Risk management entails monitoring a large number of reports, looking for iterative variations and explaining these. Additionally, during investment on the underlying platform, changes impact must be managed, with the same process of looking at differences of indicators before and after the changes.

Unfortunately, in both cases risk controllers and testers spend too much time manually comparing data, distracting them from investigating and understanding the variations, with usually:

• Poor quality results
• Time to production pressure, particularly for “end-of” processes
• Continual compromise between time and number of reports monitored

• Business data archiving for auditing or knowledge base
• Dependency on key personnel
• High operational risk (human error)