This session will include a moderated Q&A featuring questions from the live audience.
Business users develop trust in data through use and continuous validation of results. Continuous, business-driven validation is based on an understanding of totals combined with specific, deep knowledge. During this user-driven validation process, there is a real and ongoing risk that the business users will lose trust in the data. Loss of trust may be triggered by finding outliers in the data or by identification of actual errors. This loss of trust is extremely detrimental to the ongoing success of an intelligence-driven project.
In order to maintain trust, there is a real need to build automated data validation into the ongoing data processes. During this session, Andrew Cardno will introduce five best practices for implementing continuous data validation that will enable business users to maintain real trust in the data while accepting the outlier data occurrences as a natural part of the business intelligence process.