Quit Sampling!

This post may disturb some “old school” auditors.  In fact, used to be a self described old school auditor.  If I couldn’t find a work paper in the permanent file, someone was going to get an earful about their ability to keep reliable documentation.  But the business sector has evolved at a frightening rate since those days.  Surprisingly, many audit professionals t-teststill consider sampling and testing to be their go-to procedure.

I’m not knocking the old student’s t-test.  It’s still the right tool for some situations.  But it’s been harder and harder for me to find those situations in recent years.  Most of the subjects I audit now can easily be scrutinized using data mining, or even scripted into an automated monitoring report.  Why look at a random sample of 30 transactions when I could look at 100% instead?  Those hanging onto sampling may not have a choice given the prevalence of data analytics and big data.

In the past 2 years I’ve seen “analytic auditor” job postings appear and become common, which is why I started this blog.  Managers rely on analytics more every day to make strategic decisions about their organizations.  Good luck checking the accuracy of a correlation or a clustering analysis using samples.  Good luck deciphering the source of data has been extracted from the source database, transformed into a new format so it can be loaded into a visualization where it is presented in a board room or embedded into a financial report.  We auditors need to evolve quickly or there could be another Enron-type blunder in the near future because we didn’t fully understand.