As I explained in the last Excel post, you can check for blank and invalid data in Excel several ways.
In this post, I will focus on the insights and issues encountered by sorting each column from A to Z and then Z to A.
Sounds pretty simple, but I’m willing to bet you will be surprised to learn a thing or two…
For a list of the reasons why you must validate data before analyzing it, see Why You Must Validate Data.
You can check for blank and invalid data in Excel several ways.
Depending on the size of the file and your preferences, you can either scroll through the dropdown list, sort each column from A to Z and then Z to A, or apply a filter.
Sometimes, you need to use a combination of these methods.
It’s important to know how these methods treat data differently and to be aware of their limitations.
Here’s a list of my basic data analytic procedures for Excel.
As I add more posts to the series, I’ll update this list.
I created this series because:
1) I often get asked by new AND EXPERIENCED auditors how to do these tasks,
2) when I review workpapers, I realize too many auditors are not aware of these functions,
Do you perform appropriate population validation of the data you rely on in an audit?
Population validation is simply gaining confidence that the data you are using in your audit contains all the appropriate data for your audit objectives (e.g., your server list includes all the SOX servers).
For the difference between population validation and data validation, see Why You Must Validate Data.
So how do you do population validation? Let’s look at an example…
Filed under Audit, How to...
A colleague of mine is doing some testing for an audit director that changes her mind frequently on how to deal with audit findings. Occasionally, she is all about nailing control owners who do not have all their ducks groomed and in a row. At other times, she pushes Audit to work as hard as possible to pass all controls.