扫码加入数字化转型网数据要素X专题研习社：
翻译：
How is the data factored? What are the data elements? What is data element X?
Scan code to join the Digital Transformation Network Data Elements X Project Learning Club:
1. What are data elements?
From data to data elements, there are only two more words “elements”, in fact, the connotation is very different – highlighting the value attribute and asset attribute of data, data as the fifth factor of production outside of land, labor, capital and technology, has basically formed a consensus in all sectors of society. Here are some key features of the data elements:
Fundamental: Data elements are the smallest units that make up the data set and are the basic components of the data.
Descriptive: Each data element describes a specific feature or attribute in the data set, such as a person’s name, age, address, and so on.
Quantifiable: Although data elements can be qualitative or quantitative, they usually need to be able to be quantified or coded in some way to facilitate processing and analysis.
Diversity: Data elements can include different types of data such as numbers, text strings, dates, times, Booleans (true/false), etc.
Correlation: There may be associations between data elements that reveal relationships and patterns between the data.
Functionality: Data elements can be used for different data analysis purposes, such as statistical analysis, machine learning, predictive modeling, etc.
Standardization: To ensure consistency and comparability of data, data elements often need to follow specific formats and standards.
Dependency: The selection and definition of data elements may depend on the objective and context of the analysis.
Privacy: Certain data elements may involve sensitive information that is subject to data protection and privacy regulations.
Actionable: Data elements can be sorted, grouped, filtered, and transformed to support the data analysis process.
In a database, data elements can be thought of as fields or columns that define what types of information can be stored in a table. In data analytics, choosing the right data elements is critical to gaining useful insights. The quality and relevance of data elements directly affect the results and value of data analysis.
2. What is data Factionalization?
Data factionalization includes resource-based understanding of data, establishment of data asset attributes, and realization of data capitalization, that is, the realization path of data element value is divided into three progressive ways: resource-based (cost perspective), capitalized (expected value returns, but unstable), and capitalized (financial attributes, market-based allocation of data elements). These three progressive approaches correspond to the three major activities of data elements: confirmation, valuation, and transaction. Data resourcing focuses on data processing and data governance, emphasizing the potential value of data, and how to confirm data rights based on cost inputs (such as privacy, collection rights, use rights, access rights, resale rights, income distribution rights, etc.). Data capitalization is concerned with how to measure the value of data scientifically and standard. Data value evaluation methods include method, market method, income method and so on. In the future, we will gradually explore how to achieve the concrete measurement presentation of data elements in corporate balance sheets. Data capitalization focuses on the data trading activity itself. Data transaction objects are mainly three types: basic data, data products (standardized), customized products (deep processing, non-standardized).
3. What is Data Factor X?
The “x” in “data factor x” represents a multiplier effect. The “Data Factor X” action aims to improve the efficiency of resource allocation, create new industries and new models, and cultivate new momentum for development by promoting the application of data in multiple scenarios, multi-agent reuse, and integration and innovation of multi-source and multi-type data, so as to achieve a multiplier effect on economic development.
It has the following characteristics:
From connectivity to collaboration: From connectivity based on data generation and delivery to global optimization based on effective application of data, further improving total factor productivity. It can mine useful information from the data and use it for other factors to find the “optimal solution” of enterprises, industries and industries under the constraint of factor resources.
From use to reuse: From thousands of industries using Internet technology to value creation based on inter-industry data reuse, to expand new space for economic growth. As the carrier of knowledge, the reuse of data in different scenarios and different fields will promote the collision of knowledge in various industries and create new value increment.
From superposition to integration: From the efficiency improvement supported by data convergence to the emergence of innovation driven by multi-source and multi-type data fusion, fostering new drivers of economic growth. The integration of different types and dimensions of data will promote the penetration of knowledge in different fields and give birth to new industries and new models.
For example, in the field of industrial manufacturing, support industrial manufacturing enterprises to integrate design, simulation, experimental verification and other data, and cultivate new models of data-driven product research and development to enhance enterprise innovation ability; In the field of financial services, data is used to optimize financial service processes and innovate financial products.
4. What does the Digital Transformation Network Data Elements X project contain?
The Digital Transformation Network Data Elements X topic will focus on data governance, data quality management, data architecture, master data management, data warehouse, metadata management, data backup, data mining, data analysis, data security, big data, data compliance, and other relevant links of the data-related whole industry chain.
Digital Transformation Network Data Elements X topics include:
- Data related external brain support: 100+ data related experts, 100+ data practitioners, 1000+ related materials
- Data Elements X Workshop: Discuss related issues with global data-related experts and practitioners to promote industrial development!
- International certification training: At present, DAMA international certification CDMP has been introduced, and other domestic and foreign certifications are gradually introduced
- Reference of typical cases: Learn typical cases together with members of Digital Transformation Network Data Elements X Study Club to explore the application of enterprise data
