Data quality strategy defines the goals, objectives, and plans for improving data integrity. It is the blueprint used to inculcate a perspective of shared responsibility
for the quality of data. The data quality strategy should address the following items: meaning; data store design (e.g., referential integrity, normalization, cardinality, hierarchy management, optionality constraints); and business process. All three components need to be addressed, and in addition, the data quality strategy needs to be aligned with the target data architecture. Another goal of a complete data quality strategy should be to address and reduce ROT (redundant, obsolete, and trivial) information in the data.
The data quality strategy is created based on an analysis of existing major quality issues and business objectives for trusted data. Data quality objectives cannot be met solely by technologies and techniques alone. High quality is the result of continued scrutiny shared and communicated across the organization by all stakeholders.
An implementable data quality strategy may require a cultural shift, obtained by strong support of executive management and sustained promoting, educating, and mandating attention to the data assets.
To successively achieve a data quality culture, the organization must develop a comprehensive measurable strategy appliable across all business units, business processes, and applications. The adoption of a data quality strategy enables stake- holders to understand the correspondence between organizational objectives, such as enhanced analytics, more accurate risk management, and improved operations.
When defining the data quality strategy, organizations should include guidance for the criteria against which data quality will be measured. It is recommended that criteria be defined for each of the dimensions of quality. A number of di erent dimensions of quality can be measured. A sample set is presented below:
- Accuracy – criteria related to a nity with original intent, veracity as compared to an authoritative source, and measurement precision
- Completeness –criteria related to the availability of required data attributes
- Coverage – criteria related to the availability of required data records
- Conformity – criteria related to alignment of content with required standards
- Consistency – criteria related to compliance with required patterns and uniformity rules
- Duplication – criteria related to redundancy of records or attributes
- Integrity – criteria related to the accuracy of data relationships (parent and child linkage)
- Timeliness – criteria related to the currency of content and availability to be used when needed