Five tips…#4. Consider metric, outlier, and exception queries

For readers seeing this post as their first of the series, today is actually the fourth of a five-part blog that has been developed in response to Internal Auditor magazine’s lead article titled “The Year Ahead: 2015“. Because so many people make resolutions for the new year, we wanted to help audit and compliance professionals succeed with their resolutions.  Especially because we believe there are more than a few whose resolutions include becoming more data-driven in their work through regular use with data analytics.

Yesterday we defined metric, outlier, and exception queries, and provided examples in the context of related potential audit projects around expenses such as Accounts Payable, Travel and Entertainment, or Payroll. To review, metric queries are simply lists of transactions that measure values against various dimensions or strata, such as rank or time series. Top 10 largest or simply transactions by day of week are examples of metric queries.  These metric queries are powerful, and can become even more powerful when combined as part of outlier and exception analysis.

One recent Travel and Expense example from our client work was seeing a number of executive assistants in the “Top 10 Travel Spend reports.” Even before we looked at any exception report it became clear that some of the organization’s executives had their assistants complete and submit their personal expense reports, and then approved those reports themselves.

Outlier queries are those that compare value to other values like a mean or standard deviation. As an example, saying that today is twenty degrees colder than average or the coldest day of winter is more informative than saying that it will be sixteen degrees tomorrow than yesterday. Better still, listing the 10 coldest days together in relation to average and standard deviation is even more informative.

We recommend diving into exception queries only after metric and outlier queries have been prepared, explored and analyzed. It’s common for false positives to be averted through thoughtful review of metric and outlier queries.

How does this compare to your experiences? 

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