Maintaining data warehouse quality requires considering data integrity, input methodology, import frequency, and audience. Data integrity involves consistency and reconciliation, while input sources should have rules and checkpoints. Accuracy and relevance depend on import times and frequency. User training and understanding of business processes can help identify inconsistencies and potential problems.
There are four main factors to consider when trying to maintain data warehouse quality: data integrity, data source and input methodology used, data import frequency, and audience. A data warehouse is an electronic repository of large amounts of data and is increasingly used by businesses and other larger organizations to store data in a tool that facilitates reporting and data output requirements. The utility of a data warehouse is primarily driven by data quality and responsiveness to user requirements.
Data integrity is a concept common to data warehouse quality as it refers to the rules governing the relationships between data, dates, definitions, and business rules that shape the relevance of data to the organization. Keeping data consistent and reconcilable is the foundation of data integrity. The steps used to maintain data warehouse quality should include a consistent data architecture plan, regular inspection of data, and the use of rules and processes to keep data consistent whenever possible.
The data input source for a data warehouse is typically an importer tool or program. The easiest way to maintain data warehouse quality is to implement rules and checkpoints in the data import program itself. Data that does not follow the appropriate pattern will not be added to the data warehouse but will require user intervention to correct, reconcile or program changes. In many organizations, these types of changes can only be implemented by the data warehouse architect, which greatly increases the quality of the data warehouse.
Data accuracy and relevance is essential to maintain data warehouse quality. Import times and frequency have a large impact on the overall usefulness of the tool, as well as quality. For example, if purchase order information is entered into inventory but invoices are updated only intermittently, the ability to accurately report purchase-related activity is compromised.
A quality data warehouse is easier to maintain and support if users are informed and have a solid understanding of business processes. Training users to understand not only how to build queries, but about the underlying data warehouse structure allows them to identify inconsistencies much faster and highlight potential problems earlier in the process. Any changes to data tables, structure or linkages, and the addition of new data fields should be reviewed with the entire user team and support staff members to ensure a consistent understanding of the risks and challenges facing could occur.
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