The Change Management Challenge
,Because change can often be very disruptive and is usually expected to be completed under a specific plan with a fixed time frame, the quality and execution of a data migration effort can suffer due to the time constraints. Customer data that has been poorly translated and mapped into new models can cause significant issues down the road with operational processes and customer transactions. In many cases, this could have been avoided with early engagement of an existing data governance or data steward team in the change management plans and expectations.
During organizational and operational change, subject matter experts on the business side who have been intimate with the business processes and context of the data, are often transitioned quickly into new organizational structures and environments, or possibly even let go, while IT has the responsibility to transition the systems and data without much context to the data and prior usage. This often results in poorly migrated and mapped data that can degrade overall data quality and create many operational issues until this can be corrected at a later time.
Data Governance Can Greatly Assist a Transitioning State
In Chapter 4, we mentioned that when positioned correctly, a data governance function can nicely fill a data management authority void and serve a very valuable role with an existing IT decision-making process and design methodology (see Figure 4.2). This also holds true with other forms of change. Mergers, acquisitions, and internal operational or organizational changes will usually involve various combinations of system, process, data, and resource changes, usually as part of integration or migration activities. A well-established Customer MDM and data governance model can greatly assist in these processes.
Existing policies and standards, metadata, work instructions, user information, and of course, knowing how clean and consistent the master data is, are all extremely important factors in a successful transition or transformation of customer data. A lack of accurate or complete information and poor data quality will present time-consuming and costly problems that can significantly impede a transition process. For example, in Chapter 6 we indicated that the usage and perceived quality of customer data is very context dependent, and this can vary across the business functions. A good data governance team should be well aware of this and able to recognize where any internal change or transition of this data can have widespread operational impact or other risk. In the case of a merger or acquisition, the mapping and realignment of this data needs to be carefully planned and executed so that the value of the data from the acquired company can be fully leveraged.
Where a data governance team has a good handle on the usage, quality, and context of its data, this will inherently create a more proactive and conducive state for change management. And because there are more common industry standards, tools, practices, reference data, and universal identifiers associated with customer data than with other data domains, a mature MDM model and data governance process will typically already be leveraging many of these common factors and solutions. Therefore, it makes perfect sense to engage an existing data governance team in a transition process so that these tools and common factors can still be leveraged and help enable the transition. Losing too much of the governance team's knowledge and data steward expertise too early in the transition process can be a critical mistake that can create unnecessary challenges with the execution of the transition plan.
Leveraging the Data Stewards and Analysts
We just mentioned that to assist change there is the benefit of and opportunity to leverage existing knowledge and resources within an existing MDM model and data governance team. Here are some specific examples of how data stewards and analysts can be leveraged for various scenarios:
The overarching message here is that it's very hard to recover discipline of your master data once it has been lost, so be mindful of the impact organizational change can have on an MDM practice, and be prudent in handling this to minimize negative impact on the data integrity and management.
Adopting Best Practices
MDM needs to build on itself. Organizational or operational changes that cause regression to MDM practices will only be a data management ticket to nowhere. MDM and quality control can quickly lose its grip and momentum where best practices and tools associated with data governance, stewardship, and quality management are left behind in the transition to a new environment.
At the risk of being overly repetitive, MDM practices must be ongoing. A lot has been said about not approaching it as a one-time project. Moreover, it requires maturity combined with a constant effort to remain current to be able to sustain it at an optimal level. It is still very much an evolving discipline, and the best way to remain efficient is to continuously adopt existing best practices and be on the lookout for new ones.
The encompassing nature of MDM impacts people, process, and technology; plus, it requires an even stronger collaboration between IT and business than ever before. A strong MDM program in itself can trigger some organizational and operational changes. Once the initial impact is resolved and solid MDM practices take root, staying atop of the best practices will help the management, orchestration, and synchronization of the many changes affecting pre-established dynamics.