How to ensure the effective development of data asset management?

In the previous two articles, we introduced the basic information and activity functions of data asset management.

Previous postGuiding the way and taking stock of the top 10 activities of data asset management!

Data asset management is a long-term and systematic work, in order to ensure the effective development of data asset management activities and promote the smooth progress of data asset management, strategic planning, organizational structure, institutional system, platform tools, long-term mechanisms and other safeguard measures have become extremely important. Data asset management safeguards are critical for any organization looking to remain competitive in a data-driven economy. By implementing these measures, organizations can ensure that their data assets are properly managed, unlocking the potential of data to drive business growth and innovation.

This article will introduce the safeguard measures for data asset management from the aspects of strategic planning, organizational structure, institutional system, platform tools, and long-term mechanism.

1. Clarify the strategic management of data asset management

Strategic planning is a series of actions that an organization takes to set the direction and allocate resources for its future, and plays a vital role in ensuring the continued and stable development of the organization. In the digital era, data strategy has evolved into the core of an organization's refined data asset management, which acts as a "compass" to guide the long-term and efficient operation of data asset management. Strategic management involves the comprehensive planning, firm execution, and regular evaluation of data strategy, which establishes the medium- and long-term goals of data asset management, defines the priorities of management activities, clarifies the total amount of resources required and how they are allocated, and ensures that the data strategy is aligned and synchronized with the overall business strategy of the organization.

Strategic planning for data asset management is the foundation of the entire effort. It involves a holistic look at an organization's data resources, clarifying the vision, goals, and priorities of data management. Strategic planning should be tightly aligned with the organization's business strategy to ensure that data asset management supports and drives the business.

• Strategic planning forms the cornerstone of strategic management and provides a clear navigation map for data asset management.

This process involves an in-depth assessment of the organization's current data asset management capabilities and closely aligned with the organization's macro development plan and IT strategy to establish the long-term and short-term goals, management principles, and key activities of the data strategic plan. It also includes identifying key business areas, defining the scope of data, prioritizing activities, and determining the size and allocation of required resources.

• Strategy execution is a key stage of strategic management, which ensures that strategic planning can be translated into concrete actions.

Ensure that the strategic plan is effectively executed by refining the strategic plan into executable phased improvement plans and clear implementation paths, designating participating teams, and dynamically adjusting short-term strategies based on real-time feedback during the implementation process.

• Strategic assessment is a key step to improve the effectiveness of data strategy management and enhance its guiding value.

By evaluating the strategy content and execution process from multiple dimensions such as goal completion rate, return on value and cost-effectiveness, the efficiency and effectiveness of strategic planning and execution can be continuously optimized. Taking China Unicom as an example, the company has accelerated data governance around its new strategic positioning and development direction, and promoted the liquidity and value enhancement of data elements. Through in-depth analysis of the external environment and internal resources, an enterprise-level data governance system that matches the company's digital transformation strategy is built. The company also clarified the strategic objectives, planning framework, key measures and phased goals of data governance, strengthened the overall implementation of strategic planning, and provided a set of evaluation criteria for resource allocation and the implementation of tasks during the planning period to ensure the realization of strategic goals.

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2. Build a clear and unambiguous organizational structure

The effectiveness of data asset management depends heavily on a clear and efficient organizational structure. This is because the organizational structure provides the necessary governance framework and operational mechanisms for data asset management. A well-designed organizational structure maximizes the value of data by ensuring that data assets are managed throughout their lifecycle.

This architecture typically consists of four levels, each with its own unique responsibilities and roles:

• Decision-making level: The decision-making layer is the highest authority for data asset management and is typically led by an organization's chief information officer (CIO) or chief data officer (CDO). They are responsible for setting data asset management decisions and strategies across the organization, and setting the long-term goals and direction of data management. In addition, decision-makers are responsible for designing and implementing a review mechanism to ensure that data asset management activities are aligned with the overall goals of the organization and to regularly evaluate their effectiveness.
• Organizational Coordination Layer: The organizational coordination layer typically consists of a virtual data asset management committee that coordinates and communicates between different departments and teams. Its responsibilities include refining the metrics for data asset management to ensure they are both challenging and achievable. In addition, the organizational coordination layer is responsible for overseeing the allocation and management of resources and ensuring that data asset management activities receive the necessary support.
• Data Asset Management Layer: The data asset management layer is undertaken by the Data Asset Management Office (DAMO) or similar department and is the main executor of data asset management work. Responsibilities at this level include building and maintaining organization-level data architecture, business architecture, and IT architecture to ensure data consistency and integrity. In addition, the data asset management layer is also responsible for formulating and updating the institutional system and long-term mechanism of data asset management, such as data governance policies and data quality standards. Regularly inspect and summarize data asset management, and report the progress and results of work to the organization, coordination and decision-making level.
• Work execution layer: The work execution layer is composed of business departments and IT departments, and is responsible for implementing data asset management in specific data projects. This includes the collection, storage, processing, analysis, and distribution of data. The work execution layer needs to work closely with the data asset management layer to ensure that the data asset management strategy and system are effectively implemented. In addition, the work execution layer is also responsible for collecting and providing feedback on problems and challenges encountered in data management, providing valuable front-line information for the data asset management layer.

These four levels depend on each other and support each other, and together form a complete data asset management organizational structure. By clarifying the responsibilities and roles of each level, organizations can ensure that data asset management activities are carried out in an orderly and efficient manner, thereby maximizing the use of data assets and supporting the organization's business development and strategic decisions.

To evaluate whether the organizational structure of a data asset management is well-designed, it is mainly from the following perspectives:

• Clear roles and responsibilities: An effective organizational structure starts with defining clear roles and responsibilities to ensure that each team and individual understands their own roles and expectations in data asset management. This helps reduce overlap and conflict of responsibilities and increases the efficiency of team collaboration.
• Prompt and consistent decision-making: A good organizational structure ensures a rapid and consistent decision-making process. The decision-making level can quickly respond to market changes and business needs, formulate corresponding data management strategies, and quickly communicate them to the executive layer through the organizational structure.
• Smooth communication and coordination: Frequent communication and coordination between different levels and departments in the organizational structure are required. An efficient architecture facilitates the flow of information and ensures that all aspects of data management work together to create synergies.
• Rational allocation of resources: Through the organizational structure, organizations can rationally allocate resources related to data management, including human, financial, technical, and time. This helps to optimize the use of resources and improve the ROI ratio of data management.
• Effective management of risk: The organizational structure provides a management framework for risk in data management. With a clear risk management process and assignment of responsibilities, organizations are able to identify and respond to risks and challenges that may arise in data management in a timely manner.

In summary, a clear and efficient organizational structure is the key to successful data asset management. It provides a solid foundation for data asset management, ensuring that data assets are effectively managed throughout their lifecycle, maximizing the value of data and supporting an organization's business development and strategic decision-making.

3. Establish a hierarchical institutional system

The institutional design of data asset management usually follows the principle of hierarchy, and the system can be subdivided into four main levels according to different management details: the overall regulations, management methods, implementation rules and operational specifications of data asset management at the organizational level. The content of each level is summarized as follows:

• General provisions: From the perspective of senior management and organizational coordination, the general provisions cover data strategy, role and responsibility allocation, responsibility system construction, etc., and clarify the objectives, organizational structure and responsibility allocation of data asset management.

• Management Approach: This level focuses on the data asset management layer, clarifying the objectives, principles, processes, monitoring mechanisms, and evaluation and optimization strategies of each management activity.

• Implementation Rules: Combined with the requirements of the management measures, the implementation rules set out in detail the specific implementation standards, norms and processes of each activity from the two levels of management and implementation.

• Operation specification: The operation specification is more focused on the implementation level, and further clarifies the work process, operation guidelines and templates according to the implementation rules, providing specific guidance for daily operations.

The business case provides a practical perspective on data asset management and is a useful complement to the institutional architecture. Taking Bank of Communications as an example, the bank has developed a "work manual" mechanism on the basis of overall regulations and management methods in response to the needs of theoretical localization and practical strategy. This mechanism is a key tool for the implementation of data asset management, covering multiple functions of data asset management. The workbook describes and analyzes the actual project in detail from multiple perspectives such as demand background, strategic plan, and implementation process, and summarizes experience and problems. It is not only used as a practical resource for internal training to accumulate team knowledge and experience, but also as an exemplary business case that is of great significance for guiding and optimizing long-term asset management efforts.

Fourth, choose a platform tool with perfect functions

The data asset management platform tool is an important part of the enterprise big data architecture, which is located at the upper layer of the big data platform and provides strong technical support for the management activities of data assets. These tools not only improve the efficiency of data management, but also enhance the trustworthiness and security of data through automation and integration. The following is a further elaboration of the data asset management platform tools:

1. Data asset management from the perspective of management

• Planning: The data asset management platform tool establishes a clear data responsibility system through refined role assignment and authority management. This process also includes comprehensive requirements management of data assets, as well as a comprehensive assessment of the current status of data assets, such as asset size, distribution, trustworthiness, and security.
• Execution: During the execution phase, platform tools support new additions and modifications to data management standards and specifications to ensure that data development activities follow established rules. In addition, the tool supports task orchestration and O&M, improving the automation of data management.
• Inspection: The platform tool can track the consistency of the data model, the application of standards and specifications, the processing of problem data, and the response results of data security incidents, providing continuous monitoring and quality assurance for data management.
• Improvement phase: During the improvement phase, the tool allows you to flag issues found during the inspection process and generate recommendations for improvement. Through statistical analysis and inspection of data, a knowledge base is formed, the improvement process is quantified, and closed-loop management is realized, so as to continuously improve the quality and efficiency of data management.

2. Data asset management from the perspective of development

• All-in-one platform: The data asset management platform tool comes in an integrated form, integrating data model management, data standard management, data quality management, data security management, metadata management, and data development, forming a collaborative ecosystem.
• Compliance with standards and specifications: The platform tools support the design and development of data models to comply with industry standards and enterprise specifications, ensure data quality from the source, and reduce data cleaning and correction work in the later stage.
• Whole-process monitoring: By monitoring the whole process of data asset development, platform tools ensure the smoothness and standardization of the development process and reduce development risks.

3. Practical application cases

Taking China Unicom as an example, the company has built an intensive data governance platform that not only realizes the comprehensive and unified management of data assets, but also provides one-stop query, retrieval, governance and operation functions. The platform supports the online management of data standards, as well as the full-link lineage traceability and intelligent analysis of core indicators, tags and models.

In addition, China Unicom's data governance platform has also strengthened the foundation of data quality management and promoted the instrumentalization of data quality management, modeling management, master data management and other capabilities. By forming a complete set of data governance tools and using AI technology to empower them, the platform improves the quality and efficiency of data governance, promotes the standardization and normalization of enterprise data, and provides solid support for the digital transformation of enterprises.

To sum up, the data asset management platform tool is an indispensable technical guarantee for enterprise data management, which helps enterprises achieve effective management of data assets by supporting the PDCA cycle and providing an integrated development environment, laying a solid foundation for the long-term development and digital transformation of enterprises.

Fifth, the implementation of long-term mechanisms

On the basis of data strategic planning, organizational structure and institutional system, the establishment and implementation of a series of long-term mechanisms is the key to ensure the continuous and efficient operation of data asset management activities. These long-term mechanisms include training and publicity, performance appraisal, incentive mechanism, audit mechanism, data culture cultivation and other aspects, which support and promote each other, and together constitute a comprehensive guarantee system.

• Training and publicity: Conduct regular data management training for employees to improve their data awareness and data skills. Ensure that all employees understand the importance of data management and their roles and responsibilities within it through awareness campaigns.
• Performance appraisal: Incorporate the performance of data management into the employee appraisal system, and clarify the goals and standards of data management. Through performance appraisal, motivate employees to actively participate in data management activities and improve the efficiency and quality of data management.
• Incentive mechanism: Establish a positive incentive mechanism, such as reward system, promotion opportunities, etc., to encourage employees to make outstanding contributions in data management. Through the incentive mechanism, the enthusiasm and creativity of employees are stimulated, and the continuous improvement of data management is promoted.
• Audit mechanism: Conduct regular audits of data management activities to assess their compliance, effectiveness, and risk. Through the audit mechanism, problems in data management are discovered and solved in a timely manner to ensure the standardization and security of data management.
• Data culture nurturing: Foster a data-centric culture at the organizational level that encourages data-based decision-making and innovation. Through the cultivation of data culture, employees will pay more attention to data and form a data-driven organizational atmosphere.
• Continuous improvement: Establish a mechanism for continuous improvement and encourage employees to continuously look for opportunities to improve data management. Continuously improve the maturity of your data management and adapt to changing business needs and technology environments through continuous improvement.
• Leadership buy-in: Get buy-in and commitment from senior leadership to ensure data management activities are given the necessary resources and priorities. Drive the implementation and rollout of data management activities within the organization with leadership support.

Through the establishment and implementation of these long-term mechanisms, organizations are able to provide a stable, reliable, and continuous operating environment for data asset management activities. This not only helps to improve the efficiency and quality of data management, but also helps to maximize the use of data, providing strong support for the digital transformation and innovative development of organizations.

summary

After an in-depth discussion of the safeguards for data asset management, including strategic planning, organizational structure, institutional system, platform tools, and long-term mechanisms, it is clear that these measures are interdependent and mutually reinforcing, and together form the cornerstone of successful data asset management. Through these comprehensive measures, organizations can not only improve the efficiency and effectiveness of data management, but also ensure that data assets are maximized to gain an advantage in the fierce market competition.

With the increasing role of data in modern business, data asset management has become an indispensable core competency for organizations. Effective data asset management safeguards ensure that organizations remain agile and adaptable in a rapidly changing, data-driven economy, while fostering innovation and growth. Through continuous optimization and improvement, organizations can continuously improve the maturity of their data asset management and build a true data competitive advantage.

In conclusion, data asset management is a complex and daunting task, but by implementing effective safeguards, organizations can ensure that their data assets are properly managed, unlocking the potential of data to drive continued growth and innovation in the business. As data plays an increasingly important role in business, data asset management will be key to an organization's success. Organizations need to pay attention to emerging data management technologies and methods to further improve the automation, intelligence, and refinement of data asset management.