If the enterprise lacks effective data governance strategy, most directly, will produce a large number of “bad” data, the existence of these data may bring greater risk, higher management costs, lower work efficiency and so on. Even in these days when data analysis is so prevalent, bad data will have a negative impact on enterprise decision-making — wrong data will lead to wrong results.
The advantages of having a good data governance plan are significant and include:
Companies will have access to cleaner, higher-quality data, laying the foundation for further data activities
Standardized data asset management methods, processes, and strategies will effectively improve the efficiency of data operations
Make it easier for data to establish a close relationship with the business, and promote the realization of data assets
Improve data security and ensure compliance
Overall, the benefits of data governance are in helping enterprises more effectively translate data value into real business value. With the data blowout still going on, the continued popularity of data-intensive technologies such as machine learning and AI, and the global digital transformation in full swing, data governance will continue to be an important part of organizations’ digital strategic plans in the future.
So, which processes are most important in the data governance process?
Data standard unification
To formulate data standards, unify data caliber and determine authoritative data to ensure that all business departments and information systems can obtain data with consistent caliber. It is just like turning on the faucet and all the water flowing out is of high quality.
Data department flow
So-called a rolling stone gathers no moss, the data must be like water through the water, water, water, water flowing, circulation, fully Shared between business units, in the enterprise internal and external two-way interaction can play a better data value, even if it is idle, useless data inside enterprises, just let it flow, flow to other department or system, New value is likely to be created.
Focus on data quality
In the context of big data, in order to give full play to the value of data, make the data effectively shared, and get a good use in the analysis and mining of big data, there are higher requirements for data quality. In order to improve the quality of data, it is necessary to check the quality of data on a regular basis, just like regular water quality testing. When quality problems are found, they should be corrected in time and reported to the source for rectification, thus forming a cycle of data quality inspection and improvement.
Data governance doesn’t happen overnight. It’s a long, ongoing process, with no one-punch solution and no quick fix. At present, some domestic data governance platforms can help enterprises realize data governance.
Based on the concept of DAMA, RGE data governance platform integrates nine product modules, such as metadata management, data standard management, data quality management, data integration management, master data management, data exchange management, data asset management, data security management and data life cycle management, which can open up all aspects of data governance.
May refer to government and enterprises in the process of real governance, and combined with their own characteristics, the function of each module in a scope as the breakthrough point, or two step iteration, lands to carry out through income gradually driven governance behavior, farce can treat nine big modules according to the personalized needs of customers, can independence or any combination of use, rapid meet government and enterprises of all kinds of different data management scenarios.