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:

• Migration to a new environment. Often enough, knowledge about data and processes surrounding existing systems is spread among multiple individuals and/or organizations. The new environment also presents its challenges, with still quite a bit of unanswered questions and how it will be possible to move into a new structure and accompanying processes and practices. Data stewards and analysts become critical pieces to understand existing and new structures, profile existing data, uncover potential issues, and assure a smooth migration.
• Business process automation or reengineering. Where there is transition to new environments, such as the result of a merger or acquisition or because of internal system changes, the reorientation of data and associated processes may require or provide a great opportunity for process automation or reengineering. Or, it's not unusual for an acquired company to have better processes and practices in some areas than the acquiring company. In either case, existing data stewards and analysts are usually well versed on these data and process areas—often just waiting for an opportunity to further improve a process—and can be an invaluable asset with opportunities for process automation or with repositioning a best practice.
• Organizational change. Internal organizational changes can often result in rather cursory chops at who and what goes where. Cross-functional teams and processes, such as a Customer MDM team and the data governance process, don't always fit well into a new structure and reorganization plan. In some cases, a new organization may not be very interested in supporting the MDM function, its budget, and the facilitation of cross-functional issues. A new organization may even let a Customer MDM function die on the vine if the transition and acceptance of the function has not been well orchestrated. To avoid this scenario, the data governance council should be engaged early in the plan for organizational change to ensure that an MDM practice and its merits are well recognized and will continue to receive proper commitment and support after the reorganization.
• Outsourcing. Similar to what we have just mentioned about organizational change, outsourcing of IT or business functions such as a call center can suddenly cause disconnection between the Customer MDM practice and that function. The negotiated contract terms with the outsource vendor may not have considered and covered certain MDM-related support expectations or services that had existed previously. Support or services related to data entry, management, standards, analysis, cleanup, monitoring, and so on, can significantly change. Again, there should be a strong data governance presence when considering an outsourcing plan and how to mitigate potential disruption to the MDM practice.

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.