Data governance is defined as the collection of data management processes and procedures that help an organization manage its internal and external data flows. It aligns people, processes and technology to help them understand data so it can be turned into an enterprise asset. At present, data governance has become a basic link in the digital transformation of enterprises.
The need for data governance
With the development of business, companies are applying more and more data, and data is growing rapidly with the business. At the same time, it also brings about various problems in data storage, data model construction, data quality, usage specifications, etc., such as complex data structures, data redundancy, data islands, etc.
Given that data has become a new productive force and the data governance system has become a typical representative of new production relations, data governance issues are urgent. This requires enterprises to use data as the object, and on the premise of ensuring data security, establish and improve a rule system, straighten out the rights and responsibilities of all participants in all aspects of data circulation, and form a healthy interaction and joint construction and governance among multiple participants. A shared data circulation model can maximize the release of data value, promote the modernization of the data element governance system, and ultimately achieve the purpose of empowering enterprise business development.
The importance of data governance
Today, the power of data in driving business growth is well known. Effective data governance enables businesses to derive maximum benefit from their most valuable assets. With high-quality data, businesses can gain insights for better business decisions and increase efficiency and productivity.
For enterprises, the importance of data governance is mainly reflected in the following three aspects:
1) Help enterprises adjust data strategies in a timely manner and reduce data management costs;
2) Through data governance, re-adjust the current organizational roles and responsibilities, structures and tools to make the work process more reasonable, reduce redundant consumption, and generate meaningful business insights in a timely and economical cost;
3) Protect enterprises from compliance and regulatory issues that bad and inconsistent data can bring.
Data quality governance is a long-term and continuous work, and it is impossible to expect it to be achieved overnight. In the process of governance, it is necessary to continuously optimize quality shortcomings and consolidate the cornerstone of quality. Set goals and responsibilities, actively cooperate and take actions, make full use of platform tools, and jointly build a data utopia so that data can create real value.