One of many discussions I heard over Thanksgiving turkey was, “How could the government have let the financial crisis happen?” To which the most frequent response was that regulators were asleep at the wheel. True or not, one could legitimately ask why we have problems with our business intelligence reports. The data is bad and the report is meaningless—who’s asleep at the wheel?
Everyone’s talking about the single version of the truth, but how often are our reports reviewed for accuracy? Several of our financial services clients demand that their BI reports are audited back to the source systems and that numbers are reconciled.
Unfortunately, this isn’t common practice across industries. When we work with new clients we ask about data reconciliation, but most of our new clients don’t have the methods or processes in place. It makes me wonder how engaged business users are in establishing audit and reconciliation rules for their BI capabilities.
No, data perfection isn’t practical. But we should be able to guard against lost data and protect our users from formulas and equations that change. All too often these issues are thrown into the “post development” …
One of many discussions I heard over Thanksgiving turkey was, “How could the government have let the financial crisis happen?” To which the most frequent response was that regulators were asleep at the wheel. True or not, one could legitimately ask why we have problems with our business intelligence reports. The data is bad and the report is meaningless—who’s asleep at the wheel?
Everyone’s talking about the single version of the truth, but how often are our reports reviewed for accuracy? Several of our financial services clients demand that their BI reports are audited back to the source systems and that numbers are reconciled.
Unfortunately, this isn’t common practice across industries. When we work with new clients we ask about data reconciliation, but most of our new clients don’t have the methods or processes in place. It makes me wonder how engaged business users are in establishing audit and reconciliation rules for their BI capabilities.
No, data perfection isn’t practical. But we should be able to guard against lost data and protect our users from formulas and equations that change. All too often these issues are thrown into the “post development” bucket or relegated to User Acceptance. By then reports aren’t always corrected and data isn’t always fixed.
A robust development process should ensure that data accuracy should be established and measured throughout development. This means that design reviews are necessary before, during, and after development. Design reviews ensure that the data is continually being processed accurately. Many believe that it’s ten or more times more expensive to fix broken code (or data) after development than it is during development. And, as we’ve all seen, often the data doesn’t get fixed at all.
When you’re building a report or delivering data, ask two questions: 1) whether the numbers reflect business expectations, and 2) if they reconcile back to their system of origin. Design review processes should be instituted (or, in many cases, re-instituted) to ensure functional accuracy long before the user ever sees the data on her desktop.