How can I maintain the quality of the data warehouse?

There are four primary factors that need to be considered when trying to maintain data quality: data integrity, data source and methodology used, data import frequency and audience. The data warehouse is an electronic storage of large amounts of data and businesses, and other larger organizations are increasingly using it to store data in a tool that makes it easier to report and output data. The usefulness of the data warehouse is driven primarily by data quality and sensitivity to user requirements.

Data integrity is a concept common to the quality of the data warehouse because it applies to the rules regulating relations between data, data, definitions and business rules that form the importance of data for the organization. Maintaining data is a consistent and reconstructed basis of data integrity. The steps used to maintain the quality of the data warehouse must include a cohesive data architecture plan, regular data control and the rules of rules and processes to maintain data consistent whenever possible.

Data input source for data warehouse is usually an import tool or program. The easiest way to maintain the quality of data warehouses is to implement the rules and checkpoints into the data import program itself. Data that does not apply to a suitable formula will not be added to the data warehouse, but will require user intervention to repair, reconcile, or change the program. In many organizations, these types of changes can only be implemented by architect Warehouse architect, which significantly increases data quality.

The accuracy and relevance of data is necessary to maintain data quality. The timing of import and frequency has a great impact on the overall usefulness of the tool and quality. For example, if the order information is entered in the B -activation warehouse T are updated only intermittently, the ability to precisely report on purchasing activities.

Data warehouse quality is the easiest to maintain and support, PTerrible users are knowledgeable and have a solid understanding of business processes. Training of users not only to understand how to create questions, but also on the basic structure of data warehouses, allows them to identify inconsistencies much faster and emphasize potential problems at the beginning of the process. Any changes in data tables, structure, or interconnection and adding new data fields must be reviewed with a whole team of users and support employees to ensure consistent understanding of risks and challenges that could occur.