Data Quality Factors

So much has been said in previous chapters about establishing a culture focused on data quality. In an ever-changing business environment, applications come and go, and even though data is more permanent, the surrounding business practices and data requirements are also changing. Therefore, you can't expect sporadic data quality projects to properly address persistent issues and changing conditions. Good data is needed all the time, but data quality management projects can be very resource intensive and time-consuming if not orchestrated on a more regular basis. Therefore, the best approach is to foster the disciplines of data quality on an ongoing basis and throughout the entire enterprise. Data quality is everybody's responsibility, but it is a company's duty to create a favorable environment and an ongoing focus with appropriate technology, effective processes, proper skills, and a mechanism to recognize and reward quality efforts.

Chapters 6 and 8 articulate the many dimensions of data quality and how they can be used to qualify data quality issues, as well as structure a strong data metric program. But the actual enforcement of quality of information according to a given set of dimensions can render data more adaptable. Let's take a look at some of the basic data quality dimensions and explore this concept further.

Data Completeness

Completeness of information typically serves two primary purposes:

1. Better knowledge. More attributes populated translate ...

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