Author: Zen and the Art of Computer Programming
In recent years, with the transformation and upgrading of data-driven enterprises, the influx of various data sets has also increased, and the value of data has become more and more sufficient. As an important part of the means of production of enterprises, data has gradually become a rigid demand in the business field. However, how to ensure the quality of data output, effectively utilize data resources, and meet the needs of data consumers is crucial for effective management and distribution of data. Therefore, "data science" and "data engineering" in the traditional sense have gradually evolved into a new discipline - "data governance". This article will discuss the status quo of data governance and the challenges it faces, from the perspective of data productization, sort out the general rules of the data governance process, and combine actual cases to illustrate the specific methodology of data governance.
2. Explanation of basic concepts and terms
2.1 Data Governance
Data governance (Data Governance) refers to a mechanism to effectively achieve organizational goals by supervising and managing data generation, circulation, use, and sharing processes to ensure data security, availability, correctness, and integrity. The goal of data governance is to ensure the maximum value of data, establish sound infrastructure, optimize data service capabilities, improve data management level, promote information sharing, promote innovation and development, and let enterprises value their own core competitiveness.
Data governance can be defined as "managing trusted data", that is, "motivating, guiding and rewarding people to complete data work and collaborate on data work, so as to enhance the value of data, promote innovation and development, and help enterprises continue to grow". At the same time, it also includes two related but independent tasks, namely "determine data goals, determine data roadmap", and "achieve data governance goals, maintain data value and data brand". Data governance aims to ensure that the data brand can influence the information and decision-making of the enterprise, promote the development of business innovation, reduce the growing uncertainty, and help the enterprise to accelerate the development.
According to the task of data governance, data governance can be divided into three stages: the first stage is "governance discovery", the main purpose is to