Here are some hints and tips for trying to minimise time wasted on false positives, which are not really errors, and inconsistent output caused by incorrect data analysis.
We are running a series of blogs on what the most common issues are around data analytics for auditors, so from a lack of knowledge/direction and strategy through to false positives and inconsistent output along with how to handle missing data sets and fields, so, without further ado, here’s blog number 3 on False Positives and Inconsistent Output
Tip 1: Act on information
Have feedback loops – if results included the first-time around are caused by acceptable, but variable, business processes, consider redesigning the analysis to exclude these false positives.
Tip 2: Quality assess the analysis
Ensure the data analytic includes steps to ensure all data is processed and correctly so it is fit for purpose. Make sure this happens prior to going live.
Tip 3: Is there a recurring theme?
Standards will improve consistency and increase efficiency and performance. Use style guides, have coding standards, naming conventions and project methodologies. Not a ‘one size fits all’ approach but with the key principles.
Tip 4: Set the culture from the ‘top’ and lead by example
Challenge yourself and others. If false positives and inconsistent output is not something that you want to see, challenge yourself and others to do something about it and change the mindset.
Tip 5: Communication and training
Encourage shared learning and enforce standards to go some way to help deliver a consistent message and working style within the business.
Author: David Ryan
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