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 still 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.