Data Quality
There is and must be only one purpose for improving data quality: to improve customer and stakeholder satisfaction by increasing the efficiency and effectiveness of the business processes. Data quality is a business issue, and data quality improvement is a business necessity. For organizations in the public and not-for-profit sectors, data quality is a matter of survival, and then of stewardship of stakeholder (taxpayer or contributor) resources.
There are two significant definitions of data quality. One is its inherent quality, and the other is its pragmatic quality. Data that does not help enable the enterprise accomplish its mission has no quality, no matter how accurate it is.
- Inherent data quality is, simply stated, data accuracy. It is the degree to which data accurately reflects the real-world object that the data represents. All data is an abstraction or a representation of something real.
- Data is an equivalent reproduction of something real. If all facts that an organization needs to know about an entity are accurate, that data has inherent quality - it is an electronic reproduction of reality. Inherent data quality means that data is correct.
- Pragmatic data quality is the degree of usefulness and value data has to support the enterprise processes that enable accomplishing enterprise objectives. In essence, pragmatic data quality is the degree of customer satisfaction derived by the knowledge workers who use it to do their jobs.
- Data in a database or data warehouse has no actual value; it only has potential value. Data has realized value only when someone uses it to do something. Pragmatic data quality is the degree to which data enables knowledge workers to meet enterprise objectives efficiently and effectively.