As a raw resource, data needs to achieve value-added and value realization through data management, data governance, data operations and other work. It is often easy to confuse data management, data governance, and data operations. This article will talk about how to understand the connotation of data governance, data management, and data operations as well as their activities. I hope it will inspire you.
1 Data management
1.1 How to understand data management
Let’s first look at each party’s definition of data management.
DAMA’s definition: Data management is the activities carried out throughout the life cycle of data and information assets in order to deliver, control, protect and enhance the value of them, such as the development, implementation and supervision of plans/systems/procedures and practices. process.
Baidu Encyclopedia’s definition: Data management is the process of effectively collecting, storing, processing and applying data using computer hardware and software technology. The purpose is to fully and effectively utilize the data.
IBM's definition: Data management is a set of practices for ingesting, processing, protecting and storing an organization's data, which is then used in strategic decisions to improve business outcomes.
Simply put, data management is the process of managing data to turn data into data assets or data elements. The popular understanding is a series of management processes that transform an organization's data from uncontrollable, unavailable, and difficult to use to controllable, convenient, and easy-to-use, and can lay the foundation for business feedback.
1.2 Contents of data management
The picture below can better explain the content of data management.
2 Data governance
2.1 How to understand data governance
The International Data Management Association (DAMA) believes that data governance is a high-level management activity based on data management. It is the core of all types of data management and guides the execution of all other data management functions. In DMBOK2.0, data governance is Refers to the series of activities that exercise authority, control and shared decision-making (planning, monitoring and execution) over data asset management.
Although the definition is difficult to understand, what you can know is that data governance belongs to the category of data management and is a series of activities. Based on the author's own business practice, data governance starts from the quality and use of the data itself, aims at improving data quality and data security sharing, emphasizes the processing and process management of the data itself, and solves the problem of data not being found and data being viewed. Problems such as not understanding, not being able to trust the data, being unable to control the data, and poor data timeliness. Without a data governance system as a guarantee, not only can data not be transformed into data assets, but it can also easily cause enterprises to fall into the trap of "data swamp". A good data governance system lays a solid foundation for data asset management and is an important prerequisite and guarantee for the operation and realization of data assets.
Data governance is a long-term and continuous work, because the business in the organization has been running dynamically, and the data is constantly being generated and changed. As long as the data changes, it needs to be governed, so it needs to be managed through continuous data governance. To ensure data security, availability, trustworthiness and ease of use. Just like IT system operation and maintenance, data needs to be continuously maintained to continuously maximize the value of the system.
2.2 What are the activities of data governance?
Let’s look at two pictures first. One is the DAMA 2.0 data governance framework, which is more theoretically guided. The other one is Huawei’s data governance framework, which is more practical. By comparing the two figures, we can see that the main activities of data governance are: data standards, data storage, data quality, metadata, master data, data security, etc.
3 Data Operation
3.1 How to understand data operations
Data operation means that the data owner uses data analysis and mining to use the information hidden in massive data as a commodity and publish it in a compliant form for use by data consumers. Data operations aim to improve the efficiency and competitiveness of the organization through data analysis and application, and to realize the commercial value or social value of data.
3.2 The relationship between data operations and data management
Data operations focuses on the utilization and value creation of data, emphasizing treating data as an asset, and transforming data into insights and insight required for strategic and business decisions by effectively managing and utilizing data to achieve the organization's business goals, thereby unlocking Data value.
Data management focuses on ensuring the quality, integrity, reliability, compliance and security of data and other infrastructure work, and provides specifications and guidance for data access, sharing and use. Through data management, we clarify data distribution, improve data quality, ensure data security, data sharing and exchange, data analysis and mining, etc., laying the foundation for deepening data application and data value-added.
In addition, for most data owners, such as the three major operators, financial companies, power grids, and public sectors, after nearly 10 years of big data development, the data infrastructure within the organization has been basically completed, and the data management system and The data governance system is also gradually improving, forming a certain scale of data assets. However, how to convert these data assets into commercial value or social value cannot be separated from the operation of data. In the future, as the national data element market gradually matures, data element suppliers will pay more attention to data operations and release data value from the perspective of user needs.
3.3 What activities are involved in data operations?
Building data operations capabilities will be the key next step for data owners. Based on the recommendations given by the Big Data Technology Standards Promotion Committee, the main activities of data operations include data directory management, data application scenario construction, data service promotion, data dynamic optimization, data cost management, data value assessment and other links for your reference. The author believes that the activity of confirming data asset rights should be added before data directory management. Regarding the content of data asset confirmation, please refer to the author's article " Data Rights Confirmation for Data Element Circulation "
