3 frequently asked questions about audit analytics

Leveraging these tests is also important. For example, is there a need to look for duplicates, is there value to pulling outliers, etc.?  Although the data and the overall parameters will be different, the fundamentals of these tests are the same across each audit.

How best to use audit analytics to validate (or challenge) initial assumptions?

It’s not uncommon for the audit team or auditee (or both) to have formed an opinion based on preliminary work or information, with an assumption that was challenged by the data analysis work.

Data analytics supports an auditor’s natural curiosity and review. When done correctly and consistently, it often reveals new questions or paths of inquiry. This is precisely the benefit of putting analytics tools into the hands of all auditors. This is the best way to uncover hidden risks and deliver value to the organization.

Furthermore, this underscores the value of being agile. Agile auditing provides audit teams the flexibility to dig deeper into areas where preliminary or related information raises concerns or additional questions.

Are there basic analytics tests that can be used to help auditors gain confidence when starting an analytics program?

With any data set you receive, checks for completeness and accuracy should always be performed. This includes confirming data is formatted correctly (a numeric field is numeric for all records, confirming data is within sensible or required ranges, etc.). For example, if you asked for all data for a given month, check that all dates are within that range, confirm that you received the correct number of records, and that the amounts cross check to another source.

Always look at graphical representations of the data. This is a good way to initially become familiar with the data and look for unexpected data patterns. A TeamMate Analytics customer did this with their data and immediately recognized many instances of a transaction type that neither she nor the auditee expected.

It’s common to manipulate data for either analysis or reporting purposes. Having a robust toolset that is easy to use can save hours of time. In fact, TeamMate Analytics customers have told us that using our tools to manipulate data have saved them many hours per month.

Keep in mind, the above three questions are preliminary to the process of digging in and doing the data analysis auditors should be performing on a regular basis. The most common tests we see internal auditors conducting are joining data sets together, looking for duplicates and summarizing data. We also see an emphasis on sampling, but this is often done on data that has been reduced based on other tests or on tests that have been summarized.