisions.

The core process of Master Data Management (MDM) consists of the following key steps:

• Develop a master data management plan: Define the scope of master data management based on the enterprise-level data model, and establish a single source system and identification standard for master data. Create a data model for master data, including the distribution of primary and secondary data sources, and set norms or rules for data standards, data quality, data security, etc., and clarify the connection between these specifications and the overall data asset management of the organization.
• Perform master data management: Leverage platform tools to synchronize and share data between core business systems and master data repositories to ensure data consistency.
• Oversee master data management: Conduct regular quality audits of master data to ensure data consistency and uniqueness across the organization, and document issues found.
• Continuously optimize master data management: According to the problems found in the supervision process, summarize experience, formulate improvement measures, and continuously improve the quality of master data and the efficiency of master data management.

Through this series of steps, master data management ensures that organizations can maintain a unified, accurate, and trusted master data environment that supports their operational efficiency and decision-making quality.

5. Data security management

Data security involves taking appropriate measures to protect data from unauthorized access and lawful use, while ensuring that the data remains secure on an ongoing basis. Data security management is a series of activities carried out through cross-departmental cooperation within the framework of an organization's data security strategy to ensure the safe and lawful use of data. This includes establishing a data security governance team, formulating security policies, building a technical protection system, and cultivating data security talents.

Key steps in data security management include:

• Develop a data security management plan: Understand the internal and external data security needs and regulatory requirements faced by the organization, and establish a data security management system, including basic principles, management rules, procedures, and coordination mechanisms. It is also necessary to clarify the measures for the protection of personal information and publish standards for data classification and grading.
• Perform data security management: Use technology platform tools to identify sensitive data, and deploy corresponding security measures according to the data security level, such as access control, data masking, leakage prevention, and security audit systems.
• Supervise and inspect data security management: monitor the security status of data in all links (collection, storage, transmission, processing, and use), and conduct internal and external security audits.
• Continuous improvement of data security management: Analyze data security issues and risks, evaluate the effectiveness of existing security standards and norms, and continuously optimize data security management measures.

Data classification and grading is the foundation and key to data security management. The promulgation of the Data Security Law and the Personal Information Protection Law, as well as the solicitation of opinions on the Regulations on the Management of Online Data Security, have emphasized the importance of data classification and grading. The state implements different degrees of protection measures for different levels of data, especially focusing on the protection of personal information and important data, and implementing strict protection of core data. All regions and departments need to classify and manage data in their respective fields in accordance with national guidelines.

6. Metadata management

Metadata is information that describes the attributes of other data, providing context and meaning to the data, helping users better understand, locate, and use the data. Metadata includes key information such as data source, data quality, data access permission, data update frequency, and data relationships, and is an indispensable part of data management and data analysis. Metadata enables organizations to track the history of data, ensure data quality and compliance, optimize data architecture, and support data-driven decision-making.

Metadata management is a systematic set of processes and practices designed to ensure the accuracy, consistency, and availability of metadata within an organization. It involves the collection, storage, maintenance, updating, and application of metadata, including defining metadata standards, designing metadata schemas, implementing metadata quality control measures, monitoring metadata usage, and providing metadata access services.

The core activities of metadata management include:

• Develop a metadata management plan: Identify the participants in metadata management, collect management requirements, and define the type, scope, and attributes of metadata. Design metadata architectures to ensure consistency with data models, master data, and technology architectures, and develop specifications for metadata management.
• Perform metadata management: Use a metadata management platform to collect, store, and manage metadata. Visualize data lineage, apply metadata to unstructured data modeling, and automatically maintain data asset catalogs.
• Supervise and inspect metadata management: Check and govern the quality of metadata, ensure the standardization of the metadata governance process, and record the inspection results to establish a baseline.
• Improve metadata management: Based on the results of the inspection, work with relevant stakeholders to determine the metadata optimization plan, formulate an improvement plan, and continuously improve the method, architecture, technology and application of metadata management.

Metadata is the backbone of the entire data asset management process, and it enhances the deep understanding of business and management through business metadata and management metadata. Technical metadata effectively manages change risk by automatically parsing the data flow process, tracking data lineage, and identifying the impact of data model changes. Metadata also supports automatic maintenance of data asset catalogs and data service catalogs, such as Guangdong Power Grid monitoring data links through the metadata management platform to achieve abnormal location and early warning analysis.

Metadata management is critical to improving the performance of your data warehouse, optimizing data analytics, supporting data governance and compliance, and maximizing the value of your data assets. With effective metadata management, organizations are able to better understand data assets, increase data transparency, reduce data management costs, and facilitate data sharing and collaboration across departments.

7. Data development management

Data development refers to a set of technical and analytical activities aimed at designing, building, testing, and maintaining data systems and databases. It includes the creation of data models, the development of database schemas, the implementation of data integration and cleaning processes, and the development of data interfaces and APIs. Data development also involves data migration, data security, performance optimization, and the formulation of data backup and recovery strategies. In addition, data development may also include the construction and optimization of data warehouses that support business intelligence and data analysis.

Data development management is a series of processes and activities that involve planning, directing, and supervising the entire data development lifecycle, including requirements analysis, data model design, database development, data integration, data cleansing, data migration, API development, performance optimization, and data security and privacy protection. This management process ensures that data solutions meet business needs while maintaining data quality and consistency, supporting data-driven decision-making and operational efficiency across the enterprise.

The core steps of data development management can be summarized as follows:

• Develop a data development management plan: Establish standards and processes for data integration, development, and operations.
• Perform data development management: Develop an integrated data development tool, which includes data integration, program writing, testing, task scheduling, and O&M. Perform logical or physical data integration according to established data integration specifications, and customize data development according to the needs of data consumers.
• Supervise and inspect data development management: Continuously monitor the execution of data processing tasks, and quickly respond to and resolve any exceptions.
• Continuous improvement of data development management: Regularly review and evaluate the effectiveness of data integration, development, and O&M, and continuously optimize and update relevant specifications and processes based on feedback and analysis results.

Through these steps, data development management ensures that data development activities are efficient, prescriptive, and adaptable, supporting an organization's data-driven strategy.

8. Circulation of data assets

Data asset circulation refers to promoting the realization and utilization of data assets within and across organizations through various modes such as data sharing, data openness, or data transactions. This process involves breaking down data silos and ensuring data liquidity and accessibility, while considering data security, compliance, and the interests of both parties to the transaction. Data asset circulation includes not only the exchange of data between different departments or teams within the organization, but also the interaction of data with external partners, government agencies, and even the public, with the aim of maximizing the economic and social value of data.

• Data sharing involves breaking down data silos between departments within an organization and creating a unified sharing mechanism to accelerate the flow of data resources within the organization.
• Open data refers to making data accessible and understandable to the public. For governments, this usually involves opening up public data resources, while for enterprises, it may involve disclosing information about business operations to facilitate data integration between governments and enterprises.
• Data transaction refers to the commercial activities with data or its derivatives as the main transaction object on the basis of ensuring security and compliance between the two parties under the constraints of the contract.

The main difference between data sharing, data openness, and data transactions is the nature of the data and the scope of subjects involved in its exchange. The circulation of data of a public nature within an organization is considered data sharing, such as the exchange of information between government departments, and when its circulation extends outside the organization, it constitutes data openness, such as public data that is open to the public. The circulation of private (commoditized) data within an organization is a data sharing, such as the exchange of data between different departments of the enterprise, and when the circulation occurs outside the organization, it constitutes a data transaction. It is important to note that data transactions do not always involve monetary settlements, and according to the principle of equivalent exchange, whether it is a traditional peer-to-peer transaction or an intermediary transaction through a data trading platform, it may include a transaction form of "data for data" or "data for goods".

The significance of data asset circulation is to promote the optimal allocation and efficient utilization of data resources, enhance the liquidity and availability of data through data sharing, openness and trading, so as to promote cross-departmental and cross-industry collaborative innovation, improve decision-making quality and business efficiency, stimulate the emergence of new formats and models, accelerate digital transformation, and ultimately maximize the value of data, providing new momentum for economic growth and social progress.

9. Data value assessment

Data value refers to the sum of the economic, intellectual, or social impact of data assets on individuals, organizations, or societies in a given context. It includes not only the direct economic benefits of data, such as cost savings or increased benefits through data transactions, better decision-making, improved efficiency, etc., but also the role that data plays in fostering innovation, enhancing customer experience, and supporting strategic planning. Realizing the value of data depends on the quality, relevance, accessibility, and effective use of data, which is key to the success of digital transformation and data-driven decision-making.

Data valuation is a systematic process that aims to measure the specific value and potential impact of data assets on an organization through both quantitative and qualitative methods. This includes analyzing the quality, completeness, accuracy, timeliness of data, and its role in supporting decision-making, optimizing operations, driving innovation, and enhancing the customer experience. Data valuation helps organizations understand the business potential of their data assets, develop data management and monetization strategies, and optimize data investments while ensuring compliant use and risk control of data assets. With this assessment, companies can more accurately unlock the economic value of data, increase competitiveness, and gain an advantage in a data-driven market.

At present, many domestic and foreign standardization institutions, financial accounting professional organizations and technical consulting service companies are actively conducting in-depth research and exploration from their own professional perspectives. The results of these organizations are shown in the figure.

At present, the industry's focus on data valuation is mainly focused on three core areas:

• Data asset valuation: Demonstrate the financial value of data through direct quantitative means.
• Data asset accounting: Treat data assets as the core assets of the enterprise and include them in the balance sheet.
• Multi-role participation in the data element ecosystem: Promote different participants to enter the circulation and transaction cycle of data elements.

Most enterprises focus on quantifying the value of data assets in the understanding and implementation of data valuation. For example, by creating the Data Asset Operation Report, Shanghai Pudong Development Bank comprehensively measures the scale, value, operational efficiency and management level of data assets, objectively evaluates the application effect of data in key business scenarios, and clarifies the role of data in improving business quality and efficiency and promoting business model transformation, thus forming a positive cycle of data management and application.

Government agencies and industry associations, as regulators of the data element market, pay more attention to the latter two aspects. In 2022, the Accounting Department of the Ministry of Finance issued the Interim Provisions on the Accounting Treatment of Enterprise Data Resources (Draft for Comments), which proposes guidance on the accounting treatment of both parties to a data transaction, as well as the accounting treatment of whether data resources can be included in assets. In the same year, the China Appraisal Association issued the Guiding Opinions on Data Asset Valuation (Draft for Comments), which provides specifications for the operation of asset appraisal institutions and professionals in the process of data asset valuation, including clear guidance on assessment objects, data quality evaluation, evaluation methods and information disclosure requirements.

10. Data asset operation

Data asset operation refers to the process of improving the economic benefits brought by data assets and ensuring and promoting the development of various undertakings through the rational allocation and effective use of data assets. It involves treating data as a completely new form of asset and strengthening relevant systems and applications in accordance with the standards and requirements of asset management. The core of data asset operation lies in the realization of data value, through analysis and mining of the value of data assets, and transforming them into user value, group value, and social value, so as to promote the upgrading of social governance, the transformation of economic growth model, and benefit people's livelihood.

The key activities of data asset operation include inventory, evaluation, governance, and sharing of data assets, aiming to achieve stable operation of data assets. It includes not only data sharing within an organization, but also data openness, trading, and collaboration, as well as the assessment, planning, and auditing of data assets, all of which can open up new business opportunities.

The key activities of data asset operation mainly include the following aspects:

• Data asset identification and inventory: Comprehensively sort out the existing data types, sources, storage methods and utilization status of the enterprise to form a data asset inventory.
• Data governance: Establish a data governance framework, including data quality management, data security, and data privacy protection, to ensure the compliant use and maintenance of data assets.
• Data asset valuation: Evaluate the value of data assets to determine their value to the enterprise and potential business value.
• Development and utilization of data assets: Improve the rules for the development and utilization of data assets, and form a development and utilization mechanism with clear rights and responsibilities, transparent processes, and controllable risks.
• Data sharing and openness: Facilitate the sharing and flow of data inside and outside the organization while ensuring security and compliance.
• Authorized operation of data assets: Sensitive or inappropriate data for disclosure is authorized to be operated and provided to society and enterprises after processing.

Together, these activities form the core content of data asset operations, helping enterprises fully explore and realize the business value of data assets.

The successful implementation of data asset operation requires the construction of data asset operation capabilities, which is a key link for enterprises to realize data assetization and enter the data element market. With the deepening of data management, enterprises have moved from the stage of data resourceization to the stage of data assetization, so as to expand the application scope of data assets, build a virtuous closed loop between data users and managers, and continuously release the value of data assets. In addition, data asset operation also includes the establishment of good organization and awareness, processes and norms, platforms and tools to cultivate the soil for data asset operation within the company. On this basis, through the inventory, evaluation, governance and sharing of data assets, the core business data assets are held in close hands to achieve the stable operation of data assets.

Data asset operation is the key to maximizing the value of data assets, which requires enterprises to transform from the stage of data resource to the stage of data assetization, and improve the level of data management and operational efficiency through a series of strategic measures, so as to gain a competitive advantage in the era of digital economy.

summary

After an in-depth discussion of the top 10 activities of data asset management, we can see that the effective management and application of data assets is critical to the success of enterprises in the digital era. Every aspect of data asset management is the cornerstone of building the core value of enterprise data assets. As technology continues to advance and the market environment changes rapidly, organizations must continuously optimize their data asset management practices to remain competitive and market leading.

To achieve this, organizations need to build awareness of data assets and strengthen cross-departmental collaboration to ensure comprehensive and consistent data governance. At the same time, enterprises should also invest in advanced data analysis tools and technologies to improve the level of automation and intelligence of data processing. Through these measures, companies will not only be able to improve the efficiency and accuracy of decision-making, but will also be able to open up new revenue streams through data-driven innovation, ultimately achieving sustainable growth and development.

In this context, the role and value of Finesoft as a leading data analysis and business intelligence solution provider have become particularly prominent.