Ep. 148: Gregory Kogan - Self-service Analytics
Gregory Kogan, CPA, joins Count Me In to talk about the value and importance of self-service analytics. Greg is a professor of practice in accounting at Long Island University focusing on teaching undergraduate and graduate courses in accounting and finance. He received his MBA from Rutgers Business School in Accounting and a Bachelors in Science in Computer Science from Rutgers University. He is also currently pursuing his Doctorate in Business Administration at the University of Scranton with the research focus of data analytics in accounting. In this episode, you will hear Greg talk about what is driving the accelerated pace of analytics adoption, how organizations can and should focus on self-service tooling for their analytics, and how data governance continues to play a role. Download and listen now!
Self-Service Data Analytics and Governance for Managers (book): https://www.amazon.com/Self-Service-Data-Analytics-Governance-Managers/dp/1119773296
FULL EPISODE TRANSCRIPT
Welcome back to episode 148 of Count Me In, IMA's podcast about all things affecting the accounting and finance world. This is your host Adam Larson, and I'm pleased to kick off today's episode by introducing you to Gregory Kogan. Gregory is professor of practice and accounting at Long Island University, focusing on teaching undergraduate and graduate courses in accounting and finance. He is also currently pursuing his doctorate in business administration at the university of Scranton with the research focus of data analytics and accounting. So in our upcoming episode, you will hear Greg discuss self-service analytics. Keep listening as we head over to the conversation now.
In the field of finance and accounting, there's been a lot of talk about data and analytics. And, I know in your space you have a lot of experience. I'm just curious from your perspective, what is really driving the accelerated pace of analytics and the overall adoption at these larger enterprises?
Yeah, so I think the biggest thing, and if we're talking about the finance function, it's really the, realization of ROI (return on investment), where companies can use these new techniques, analytics, automation to accelerate their processing, right? So in finance accounting, for years, we've been doing things manually and repetitively. And now with these new tools and these technologies, a lot of companies are adopting these tools to accelerate processing, reduced processing time, reduce hours, accelerate processes, and there are benefits like it's more accurate, there's more control, better internal control. And those are really big benefits on top of the financial benefits. So there's sort of a convergence, I think that, companies are just taking advantage of this, the, to have more smooth and streamlined processing. That's more efficient.
Now I know something that you focus on or, you know, you'd like to share a little bit more here are these, self service tools, right. And, you know, just for our listeners, what are some of the defining characteristics of this subset and how does it work into analytics? And, you know, when it comes to again, advancing some of these opportunities, I guess you could say, why do these tools lead to more of a decentralized pattern for your reference?
Right? So the tools, yeah. So the tools we're talking about, you know, and coming out, you know, very much out of the what's happening in public accounting and what's happening in the finance function in terms of, financial and managerial accounting. We're really talking about Tableau and Alteryx, which are off the shelf tools. And even in higher education, we have a lot of these now in the classroom. So this is a still pretty, fairly new, but very much highly used. And we call themselves service tools because, it's not something you develop, what you end up developing is a specific process within that tool. So for example, an Alteryx, you can create a little process that say does a reconciliation or a certain reporting. And it's something that used to live in Excel. That's really now living in this tool and we call it self service. It's in that bucket of you can really do it yourself, much. Like you do Excel yourself. You could really, as a finance professional, since it's low code or really no code you pick up the tool you put in your data, which you really, you already have access to. That's really something you work with on a day to day, and you can set up these, we call them analytics, assisted automations for Alteryx and in Tableau it's really dashboards and visualizations. So it depends what part of it you're working with. But yeah.
That's very helpful. And I know, you know, in our space management accounts, specifically, a lot of that, you know, internal focused and we're really into, you know, the storytelling behind it and the tools that you referenced literally enable, you know, our, our listeners, our finance and accounting professionals to present this data in a way that's easily easy to understand for everybody, right. I think that's really the goal, but, you know, taking it even a step further here, try to, you know, set the stage for us a little bit. What are some of the primary motivations? And, you know, there is some kind of investment or, you know, even if it's just a learning curve in order to adopt these tools, what are the end goals, but what can our listeners expect if they're able to implement these strategies?
Right. So what you can, what are the, some of the benefits, essentially, after some investment, what you can end up doing is something that you do on a recurring basis, manually in Excel, right? And, and we had this also, as a case study in the book that we're kind of referencing here, the self-service data analytics and governance for managers, but this is something that I've been doing as a case study with students and in the MBA. And what happens is we basically have like five years of data of balance sheet and income statement data. And, and we do this in Excel where we compute all the financial ratios, profit margin, asset turnover, return, and equity. And we do like the DuPont model, basically for all the companies in the S&P 500. So for example, what we did as a case study in the book, we put it in the Alteryx and then we set it up as like little steps, rather than Excel. It's sort of all in one big place and you could still see everything. And we do pivot tables and graphs. It's still a very, very good, but once we set it up in Alteryx, we're able to filter the data by industry. So all of a sudden we started looking just at information technology. We started looking at graphs for each company of all the ratios, and then we started looking at specific companies a little bit further down the line to see, oh, wait, we just keep looking for the best one. What is the best industry? What is the best company? And then for that company, we have four dashboards for each of the ratios over five years. And after we set that up, we thought, wow, if this was like, say this was in management accounting, and I was doing my own internal reports, it could still be profit margin by region or geography. I could really sit with that and just flip my filter from Europe to north America and see my ratios, you know, and then we were thinking about it for me to do it in Excel every month. And it's something I used to do as an accountant. I just imagine it's a lot of work, get the new data uploaded, reconcile it. And that's something that takes us a couple of dates and just the flip, the switch. And Alteryx where you just upload the new data. And it does it for you. That's what we sort of started imagining. And of course we have seen the benefits. We've talked to people who've seen the benefits, but just to feel it yourself, like that amount of work going down from three days to like 30 minutes is exciting. And I don't know, I don't think you lose anything in the process. In fact, it is still stable. It is controllable and it's more flexible because the, all the charts are, you still see them, you know, and you just, you do it yourself. It's not something you have to call an IT person too. So I think it can even feel very empowering that it's still your it's within your role and your routine. You just do it in a different way.
Speaker 2: (07:36)
Just a quick follow-up on this, part of our conversation here, you just mentioned, you don't need it for this. It is your responsibility, essentially, as you know, within the finance function, but what is the learning curve? You know, what, what goes into actually being able to upload this data and, you know, work with it to a point where you're comfortable, you know, first of all, you have to trust it, right? You have to trust that everything's working because you're not the one who's actually doing it. I think that's a big thing with accountants, right? They're used to, as you said, reconciling everything and they know that it's right, but you know, the trust factor, but then the learning curve and just being able to do it all from your experience, what does that look like?
Okay. So, the learning curve is basically, and this is something that I went through a couple of years ago is something that, it can take a couple of weeks essentially to get ramped up. And, you know, Alteryx itself has a bunch of videos on their website and a, and a guide on each one of the processes. And they give you, I believe the license. And also the licenses are very affordable or free, depending on the situation, whether you're a student or affiliated with an organization, or it might even be available within your organization. And I would say, yeah, it's a couple of weeks that are kind of playing around with the process. Following the videos, the training itself can take a couple of weeks to get set up with these, and then the setup itself, or the specific process can be a couple of hours or a little bit more. So it's not, it's not as intensive as you would think, oh, wow. I have to take a year to go study this stuff. No, it's a couple of weeks of, I would say, an hour or two a day to get caught up and then a little bit more to play around with it. And the only thing I would recommend is speaking to the other people who are using these tools and kind of try to connect whether it's online or through these podcasts, or, I know that IMA has several courses that I've taken on RPA. I'm sure now there's other ones in data analytics. So actually I've taken a couple of IMA courses on data analytics and RPA, but those are very helpful. And if you, in fact, if you start with one of those that I think that one was four hours and you build your own study and you connect it with speaking to some people. Yeah. There's really no standard way. I think because it is a little bit of a new space, but I think organizations like yours really help out because in a way I would compare it much easier than say going out and studying for the CPA. I mean, we're talking about really like a 10th or one point of the effort. So I would think, you know, start with those smaller courses and build up from there.
That's that's perfect. And thank you for sharing that your personal experiences, you know, I think that helps our, our listeners really understand, you know, all this sounds great, but having an idea of what goes into it, you know, it makes it a little bit more, you know, feasible, I think, in their minds. So, that's great. I appreciate it. And, you know, taking this a step further now, talking about analytics and the different things you can do with data, if you've taken a couple of the IMA courses, you know, one of the things that we've put at the foundation of all of our data and analytics conversations is governance, right? And I think that's something that, you know, you might be able to automate some of these processes, but the governance needs to be in place. So what in your, you know, your voice, what is the importance of data governance and what goes into the, the requirements, you know, your recommended procedures, policies, whatever it may be.
Yeah, yeah, absolutely. Yeah, we speak extensively about governance and, you know, I think data governance is a huge field of study, right? And, and, and it, you know, and it ends up probably the best way to enter it. And I think data governance in this context really focuses on the input level because when you, when you work with the self-service analytics tools that are very much accessible to management accountants, the biggest, most encompassing issue is on the input side, right? Much like with any Excel spreadsheet and picking the same principles, you have to make sure that those inputs are correct. That there's integrity, that there's a sort of a custody chain of data as it moves from different places, whether it's in the ERP system or a cloud to the Excel, to the Alteryx out of Alteryx to somewhere, back to the ERP. So that custody chain has to be maintained and made sure that every link is properly has proper security and integrity, privacy, accuracy, and then the extra step. So all that I think is already well-known in a way in the data governance was just like a whole field of study. And I know you focus that you focus at a time a very much, and then the additional pieces that we discussed that are specific to self-service analytics are things within the tool. So once you get in the pool and say, Alteryx, yeah, it sounds the one I described does sound kind of like simple and exciting, but I've seen some that have like 50 processing steps and they're looking for fraud. And now they're using advanced text analytics, the mine, a whole email database. So it gets more advanced. The capabilities are there and people are using them. So what we recommend is also that additional layer of each step within Alteryx has to be verified, has to be assured and has to be tested, you know, make sure what you put in is what you're expecting to get out. Make sure it doesn't seem like a black box where you don't know what's happening inside and making sure that the auditability of it is there, you know, whether it's for management, accounting, and that's going to management, or it might be a number that ends up somewhere in a report, that's going somewhere else. So there's a risk assessment part to it that we discussed where we say, Hey, we were going to build these, analytics assisted automations. We're also going to make sure that we're aware that there could be some risks and we should be auditing some of that risk and we should be monitoring the performance of those builds.
So just real quick on that topic. Cause I did have a follow up on that as far as risk goes. Again, some people who are new to this and may not have the governance procedures in place and, or, you know, any kind of internal control, maybe over, you know, like you said, the custody chain there, if you don't take the time and put in the effort in order to ensure that, you know, all of these policies are in place, what could the risks be? You know, sometimes you want to give them a picture of down at the end of the road. If you don't do all this, this is what could happen. So why is governance so important? What are you really hopefully preventing?
Right. So what's going, what happens with these? And this is on one level, there's that traditional risk that we know from accounting where, you know, number's wrong and ends up on a report there's liability, there's risk, there's reputational risks. There's financial risk. Yes. That's all there. And we probably are aware of that already, but the additional piece here, is that, you know, if you go back before all of this, all of our digital processing was inside of some kind of ERP system that centralized ERP system already was designed with the controls in mind, authorizations, reconciliations, different checks, segregation of duties, and all the users were funneled into those control funnels. If you think about it, now we have people sitting there doing Tableau, doing old tricks on their desktop, you know, getting their data from wherever and inputting it in. So that's sort of what we call data. democratization where now users are using the data to do their own processing. So they're not being funneled into the central ERP system that has all the control architecture. So we need those additional controls. And one of the things that can happen is just total chaos, where everybody's doing their own processing and they're doing their own reporting and say, you're like a higher level control manager at NuCalm, and you say, how do I know any of this is right? It's not, where's the segregation duties where where's the reconciliation where are my additional system checks, that's all happening on those decentralized basis now. So without governance, it could be a situation where you actually can't really rely on any of those outputs anymore. So that's sort of a, trying to get it in advance of that. As it gets adopted, the governance can really, can really help. And the other thing we recommend, and I think you mentioned that it's also lack of governance has been known to be the number one issue in scaling the analytics. So people say, oh, all of a sudden, oh, I can't do this. Oh, this is too much. And it's partly because there's no governance and scaling analytics can be a huge digital transformation goal. So we sort of say, Hey, we want to scale digital transformation analytics. That is sort of something that's actually going to help you do that. Otherwise it's, it's really, it's kind of a steep slope without it.
That's perfect. That's exactly what I was looking for. And, you know, like I said, sometimes you just paint that picture upfront and give everybody the heads up, but this all sounds great. And listen, I know you briefly mentioned the book. I want to give you an opportunity to talk a little bit more about it here, as we wrap up our conversation, because it's all very valuable information. So how is all of this really presented in the book? And again, plug the name for us one more time and give us a little bit of the background story to it. Why's it all relevant? And then, you know, what kind of information or, takeaways can our readers and listeners expect from some of the work that you.
Absolutely. Yeah. So the book is by myself and Nathan Meyers, and it's called Self-Service Data Analytics, Governance for Managers, and essentially what we do, it's sort of a two-step process where we first discuss all this analytics and service self-service analytics. And we discuss it in a way that is very accessible to accountants because we're both CPAs with an accounting background. And, Nathan has been leading these digital transformations in the corporate world, and I've been embedding analytics into accounting classes in higher education. And we sort of looking at from a perspective of making it really accessible, making it really understandable and making it really kind of down to earth. And that's the first couple of chapters where we define all of these technologies and we make the argument that where we are, where we are today in accounting is where aside from, for example, artificial intelligence and RPA, which are tremendous topics in this world, we kind of make the argument that look this world of Tableau and Alteryx is something that is really happening. And we kind of make the argument that it's something that may grow quite a bit. And I, and I have seen it growing since we started, in the past year and we'll see what happens next year, but it seems like a lot of organizations that are really using it. So, and then we go and say, we say, well, there's an issue with controls if you're doing everything in Alteryx and Tableau, because it is decentralized. And we actually propose a whole governance framework for users. And it's, it's mainly around project governance where you kind of make sure that each of these projects has proper assurance capabilities and, development standards and it's properly documented and has the proper data governance and in risk governance talks about how each one of these can be risk assessed according to unique risk dimensions, and basically treat them as a portfolio and have a whole portfolio of these builds and risk assess each, and then monitor them, monitor them, report risks, report exceptions, create risk transparency. And basically the goal is to create trust and the outputs. And now we live in the world where there's so much emphasis on, in a way mistrust with technology. And then there's also a ton of emphasis by accountants. I think we're leading the way in creating trust around that, but then that's really the goal. Each chapter includes like a, basically a checklist of governance precepts, for project risk. And then we also talk about investments. So make sure that your dollars are going through the right opportunities, make sure you are prioritizing the best processes and it's sort of to help grow your ROI. And we call that investment governance.
So again, before I wrap this up, we may have to bring you back and talk strictly about this governance framework. I think that's something that our listeners would really be interested in, but in the meantime, you know, before we get that reporting done, where can the listeners find this book? How can they get their hands on it?
Absolutely. So the book is on Amazon, you get it through Amazon or through Wiley directly. And, and essentially as self service, if you go self service, data analytics, governance for managers, it comes up. Usually it's Amazon is the way to go these days, but, Wiley has a very nice thing. And the other thing I'll say is that if you are a student or part of a university, I know it's widely available in, in all the university libraries. If you just go, if you just search it in the library search box as well.
Speaker 4: (21:24)
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.