Data governance does everything

stay DAMA  In the data management body of knowledge guide , Data governance lies in data management “ Wheel diagram ” Right in the middle of , It's data architecture 、 Data modeling 、 data storage 、 Data security 、 Data quality 、 Metadata management 、 Master data management, etc 10 The general outline of big data management , Provide overall guidance strategy for various data management activities . It's more important to watch , But the real thing is that data governance does everything , And it's all dirty work , Exhausted , It may also be thankless or “ A good fighter is unknown ” The state of .

Data governance involves enterprise strategy 、 Data standards 、 Data specifications 、 Data culture 、 A complex of related tools . People who have not done data governance or do not understand data governance have heard so many tall terms , I must think that data governance is a relatively big job , What kind of governance will lead to the formation of data assets , And data governance drives organizational change , Change the business model , Drive business change in the enterprise , Even an important foundation for enterprise digital transformation . Including culture 、 strategic 、 The standard , Is still in the DAMA The core of the wheel diagram , It sounds quite tall , But the real data governance work is to do everything , Dirty work 、 Exhausted , Even any pot can be pushed to data governance .

Keep saying that data governance is important as soon as possible , It's the number one project , Senior leaders also attach great importance to it , But in the real implementation process of many enterprises , But there is always a lack of support from senior leaders , The cooperation of business department personnel is not in place , Data governance always has to give way to business and so on . The reason is ： Leaders say they value data , But for business operations , We must see its value before we can continue to invest or invest more persuasively . Data administrators should also have good data thinking , To achieve continuous improvement of enterprise data quality , Grinding out data standards suitable for enterprises .