Regarding data asset management, it has been a hot topic in the data governance industry in the past two years. Of course, there are reasons for the national policy support and direction guidance we mentioned earlier. On the other hand, our data governance colleagues learn and absorb excellent data governance theories from abroad, further think about how to apply the theory in practice, and practice the realization of data value in combination with the actual situation of our country.
By studying excellent cases and related theories at home and abroad, as well as excellent articles and white papers published in the industry in the past two years, I also summarized the architectural framework of the data asset management system. I hope to explain how we manage data assets from this framework. Below I will express some ideas in detail by framework and region, and of course I also draw on the content of the recently released excellent data asset management system.
Regarding the content of the recently released "Commercial Bank Data Management Construction Practice Guide", maybe I started the data governance practice from the banking industry. I personally agree with the point of view. Explain the preface of "data asset management system".
The first is about the five major activities of data asset management: asset processing, privacy protection, asset ownership confirmation, asset identification, and value evaluation. These five management activities reflect the key activities that we determine what kind of data is data assets and how to transform data resources into data assets. Through these five key activities, we can convert the data in our organization into data assets according to the route of data, data resources, and data assets, which is what we often call the process of data assetization.
From another perspective, let's look at how the enterprise realizes the value of data. According to the enterprise's data strategy, the enterprise's business activities are digitized (business digitization) to form data resources; through the operation system of data assetization, the data is transformed from data resources It is data assets; data assets internally tap the data potential of the enterprise organization, realize data sharing externally through data productization and other methods, and realize the value of data. This is the process of asset productization; finally, through data assets to the market, data elements marketization.
When data governance was first started in China, more consideration was given to data governance and data management related activities, data standardization of data within the organization, improvement of data quality, etc. In the past two years, we have considered more through the data asset management system to achieve The value of data in enterprise organizations, data elements have recently been upgraded to one of the productivity elements, we need to consider how to manage data assets, and form a data asset management system to realize the value of data elements. So what is the relationship between these systems? The following picture will clearly express the relationship between them and their mutual dependence and support.
Ok, we have clarified the positioning of the data asset management system and the key activities of data assetization, and we will explain how we realize the value-oriented data asset management system region by region.
1. Organized
The above shows two examples of data management organizational structure, which are designed from the perspective of data governance management and data asset management organization. We can find that their hierarchical frameworks are actually the same, which are designed in three layers: decision-making layer, management layer and execution layer. From this point, we can know that the essence of data asset management also belongs to the category of data management. The essence is also the content of organizational management. From the perspective of management and related theories, the level of organizational structure design is also the same.
Therefore, in fact, when we design the organizational structure of data asset management to ensure the operation of our data asset management system, we can refer to the organizational structure of data governance that we have successfully implemented before or directly apply it, but we need to apply the corresponding The detailed definition and responsibility definition of stakeholder roles and organizational departments in the hierarchy are clearer and more suitable for data asset management. For organizations that already have a mature data governance organizational structure, I personally suggest that only relevant stakeholders or management departments need to be added to relevant departments, and there is no need to establish a new data asset management organizational structure.
The above picture is to build our data asset management organization from another perspective, using a data management organization construction model that may be considered more pragmatic and designed to be driven by data applications.
2. There is a system
Regarding the system, I think I have talked a lot about this aspect in the articles and theories related to data governance. Everyone also realizes that there is a need for relevant systems to support our data management activities. The following is a diagram to express it. If the data assets The management system is formulated separately, and the relationship with the traditional data management system.
In fact, we can also include the data management system into the data asset system. It mainly depends on the status quo of the existing data management system of the enterprise and the strategy of data management.
3. Standardized
Under normal circumstances, the management system is more like the content of legislation, that is to say, the system is to standardize the basic principles and operation scope of various data management, and of course there are other aspects such as organization-related guarantee requirements.
In specific data asset management activities, we also need relevant management norms and processes to guide and standardize our data asset management activities. Maybe in terms of specifications, different companies will be similar, but regarding the process, it can be said that different companies will have greater differences. I personally suggest that the specifications can be made as clear as possible in the early stage, and the process needs to be adjusted during the implementation process. .
The following provides the flow of several data asset management activities in the process of data asset management.
4. Controlled
There are systems and norms, which only regulate how everyone does things. From a management point of view, we also need to ensure that everyone does things correctly and standardizedly through management and control. Systems and norms exist more in the form of documents, and through training and publicity methods to improve the awareness of relevant stakeholders and regulate the activities of relevant stakeholders. Data management and control will use a variety of means, such as assessment, evaluation, monitoring, performance, etc., and often use data management tools or platforms to implement systems and norms.
The above figure is an example of the main action steps of data management, and from the perspective of data assetization, how various data management and control activities ultimately support data assetization activities.
5. There are services
Only by serving data assets to all stakeholders can the value of data assets be reflected and brought into play. In the process of data asset services, tap the potential of data within the enterprise organization, and better promote data sharing and data circulation within the enterprise organization. Data assets can also be shared externally through services to realize the realization of data assets.
Regarding the service of data assets, we can explain it in the following two aspects. The first is the value evaluation of data assets. Through relevant qualitative and quantitative indicators, we can accurately evaluate the value of data assets, explore the potential of data assets, and promote the effectiveness of data. , and finally realize the realization of the production capacity of data assets.
The evaluation framework and evaluation system are described below:
Through the potential evaluation, performance evaluation and capacity evaluation of data assets, it fully reflects the intrinsic value, cost value, business value, economic value and market value of the data assets of the enterprise organization.
Secondly, to evaluate the value of data assets, it is necessary to provide data services of data capitalization results in order to realize data assets. We know that data assetization can be divided into the data asset formation stage and the management stage. The following fully explains how we provide data capitalization results in the form of data services.
6. Operational
We talked about the organization and system, norms and processes, data management and control, and data services earlier. We are all talking about how we realize the process of data assetization and the related guarantee content. But even if the data is capitalized, or even served, and the value is realized, just like running a business, we need long-term operations for data assets to ensure that our data can stabilize the results of data capitalization, and Continue to play its value and continue to obtain realized benefits.
Regarding the operation of data assets, this is what everyone has been discussing in the past year. Here, first of all, let’s take a look at an example of a data asset operation system of a large enterprise.
From the above examples, we can observe that this operating system ranges from the formation of internal data assets to the pricing of final data assets. It fully embodies the data asset operation system framework that fully considers data security and data asset value realization.
From the above examples, we understand that in the operation of our data assets, we need to fully consider the data security and compliance requirements in the process of data asset collection, circulation and use, so that data assets can be circulated on the premise of security and compliance.
In addition to considering data security compliance, we also need to design and organize our operational support system from the core elements of data asset operations - "people", "goods", and "fields".
7. Have tools
I think everyone is aware of the importance of data asset management tools. We all know that without proper tool support, it is difficult to guarantee the actual implementation of the data asset management system due to the magnitude of modern data and the complexity of management. We need tools to implement the implementation, monitoring, and analysis of our system, and we also need tools to solidify and accumulate the results of our management system.
Regarding the tools, there are hundreds of flowers in the market, each with its own characteristics. Here I distinguish the data management tools (functions) that may be needed from the route of data generation, resourceization, and assetization. For better illustration, the functions of the data governance platform and the data asset management platform are designed separately. Of course, the two can actually be combined into one data management platform.
