Before you start analyzing data, you need to 1) know you have the right data, and 2) understand the data and the process that produced it.
This post assumes, of course, that you already accomplished some of the hardest tasks already: figuring out what data you need, where to get it, and actually getting the data. Good luck with that. :)
This post is part of the Excel: Basic Data Analytic series.
Before you analyze data, you should profile it.
Otherwise, your analysis may not be too broad, too narrow, or you may miss some important insights or errors.
This post is part of the Excel: Basic Data Analytic series.
Data profiling is developing a profile of your data, just as facial profiles of a person, taken from various angles, helps you size up a person’s nose, identify whether his chin is sagging, and how far apart the person’s eyes are.
It’s official: ACL is changing its name AND its spots.
I’ve claimed several times that ACL has left its first love (analytics) and doesn’t put enough work into their flagship product, ACL Analytics.
Correction: their FORMER flagship product.
At least they are publicly admitting it finally–they NO LONGER are an ANALYTICS company!
Rumors have it that ACL will no longer be available on the desktop (laptop, or other local machine) in 5 years.
That is, according to an ACL user who attended the 2018 ACL Connections conference.
To increase the amount and depth of the analytics performed, steal some agile methods, and apply them to your audits.
If you’re not familiar with agile methods, check out the first 5 topics listed here (just click Next at the bottom of each page; the topics are quick to the point and full of pictures).
Briefly, agile projects are performed in cycles, or iterations, rather than in a long, linear-waterfall fashion, which is: do all planning, then field work, then reporting. Each iteration of the project creates some value and includes feedback, which is used in the next iteration to increase the value of the project.
A while back, a reader named Kyle and I had a conversation about analytics.
It started with his reading my Excel:Basic Data Analytics post where I list a number of procedures that anyone can do in Excel.
Kyle said he was expecting some “super sophisticated process & methodology that works like magic.”
In the previous post, Create a Team for Audit Analytics? Part 2, I explored the pros and cons of expecting all auditors to develop a level of data and analytic proficiency.
These auditors would continue to do audit testing that involves analytics as well as testing that does not involve analytics. In addition to keeping up their business skills, they would be learning and upgrading their data analytic skills.
In the first post of this series, I reviewed some of the pluses and minuses of creating a dedicated analytics team.
However, a third option exists, which is sort of a hybrid between having dedicated analytic auditors doing all the analytic work and requiring everyone to increase and develop their data and analytic skills.
Let’s explore the hybrid method in this post, and wrap up the series with a few final thoughts.
This is the third post of a 3-part series…
In the previous post, Create a Team for Audit Analytics? Part 1, I explored the pros and cons of developing an analytics team.
This team consists of analytic auditors who are dedicated to analytic projects; they would NOT typically manage audits or testing that did not include analytics.
In this post, let’s explore another option for managing and growing analytics in an audit department — expecting all auditors to develop a level of data and analytic proficiency.
This is the second post of a 3-part series…
Once your audit team has proven the value of doing analytics consistently, the next question is: Do we create an analytics team and have the team do all (or the majority) of the analytics?
Or should we expect all auditors to develop some levels of analytics proficiency?
Of course, this question often comes a bit further down the trail on the analytics journey, but I think the sooner it is decided, the better.
This is the first post of a 3-part series…
Here’s the 5 things I’m hoping will change in 2018 regarding ACL.
They are all related to each other and feed off each other…
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).
Some Chief Audit Executives (CAEs) and audit managers tend to think that audit automation is a set-it-and-forget-it process. NOT.
In this post, I want to expand on a problem I mentioned in an earlier post , 10 Signs Mgmt Doesn’t Really Support Analytics.
Audit management too often thinks that once a process or an audit is automated, ALL auditor/staff hours previously spent performing that process can be reassigned elsewhere.
That is not the case at all.
If your department doesn’t track metrics on your analytics, you are probably not doing analytics or you are making little progress in analytics.
In either case, its obvious that analytics isn’t very important to your management.
Which is one of the points I made in my post, 10 Signs Mgmt Doesn’t Really Support Analytics.
So far, I have encountered very few audit departments that track meaningful metrics about their analytics.
Counting the number of projects that include analytics isn’t enough.
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.
Recently, a reader named Porak asked me what careers IT auditors can move to when they leave auditing (see the original question here).
I couldn’t find much on the Internet on this topic, but there’s a lot of options.
I’ve actually worked in quite a few of the areas mentioned below…
If you’re an auditor and you are not yet using Excel PowerPivot, you are missing the next greatest thing since spreadsheets arrived.
If you are NOT an auditor, and you don’t use PowerPivot, you’re in the same boat with the auditors mentioned above, and it is sinking.
In other words, if you use Excel, you should be learning Excel PowerPivot. It’s that big.
Let me explain why.
NOTE: I updated this post quite a bit with new info…
Since some of you are newer to the blog, I thought I’d bring a couple of my favorite posts to your attention.
If you like Dilbert cartoons or big data, you might enjoy Dilbert’s adventures in data analysis, data mining, data privacy, security, and dealing with a dumb manager.
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.
Do you know the #1 reason auditors don’t do data analytics (DA) much?
It is so simple, so obvious, I hesitated to blog about it. Let me know if you agree.
Here’s my list of the top 10 reasons to be an IT auditor:
10. You have access to all systems, data, and people (with a business reason, of course). Employees rarely ignore you.
9. You can uncover fraud, mischief, ignorance, and just plain laziness. Either way, you “add value to the business” (yeah, I hate that term too, but it is what audit is about, and so appropriate).