A Process for Data Quality
There is no one-size-fits-all model for data quality. When creating one, it is necessary to take into consideration a company's culture, the MDM approach being implemented, how multiple LOBs interact with each other, the maturity level of the data governance and stewardship teams, the degree of management engagement and sponsorship, technology resources, and personnel skills.
Figure 6.1 depicts the major roles involved in a data quality process and how they interact with one another to create a flexible and effective model to address data quality issues. The arrows represent events or dependencies, while the numbers represent the sequence of activities.
A description of each of the elements presented in Figure 6.1 is provided next.
Drivers are essentially the initiators of a data quality activity and the means by which they bring data quality issues to proper attention. A company with a mature data quality practice should be able to support a multitude of drivers. Not only that, it should also demand that everyone across the company participate in improving the overall quality of the data. After all, data quality is everyone's responsibility.
Continuous training, both formal and informal, is essential to achieve everyone's participation and strengthen a culture of focus on data quality. Actually, several studies ...