Similar to having committed people, there need to be processes and channels available that are geared to the recognition and support of data management or quality issues. In the previous topics, we have given a number of examples of data management issues where data stewards have been involved, and so far we have talked about how CDG, the data quality forum, and the incident management processes need to involve the data steward role. But let's discuss what triggers the engagement of these data stewards.
This actually goes back to the point we made in Chapter 4, where ideally, data governance involvement should be considered in the design and implementation of systems and business processes. When engaged in the design process, data stewards' roles and engagement points can be formally identified in the process model and flow. But since many systems and processes will already exist before data governance practices emerge in a company, getting data stewards plugged into IT and business processes will usually require the MDM team and governance council to actively pursue how and where to get this type of data steward engagement into place. Here are some examples of where process area data stewards can get plugged in to existing processes to better establish data management support and to begin demonstrating the data steward value proposition:
- Incident management and service request processes. Set up specific customer data management and data quality labeled queues or service request paths associated with system or process areas where the data stewards can be formally engaged in front-line or back-line support process with expectations to monitor and respond to the issues or requests.
- Change management processes. Often resulting from bugs or enhancement requests reported through the incident management or service request process, there will typically be an IT-driven change management process that controls release of fixes, enhancements, or upgrades to systems and applications. Most of these change management processes will require a certain amount of business involvement with regard to justification, testing, process verification, or sign-off that the fix or enhancement is working correctly. Where change management actions can affect specific process and data areas, data stewards should be engaged with responsibilities to assist in the review, test, verification, or sign-off where appropriate. This will ensure that the data management and quality impact aspect of change management is not overlooked.
- Data access management process. We'll cover this more in the next chapter, but this is another important process area where data stewards can work closely with the data access gatekeeper to assist with monitoring, verification, approving, or auditing of the user data access process. This type of involvement will give the data steward a clear sense of who in the process area has access to the customer master data, which is key to the successful management and control of master data.
- Governance and quality management processes. In the prior chapters, we have pointed out how data stewards are integral to the data governance and quality management process. And similar to what we have previously mentioned in the other process examples, there needs to be formal interactions and responsibilities in which the process area data stewards are engaged. This can cover analysis, planning, testing, verification, readiness, support, or other types of tasks. All of these types of tasks should be expected in the execution of governance and quality management initiatives; therefore, you should ensure that these types of expectations are properly set in the data steward job role.
These are just some process engagement examples where data stewards can add value. Every company will have its own flavors of these processes, but check closely to see how well these processes are actually distinguishing and supporting the data management area. If these processes are very system- and application-centric and act as a pipeline primarily for IT support, then most likely the concept of data stewardship and focus on data management has not been well established in the company.