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The Data Quality Audit

An audit can help you discover the source of data quality problems. Hint: It might be the warehouse.

These validity tests are derived from known business policies and formulate the basis for the decision-making within the company. The results are published in reports to management. Each report focuses on a table or tables within the data warehouse, with multiple table reports developed to validate cross-table business rules.

Warehouse tables can be the center of an audit because they provide the foundation for information delivery to your business users. These tables provide the data management assessment team a starting point for examining critical activities in the warehouse process instead of concentrating on the source or feeder systems. Using the warehoused data as a baseline, the emphasis is on the effect, followed by the cause.

The following data quality audit approach is iterative. Using the spiral method, subject matter areas are identified and tasks are defined, enabling the team to set specific milestones and associated deliverables unique to iterations. By keeping the process simple and focusing on key checkpoints, you'll be able to make significant progress.

The six steps of this approach are iteratively conducted for each subject area of your audit (see Figure 1). It is important to note that steps 2 and 3 can be switched. In other words, it is sometimes better to formalize all business rules prior to extracting sample data. This is especially true when dealing with multiple table joins. In the example shown in Figure 1, three subject areas are audited, including Customer, Account, and Transaction. The steps applied to each include:


FIGURE 1 - The six steps of the data quality audit.