Data Quality Policy

Many organizations have started the journey but have not been able to sustain the effort and keep the data quality activities going. Too often the activities are executed by a single champion or a single department that recognizes the benefits that can be obtained from such activities. The result is that the process is not institutionalized, and inertia within the rest of the organization leads either to a slow decay or an abrupt halt to all data quality initiatives.
We firmly believe that a key to success in the data quality journey is the creation of appropriate organization policies that involve all functions and activities related to the maintenance of the data product.
A complete policy has different levels of details ranging from broad guidelines and principles to more detailed elaboration and specification of these principles. It addresses both data quality practice, such as management issues, implementation, and operational issues, and standards, and the quality of the data product itself.
We provide ten broad policy guidelines that should form the basis of any organizational data quality policy

Ten Policy Guidelines:

1. The organization adopts the basic principle of treating information as product, not by-product.
2. The organization establishes and keeps data quality as a part of the business agenda.
3. The organization ensures that the data quality policy and procedures are aligned with its business strategy, business policy, and business processes.
4. The organization establishes, clearly defined data quality roles and responsibilities as part of its organization structure.
5. The organization ensures that the data architecture is aligned with its enterprise architecture.
6. The organization takes a proactive approach in managing changing data needs.
7. The organization has practical data standards in place.
8. The organization plans for and implements pragmatic methods to identify and solve data quality problems, and has in place a means to periodically review its data quality and data quality environment.
9. The organization fosters an environment conducive to learning and innovating with respect to data quality activities.
10. The organization establishes a mechanism to resolve disputes and conflicts among different stakeholders.

....to be continued