In order to better supervise and manage information and improve the efficiency of business operations, many enterprises began to invest in data governance projects. Its strategy and process are to produce more accurate and consistent data in the whole enterprise, and DataSteward needs to ensure that it is transformed from theory to practice. To a great extent, the success of data governance strategy depends on the efforts of relevant data experts. To some extent, it is very important to establish a data governance management architecture and operation mode that conforms to the existing architecture of the enterprise. This includes all elements of data management. It sounds simple, but it is difficult to operate in practice.
When a project is going to be launched and a hasty decision may not reach the expected goal, the problem of data governance appears. For example, if a company chooses a data management pool before defining what it should do, it will lead to serious confusion. In addition, those enterprises that want to prove their rapid progress are the biggest headaches for data specialists, because they have to conduct metadata surveys and do a lot of meaningless work.
How to effectively establish and manage a data management team so that it can maintain coordinated governance activities? This article will give seven related suggestions:
Regularization of positions. Ensure that there is a formal division of responsibilities before requiring individuals to be data experts; Determine the skills required for this position; Indicators to measure its performance; If the data specialist is not a special position, you have to finalize the details of how to combine with your existing job.
Fine division of management roles. The data specialist actually includes many roles, such as metadata administrator and operational data administrator. It is best to clearly describe how these roles are distinguished and how employees work together to support the data management process.
Establish business ownership of data. Data experts may be responsible for keeping consistent with data governance policies, but this does not mean that they are responsible for the data itself. Ownership and responsibility must be assigned to the appropriate business unit or department.
Be consistent with the business. As a part of the data governance project, data availability expectations are formulated in the context of expected business improvements, such as increasing revenue, reducing costs, reducing risks and improving productivity. But most IT and data management practitioners are more familiar with data management mechanisms than business processes. If the data specialist is not from the business field itself, there should be experts in key business fields to help them identify data problems and determine the priority of tasks.
Establish a reward mechanism. Unlike typical projects with obvious results to deliver, the essence of data management is to ensure that data accidents can be handled, and the results may not be very intuitive. So establish a reward mechanism for your data specialist to recognize and reward them.
The right people do the right thing. As the role of data experts is still developing, it may be unrealistic to recruit people with years of experience. And in many companies, data management is not a full-time job. Therefore, you may need to recruit people with management potential internally. Consider what data management skills are necessary, and look for employees with valuable and good communication skills, who are confident to seek best practices and can adapt to changing ideas.
Provide appropriate tools for data experts. Although data management is fundamentally a procedural issue, there are still corresponding tools to support it, including data quality assessment, data verification, data event reporting and management software, and even data quality and data management scorecard applications.
All these steps have a common theme: what reasonable efforts should be made before designing a data governance and management project to make it work properly. After completion, it will help to start a sustainable data management process by recruiting the right people, giving them a clear role definition, keeping them in sync with the business department and providing them with performance incentives.