Sometimes I think that we will all soon be out of a job, and then I have a very boring task to do, and I wonder why I can’t just ask a machine to do it!
Just recently we had the task of comparing a 900-page PDF document to an Excel document that was in a slightly different format. Now, if I was going to do that page-by-page manually, then, as an intelligent machine, it would be easy, right?
Unfortunately, the machine learning is not quite accessible for me to just ask my computer to do it… so I found an intern! (Luckily the intern did find some online tools, but the process was still semi-manual – and then required reviewing, etc.).
So, what types of tasks are we currently getting machines to do and what types of tasks should we soon be able to get machines to do? And how will we get machines to do them?
We recently moved our kids from one high-tech school to a much cheaper school that could not afford any computers. The reason? Because the high-tech school is convinced that our kids need to use PowerPoint and Excel from Grade 3 in every lesson. But PowerPoint and Excel are surely soon a thing of the past, right?
And so is programming! Or data analytics… and auditing?
For example, when you get the latest version of any good data visualization software, such as QLIK, you will be able to use a “bot”. Using the “bot” you can type a question: “what were my sales in 2020”? And before your eyes a chart showing the sales of 2020 will appear. This is really what we mean by machine learning. It is getting the machine to understand the human-formed requirement, and doing the obvious work, that any decent data analyst would know how to do. The machine interprets the sentence and does the work to get the result.
And we can go further. We can then say to QLIK bot: “what about the margin?” In this question, the bot will be “in context”, meaning that it knows the context of the conversation. It knows that we are talking about 2020 and it will give you the margins for 2020. The machine has “common sense”.
So, these things are coming. And it is true that if you have an army of data analytics people in your office, you may not need so many of them in the future. Instead of asking your data analytics team to produce a dashboard on sales margin per product, you will be able to write the sentence “Sales margin per product” and QLIK will present you automatically with a set of options of dashboards for sales margin per product.
You might be thinking – but how does QLIK know where to get the data? Well, probably someone at some point is going to create standardized data models for each major ERP (Enterprise Resource Planning: aka accounting tool) system so that it can become an off-the-shelf SAP robot for QLIK.
You might also be thinking – but how does QLIK know what to do if some of the data is from the ERP and some of the data is from elsewhere? So yes, probably you will still need someone to understand what data is in Excel and elsewhere, and correctly give that data to your robot, so that the robot can identify what the data is, what it is used for, etc. QLIK will know all of these things because the data will be set up in a Data Lake, by your IT department and it will be categorized and organized correctly so that the robot can easily ask questions about it.
So, if you are in data analytics for audit, you may be wondering what the future holds for your particular skill-set right now.
Well, marketing people do like to say that everything works fine before it does, so we still have a few more years before we can truly speak to our computers. But probably only a few.
In the relatively near future, it means that the auditor will need to focus on those more functional skills of actually interpreting the information received.
For example, if you receive a dashboard from your system with the margin per customer, then you might want to just copy-paste it to your audit report, to show that all is good because the margins are all positive.
But if you are a good auditor, you might think, that all looks fine, but are there any products that have a negative margin? Maybe, if you ask the questions like that, the machine will always come up with the answer, “no”. What if you asked the question “are there any customers products that have a negative margin?”. Then the machine might say “yes, customer ‘Mercedes, Germany is getting product ‘leather upholstery’ on a negative margin basis for the last 3 years in a row.’”
The machine has given a very interesting answer. Now, the auditor could think, “great I can put that in my report.” But if you are a good auditor, you might then think. “ok, but why?” Then you could start asking other questions, such as:
A good auditor will think about all of the situations and all of the different contexts that could help to explain the anomaly of negative margin for leather upholstery sold to Mercedes Germany. They will use their knowledge and experience of the company, internal control weaknesses, internal audit standards and non-corporate information sources to think about why the anomaly may have occurred, and then to test those hypotheses until the proof is found.
This will enable a good auditor to go into their audit meeting and present the full story as to what is going on in the company, painting a detailed picture of the fraud scenario, or showing that there is some error in the CRM-SAP interface causing the figures to be wrong.
And yes, maybe in the more distant future, even the machine, might be able to think out of the box and answer all of those questions, that the auditor would think of. However, for the moment, data analytics and data visualization software companies are still racing to perfect the “bot”, so that auditors can simply ask the question “what are the sales margins for 2020”, rather than spending days creating data analytics programs to get the answer.
From my experience, I would expect that by 2025, most data visualization software will be at the stage where the bot can easily answer basic questions, such as “What are the sales margins for 2020?”. However, the answers to those questions will probably still be disputable because their accuracy will depend on the accuracy of the underlying data lake. Did the IT department match the correct information to the correct category? Did the IT department really understand the non-SAP data sources and how to link them together?
Today, we have a lot of errors concerning interfaces between systems. It always amazes me, the number of people employed to re-compute this or that to check if totals from different systems match. Such will probably be the same in 2025, to check if the robot got it right.
And then comes a new task for the auditor, which will be to ensure that there are controls in place to check that the data robot is doing its job properly!
So the life of the auditor, even the IT auditor, although it will evolve, it is still here to stay for another 10 years yet. At the same time, the auditor will get more intelligent and be expected to ask more questions. So hopefully, the machine and the auditor will grow more intelligent together.
Thanks for reading!
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