What do the developments of the digital economy and digital transformation have in common? The answer to this question is also obvious. Data is the foundation of the digital transformation of the digital economy and the core element that promotes the rapid development of both. In the digital age, data has become an important strategic asset of individuals, institutions, enterprises, and even the country. Therefore, how to make good use of data and make data truly a data asset requires the construction of data asset management.
"Data return rate" increasingly determines the core competitiveness of enterprises
Like physical assets and intangible assets, data assets first appear as a resource. That said, not all data resources can be upgraded to data assets. Data resources as "assets" are generally manifested in the following two forms: First, new value can be created through reasonable application. The second is that through processing, it can help existing products to achieve an increase in value or profit.
Data Visualization - Pico Data Business Intelligence BI Visual Analysis Platform
In 2019, the "Data Asset Management Practice White Paper (Version 4.0)" released by the China Academy of Information and Communications Technology started from the concept of assets and specified the category of data assets. Physically or electronically recorded data resources for future economic benefits, such as electronic data, documents, and related materials. It can be seen that the concept of data assets has basically become an industry consensus, no matter from the form of expression or the basic definition. In contrast, the application channels and management methods of data assets are still in the exploratory stage, and the management of data assets still faces many challenges.
The resource-based theory holds that an enterprise is a collection of various resources, and the competitive advantage of an enterprise comes from special heterogeneous resources. Like other resources, data generation, storage, maintenance, and management also require costs. Compared with the return on assets, the "return on data" of future enterprises will increasingly determine the core competitiveness and long-term development capabilities of enterprises.
On the one hand, with the increasingly obvious trend of deep integration of the digital economy and the real economy, the market's demand for and investment in the acquisition, collection, storage, analysis, and application of data resources has increased significantly, and data has increasingly become an important part of enterprise organization's production, operation and Fundamental strategic resources for the entire transaction process.
On the other hand, it is difficult to directly form the core competitiveness of enterprises in terms of data alone, and the transformation of data from resources to assets requires further mining of data. However, in reality, enterprise data resources are often scattered in multiple departments and business systems in the form of data islands, and the inconsistency of data statistical standards and observation dimensions also reduces the efficiency of data utilization. This also directly leads to the inability of enterprise decision makers and business personnel to be keenly aware of the distribution and update of data, and it is even more difficult to realize in-depth mining of data.
Data Visualization - Pico Data Business Intelligence BI Visual Analysis Platform
In this context, realizing data asset management and tapping the potential value of data assets is a prerequisite for realizing the digital transformation of an enterprise, and it is also an important way to further exert the competitive advantages of an enterprise and improve the quality of enterprise development.
Data asset management should be oriented towards value creation
Fundamentally speaking, data asset management should be guided by value creation thinking, that is, data assets should be regarded as core assets that can continuously create value for enterprises on the same status as physical assets, knowledge assets, and human assets. This requires enterprises to build a complete and unified management and control structure to manage them. On the premise of following the principles of cost and efficiency, focus on the core market, core business and core resources of the enterprise, and coordinate the entire enterprise to carry out data asset inventory, confirmation of rights, and value evaluation and operations and circulation.
Not only that, in the process of transforming from "data resource management" to "data asset management", in order to unlock the new value of data, it is necessary to start from the management team, business department and risk department to ensure the normalization, standardization and Securitization.
First, it is necessary to cultivate the data awareness ability of the management team. Proper data asset management can enhance the value of data. Although data assets can provide new development ideas for enterprises, the management system and organizational structure that match data assets must also be adjusted accordingly. The fundamental purpose of this is to reduce wrong decisions caused by "blind men feeling the elephant" due to incomplete samples, incomplete data, incomplete information or incomplete facts. To achieve this goal, enterprise decision makers and management need to change their cognition and awareness of the value of data assets, improve the core concepts and methods of data asset management, and gradually establish a data asset value system in enterprise operations and management. Realize the demand orientation of data asset management from top to bottom, so as to reduce the trial and error cost of enterprises and improve the efficiency of enterprise decision-making and management.
Data Visualization - Pico Data Business Intelligence BI Visual Analysis Platform
Second, it is necessary to exercise the development and application capabilities of the business team. Quantitative changes lead to qualitative changes, and the purpose of enterprise data asset management is to achieve the best allocation of enterprise resources. Only through the full mining of data by the business team to discover potential value associations and profit opportunities can the previously ignored business value be added. For example, in the management of agricultural data assets, it is necessary to fully mine plant and climate data, and focus on improving the yield and quality of crops. Industrial data asset management needs to fully mine equipment and facility data, and focus on improving the functions and efficiency of machinery and equipment. Data asset management in the service industry needs to fully mine product and customer data, and focus on customer experience and service optimization. This process of capitalizing discrete data will fully release the value of data, provide a strong impetus for the digital transformation of enterprises, and create digital value for enterprises.
Obviously, data comes from the needs of management and will eventually serve the needs of management. It has become an important strategic resource for enterprises to carry out market innovation. However, it should be noted that insufficient data security awareness and insufficient security protection can easily lead to data leakage and other incidents that endanger the enterprise. This requires the relevant security departments of the enterprise to improve risk management capabilities and be vigilant against data risks. Interestingly, data risk management also needs to rely on data technology to solve. Enterprises need to find out the weak links of development according to their own scale, adjust the risk assessment process of data assets, and continuously improve the ability of enterprises to control risks by optimizing concepts and upgrading technologies, so as to better cope with the test brought by the digital economy era to enterprise operations.
How to build a data asset catalog
1. Data asset inventory
The most important thing in the construction of a data asset catalog is to take stock of all the data of the enterprise. Presumably, this step does not need to be explained too much. After all, the content of the catalog must be clarified first. Therefore, the data asset catalog needs to comprehensively sort out the data assets of the enterprise. First, it is necessary to clarify the authority and authority of the data assets from top to bottom, and determine the core data needed by managers. Carry out classification and detailed grading, and then the technical department will sort out data assets in terms of data relationship, data structure, data caliber, and data storage.
Data Visualization - Pico Data Business Intelligence BI Visual Analysis Platform
2. Data asset catalog framework
Friends who often read books or read some papers should understand that the catalog has an overall framework. On the one hand, this framework should put similar or similar content in a large catalog; Displayed on the table of contents, you can know at a glance which content is in which topic chapter, which page is the focus of the content, etc. Therefore, the enterprise data asset catalog needs to sort out the data themes and core business through asset inventory, and then gradually divide the secondary catalog and the third-level catalog from the perspective of business.
Data Visualization - Pico Data Business Intelligence BI Visual Analysis Platform
3. Data label management
After the construction of the data asset catalog framework is completed, the enterprise can fill in the content. At this time, a data labeling system is needed to organize all the inventory data of the enterprise according to different themes, such as business themes, organizational themes, security themes, technical themes and other dimensions As the main module content of the catalog, and then use other dimensions as labels. These data assets can be further improved, because many data assets have multiple attributes and belong to multiple categories, and relevant data can be found in multiple databases, so the relationship between each other can be marked according to the different data relationships. Bloodlines are very good at tracking the source of data and discovering data problems.
Data Visualization - Pico Data Business Intelligence BI Visual Analysis Platform
4. Data asset catalog application
After completing the data asset catalog, the enterprise can truly establish an entire data asset management system, and continuously optimize the data through data governance, so that employees of different departments and levels of the enterprise can find the data they need in the data assets. And because of the existence of the data asset catalog, enterprises can also better improve the data security protection system, keep core data confidential, and avoid leakage and other events that affect the development of the enterprise. In short, after the data asset catalog is established, the enterprise can further use the data asset to create value, meet the needs of the enterprise, and continue to manage it.
Disclaimer: The articles published by this official account are originals of this official account, or edited and sorted out excellent articles found on the Internet. The copyright of the article belongs to the original author, and it is only for readers and friends to learn and refer to. For the non-original articles shared, some are unable to find the real source. If the source is wrongly marked or the pictures, links, etc. used in the article include but are not limited to software, materials, etc., if there is any infringement, please contact the background directly to explain the details The article will be deleted as soon as possible in the background. Apologies for the inconvenience caused.