Contact Ron Guymon: https://www.linkedin.com/in/ronald-guymon-369b1710/
Ron Guymon at U of Illinois: https://giesbusiness.illinois.edu/profile/ronald-guymon

FULL EPISODE TRANSCRIPT:
Adam: (00:00)
Welcome back for episode 77 of Count Me In. IMA's podcast about all things affecting the accounting and finance world. I'm your host, Adam Larson and today's conversation revolves around the future of accounting education. For this episode, Mitch sat with Ron Guymon, Senior Lecturer at University of Illinois. There, he teaches data analytics to online master of accountancy students. Ron also has experience helping small to midsize companies assemble, analyze, and visualize data to find actionable insights. In his conversation with Mitch, Ron discusses, what he has already seen in accounting education as universities seek to prepare the future of our profession and share what he believes to be the most valuable skills for students going forward. So without further ado, let's get over to their conversation now.

Mitch: (00:58)
What has your role been in accounting education and how have you seen the curriculum or the required components change along your journey? 

Ron: (01:07)
  I had been in academics for a while and there had been a growing awareness in  academics, at least since about 2010 of the need to teach students more data analytics skills. Most of it was in Excel at the time. Because my academic background, I had some data analytics skills with SAS that I learned for academic research purposes, but those skills and some relationships led to an opportunity for me to leave academics and join a small, but growing business intelligence company called New Metric. And it was a risky decision at the time, but I knew the analytics were important. And I thought that if anything, this would give me some good experience that I could eventually use in class. I was also motivated by some family reasons. So, anyway, there was that side of things, but it turned out to be a great learning experience. And, you know, some things I learned have benefited me in the classroom as I had hoped, I guess. I learned how to use some proprietary data visualization software, and I also learned how to use R. So I had used SAS a lot for academics, but I learned about some other  skill or, analysis technique. And when I looked at, when I researched about it, I found a lot of references to R and so, that's what got me into R. So my academic training was really helpful and it gave me a leg up on the statistical concepts, however, I think I can relate to many business professionals who have primarily relied on Excel and they're worried about the learning curve associated with learning a data analytic language. So, anyway, so I've come back into academics with, some experience using data analytics software that has been very helpful. 

Mitch: (03:03)
So you touched on it just now and you said, it may be difficult for some business professionals to run either, maybe want to pursue learning this new language as you put it, or the fear of being able to. So what is your perspective or what have you seen as far as particularly accounting and finance professionals really interested in this sophisticated data analytic competencies and, you know, even going back to students. Are they aware of this growing need and the fact that it will benefit them in the future? 

Ron: (03:40)
I think the professionals are aware of it, and I think that's because data is seeping into every part of an organization. And so I think they're becoming aware of it, or at least the need to be able to process more data and a desire to do that. They may not know that a data analytic language is, a tremendous way to help process more data. I know there are other tools available like Tableau for visualizing data, and some other tools for automating processes. But, I've definitely seen a need for, pretty much everyone in an organization, at least in my opinion, could benefit from learning a data analytic language. Cause you can just automate things. Students, I think are less aware of that at least undergraduate studentsk and, and so it's a little bit harder for them, but MBA students and masters students, I think they're, they're more aware of it cause they're a lot of them are working professionals as well, and so they've realized the limitations that come with point and click software, the benefits as well as the limitations, and so I think they're aware of the need to learn about it. But, yeah, the way, you know, it's kinda tough to transition from Excel or a Google sheets to a data analytical language. So that's the part that I think a lot of people are trying to navigate right now and it's not easy cause it takes time and people are working, they have families, and, and how do you learn something that's a pretty dense topic and, it takes a while to really be proficient at, but, anyway, so I think, I hope that's what I'm helping people do now as a professor at the University of Illinois, so anyway. 

Mitch: (05:33)
No, that's great, and you know, I'm sure you have your own preferences, but for our listeners here, if you could just offer up some kind of recommendation for those who are interested in pursuing more of this deep dive education, whether they are in the classroom, looking for some outside resources, professionals looking for some continuing education, where do you start and how do you really get feet into learning this new language? 

Ron: (06:00)
Yeah, that's a great question. There are so many resources out there. You could probably find a bunch on YouTube that are free. The problem is it's hard to know where to start and so I, yeah, like you said, I'm biased. I think the resources that the University of Illinois put up on Coursera are fantastic and we've tried to help people identify where to start and made a smooth transition from Excel, working parallel with Excel and AR or Python, so that you can really see what the language is doing. So yeah, that's my, you know, that's my bias. But before I was doing this, I found, I stumbled upon some great resources and I don't think it was stumbling. It was a result of going to meet ups and talking with others, but there's this group, or maybe it's more of a philosophy in the R environment, the R ecosystem, and it's called Tidyverse. And, there's this guy Hadley Wickham, who's like the leader, and he just has a way of thinking about manipulating data and has created a language that goes with that. That makes it really easy to take raw data and put it into a format that you can then analyze. And so there are some online resources from our studio that's where Hadley Wickham currently works. So our studio has a ton of great resources that will help you get started using R and analyzing the data in R as well. But I think for most people, the initial starting point is just learning how to read in data into Python or R and then start assembling it so that you can then visualize it or analyze it. And frankly, that's one of the, I think, maybe hurdles that a lot of people face is that they think, oh, I have to be a data scientist before I can start getting any gains out of using a data analytic language. But that is not the case at all. I think if you just learn how to automate a process of just reading end data, cleaning it up and maybe reshaping it and set that to run so that it happens every day so you don't have to manually do it. That'll save tons of time and you don't. And that, so let's, before you even know anything about neural networks or regression or anything that is a more deep. 

Mitch: (08:37)
Yeah, for sure. And I know where to start and that kind of fear as you were talking about some of the stuff going really deep, you know, we recognize that as an association at here at IMA and, you know, we started, our own data analytics courseware, really focusing on our data analytics and visualization fundamentals certificate, because like you said, some students, professionals who want to learn this stuff, but they really need that foundation. You know, that's what we wanted to make sure that we had to offer these fundamentals. So, you know, I would just like to plug for ourselves there and add a little extra resource that, you know, that's a really good starting point. And then you mentioned all the Coursera work that, the University of Illinois has out there. And, you know, we came across that and fortunately enough, IMA and University of Illinois, as, you know, we're able to use one of your courses. So, you know, I I'll plug that for you right here. You have the, the Beyond the Basics Data Analytics and Visualization for Accounting Professionals. And, you know, I went through that course myself and I must say, you know, it's, it's a little bit deeper and it's a great launching point for those who are interested in these different languages. So, is there anything specific about those, about your course, I guess, and, you know, the University of Illinois that you'd like to share as far as what data analytics and visualization really means for the accounting professional? 

Ron: (10:07)
Yeah. Well, thanks. That's nice of you to say, so I know there's always room for improvement, but I do hope it is an easy way to get into the area. Yeah, so I would say about that course, we have tried, I've had some experience teaching data analytics, and I think this course is a good, we finally found something that is, makes it more available for the masses data analytics, more available. At least learning about it, because it's so easy to forget what you don't know, and I think a lot of times when we start teaching data analytics, we want to get people up to speed where we're at, and we forget what we didn't know when we were starting. So this course really focuses on using Excel and showing people now using the great visual nature of Excel to manipulate data. We use that to show people what it means to  convert data from wide to long, and what's happening when you do cluster analysis. And so I think it's a, you know, that this course helps people get an idea, just kind of have that understanding that mental mindset that's required to know what's happening when you use a language, because language does everything in the background and you can't really see what's going on. That's, what's beautiful cause it happens so quickly. But if you don't know what's going on, it's, it's like magic. So I, so we've tried to kind of walk people through the process in Excel, something they're familiar with and get them to a point where they appreciate, okay, I don't want to have to drag it's the bottom of every column every time and rearrange columns before I can start doing a regression analysis or standardize the data manually using Excel for every row. And so, you know, then at the end of that course, we introduce visual basic for applications. The data analytical script language for Excel and we show people, hey, this is, this is a benefit of using a language. You can do these things with the click of a button, and, and so I hope we get people to a point where they're able to appreciate the value of a data analytic language and, you know, frankly, even if they just start using more visual basic for applications in Excel, I think that would save them a bunch of time. So, I think that's what, at least I hope that's what this course does it helps gently introduce people to this mindset so that when they start using Python or R they'll understand the analytic side of things and it's, you know, just learning the language side of things. And then you also asked for accounting professionals and, the importance of visualization. I think for, but almost well, not just accounting, but, in I don't know, probably every domain I'll qualify that because there's always an exception to the rule, but almost every domain has, or it could benefit from visualizing data because you're able to see relationships so much faster than when you're just looking at numbers. I think accounting and finance people are really good with numbers and we want to see the details. And so there's maybe less of a need for visualization in some sense, because people want to see the exact numbers want to understand, you know, how, how do the line items add up? But the same time I think being able to visualize trends is, is obviously very important. And so being able to create those visualizations on your own, I think is really powerful, and that's just a starting point. There's so many things that you could use analytics for such as finding or maybe identifying, prioritizing the list of customers who aren't paying on time, and so who's most likely to, end up paying, right? And who should you contact first? If you've got a lot of customers who you need to collect from, then you could create a model to help identify who those people are. You could also use analytics, especially for management accounting topics, one couple of areas that I've thought of a lot about our and  I'd love to get into more is identifying cost drivers for allocating overhead, cause there's so many different potential cost drivers you could easily, well, maybe not easily, but you could set up a script to look at the relationships between a number of different drivers and see which one is most accurate in leading, you know, forecasting costs are what overhead costs are. I think it'd give you a lot of insight along with standard costing. Being able to aggregate standard costs, start at a disaggregate level, bring them, roll them all up to see the overall favorable and unfavorable amounts and then break it down again and visualizing that I there's a lot of room for analytics to help with those processes. 

Mitch: (15:24)
So it sounds like there's a tremendous amount of opportunity when it comes to incorporating analytics into accounting in general. You know, when we first started discussing this conversation, I'd said, I really want to focus on, you know, the changing need of accounting education and I want to make sure that applies to continuing education as well. but I think, you know, what I would really like to kind of wrap things up with here is, you know, what is your recommendation moving forward for anybody interested in any aspect of accounting, as far as the skills and competencies needed, you know, resources that are available, tasks on the job that you think would be beneficial, different projects to work on, what is it that you would recommend a student or a professional to really focus on to ensure that they are maximizing their personal value with these data analytic tools? 

Ron: (16:21)
Yeah, let's see. I would say, don't try, don't bite off more than you can chew at once. I think if you try to just learn, you know, if you say, I just want to learn how to program or code, it's very abstract and that's really a hard way, especially as you're working, you're not a, if you're not a full time student, that's a hard way to go doubt things cause you just don't have time. So I think if you can just take whatever project you're working on and you could just try to start with that and think about what could I do to make this faster, make it more efficient. And I realized that you may not have, you may not know the terminology where to start what, even to Google. But if you could maybe contact someone who maybe there's a data scientist in your organization, or a friend or someone who, you know, that it was good, you know, with data analytics and describe the problem to them or what your tasks are, and they might be able to recommend to you where you could get started and what kind of tools to look at. And I think if you start with that, you'll learn some things that you can apply right away. It'll be productive for you. It's not just simply theory. It's not something that you're going to learn about and then forget because you don't use it, but it's something that you'll be able to put into practice right away. And so you'll remember it. And then there you'll learn a ton. They'll probably take a little bit of time to set things up, right. Just to start using Python or R. I know for me initially it was  like this is what developers do. I have no idea how to do this. And so just getting started with it was a huge hurdle, but, once you get past a few of those huge hurdles, every additional hurdle becomes easier to overcome and it makes it a whole lot easier to work on those hurdles, if you're working on a problem that is paying you, you know, it's a problem you have to work on for your job or, or something that you're super interested in that you would worked on anyway. If you, you know, if you have a hobby for, sports, for instance, you might be able to analyze sports data or cars or whatever the case may be. We live in a world where data is everywhere, so you can probably get data about anything, and so just find a project that you're interested in and try to, you know, think about what you might do in Excel. And then think about what you, how you could, you know, start learning how to automate that so you don't have to copy and paste things as often,you can automat it

Closing: (18:57)
This has been Count Me In IMA's podcast, providing you with the latest perspectives of thought leaders from the accounting and finance profession. If you like, what you heard and you'd like to be counted in for more relevant accounting and finance education, visit IMA's website at www.imanet.org.