The term Data Analytics is a generic term that means quite obviously, the analysis of data. Hence the term gets used within the world of auditing in many ways. At TeamMate we refer to data analytics, or Audit Analytics, to mean the analysis of data related to the audit. After all, the analysis of the business processes that we audit is the core of what audit does.
Audit Analytics, as I’ve defined it, really should be a core component of any audit methodology. One thing I’ve noticed from living through this pandemic is that people want to have data to support their opinions. We can get counts of infections and unfortunately deaths. We can then further analyze the data to look at it from a myriad of demographics including location, age, race, sex, other health factors, and other ways. When we can show how data supports our opinion, we then feel justified in our opinion.
Similarly, data provides justifiable support for our audit findings. Our findings are so much stronger when we can say that we looked at 100% of the data and found X, Y, and Z. This is so much stronger than sampling, which is why we generally don’t point out in our reports that we sampled, and certainly stronger than other work such as interviewing alone.
However, the challenge audit teams face is that they have been led to believe for many years that the ONLY way to perform Audit Analytics is through individuals with specialized data analysis skills and tools that require strong technical skills. Organizations with this thinking tend to be able to do very deep analysis, but they lack capacity so they can’t go very broad, resulting in most audits going without any data analytics at all.
