Abandon ACL and Others, Part 2

This post is in response to Xavier and Grant, who were kind enough to push back a bit on a previous post, Abandon ACL and Others? See their comments on that post.

I will respond to some of their points and reveal some more of my thinking as to why I believe that auditors need to become a LOT more technical.

Some may think I am just digging my hole a little deeper, but I’ve always loved the journey.

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Abandon ACL and Others?

For the past few years, I’ve been outspoken about auditors that 1) don’t do much data analysis, OR 2) rely only on tools like ACL, IDEA, Arbutus, and the like to do their data analysis.

In this post, I’m going to provide some reasons auditors should not rely on only on these tools. I’ve dealt with this before, but I want to look at it from some different angles.

In this post, I’m speaking only to auditors, as they alone are called to audit the technology and processes their companies use.

In this post, when I mention ‘ACL+’, I am referring to ACL, IDEA, Arbutus, and any other tool that typically only auditors use. I’m also including ACL ‘Robotics’ in this list.

In this post, I’m going to step on people’s toes, but my readers should be used to that by now.

My Python Journey, Part 3

In my first Python post, I described the first steps of my python journey.

In my second Python post, I shared my thoughts about whether auditors could learn programming and Python (yes).

In this third post of the series, I want to describe how my audit management has supported my Python journey (spoiler: poorly).

Create a Help Desk for Data

Companies need to create a help desk for data, similar to the help desk they created for hardware, software, application, network, and user problems.

Can you imagine if companies didn’t have a computer help desk and each department had figure out their own computer issues? If each department had to find, load, configure, and troubleshoot their own hardware and software?

But isn’t that how most companies operate when it comes to data and data projects?

IIA Analytics Article Dead Wrong

A recent IIA article on building an analytics function in internal audit is dead wrong.

At least on one major point, anyway. And it’s a big one.

As the tombstone reads, this point is D.O.A (dead on arrival, or more specifically, dead on analytics).

The article, Building a data analytics program, requires IIA membership to view, and is located at https://iaonline.theiia.org/2017/Pages/Building-a-Data-Analytics-Program.aspx (that’s actually good, as it means a lot fewer people will ever read it).

Which Way is Analytic North?

To create a successful analytics program in internal audit, you must have a plan. A plan that points to analytic North.

That requires WRITTEN goals.

In an earlier post I outlined 10 Signs Mgmt Doesn’t Really Support Analytics.

One of the signs that indicates management isn’t really serious about analytics is that management does not require every staff member to have measurable analytic goals.

10+ Signs Mgmt Doesn’t Really Support Analytics

Your management says it wants more analytics, but does it really support analytics? Here’s 10+ signs that indicate that your mgmt:

• Does NOT knows what it takes to get analytics off the ground
• Believes that analytics multiply like rabbits, naturally
• Is NOT willing to make the adjustments required to deliver and sustain real value.