Managing Data Quality in the Face of Organizational Change
By David Loshin, president, Knowledge Integrity, Inc.

Although maintaining a competitive edge requires forethought by senior management, many organizations are plagued by the absence of strategic objectives that should be intended to drive innovation and excellence in the marketplace. Organizations that engage in defining a vision and planning a strategy, are more likely to be focused on achieving well-defined objectives. In the context of a defined strategy, though, senior managers may take steps to adjust the way the organization works, often changing people’s roles, moving them from one division to another, or even letting people go.

The data quality practitioner should then plan for the contingency that there will be changes to the organization that may have unwanted impacts on a data quality program. Despite the best laid plans, the success of certain activities are likely to be bound to the level of personal investment the tasked individuals have in making data quality work. But organizations, like people, do not remain static. Rather, they change, sometimes slowly, and other times, rapidly. So, if the intention of instituting data quality is to establish an ongoing program, the policies, processes, and procedures must be able to persist even in the face of change.

Here are some examples of organizational change that have ramifications in association with the data quality program:

Reorganization and downsizing: As part of our approach to data quality management, we suggest the introduction of clearly defined roles mapped to specific individuals within an organization. Those individuals are trained in performing the tasks associated with those roles, and are expected to fulfill a set of responsibilities in order to continually and proactively manage the assurance of high quality data. However, reorganizations and organizational downsizing can have radical impacts on the stability of a data quality initiative, especially as key senior sponsors are moved to new and different roles, or when data quality staff members are reassigned or downsized. For a data quality program to survive reorganizations, the roles, responsibilities, policies, and processes must be properly documented in a way that is dissociated from the individuals implementing the program. The resulting “run-books” for data quality management can be used to bridge any staffing gaps or changes.

Outsourcing: Outsourcing is intended to reduce total costs of operations through the ability to modulate staff hiring, reduce labor costs by paying for temporary or short-term development, control capital acquisition costs, and generally reduce some of the risks related to starting up new development. However, any time that one introduces additional layers of control within a system development process, or assignation of an operational process (such as managing a call center), the challenges of miscommunication are amplified. This is especially true in translation of what is expected to be commonly used business terms, reference data domains, and exchanged data elements. When considering outsourcing, one must integrate the data quality team to ensure that data exchanges are controlled within the expectations of the consuming business processes.

Offshoring: Offshoring is basically outsourcing where the outsourced business activities or processes are performed in another country. Regulations, privacy rules, and, most importantly, the cultural and language barriers that exist in other countries will detract from utilizing common business terms and definitions, leading to confusion and potential inconsistencies.

IT management changes: Whether the changes are taking place at the senior level, or incremental replacements at other places along the organizational spectrum, there will always be some degree of instability when there are staff changes in the information technology department. Because the IT staff embodies the technical aspects of the best practices for data management, IT management changes will, by necessity, impact the continuity of the data quality program.

In each of these instances, the challenges associated with changes to organizational staff, either through reorganization, downsizing, or allocation of responsibilities to external staff, will impact the continuity of the data quality program. Therefore, when implementing the program, make sure that you accommodate for the possibilities of how staff turnover and organizational changes will affect the program.

How is this done? Some suggestions include:

• Making sure there is a clear distinction between the roles and the individuals who are assigned those roles;
• Developing a concrete program plan with discrete tasks;
• Defining processes and procedures, and carefully documenting those procedures so that the knowledge can be easily transferred;
• Developing training material aimed at different levels so that new team members can come up to speed rapidly; and,
• Identifying more than one key senior sponsor who will remain as stakeholders in case of reorganizations.