FULL EPISODE TRANSCRIPT
Mitch: (00:05)

Welcome back for episode 42 of Count Me In. As we strive to provide you with the information most important to your role as an accounting and finance professional. No topic may be more relevant than that of data analytics. Robert Koechig, president at TEG IQ talked with Adam about the importance of business intelligence for the CFO team and how to effectively use data analytics. Let's head over to their conversation now. 

 

Adam: (00:35)

So Robert, what is the importance of effective data analytics and business intelligence for the CFO team? 

 

Robert: (00:42)

I think the key word in the question that you asked was effective. I think most organizations in today's age know that there's value in data analytics and that there's business intelligence that they could access. I think most of the industry looks at that as basically really pretty Excel sheets, whether it's in browser, wherever that is. So I think we kind of have to take a step back and say, what does effective mean on what are effective data analytics. It's one of the things that we kind of strive with. We start with really saying if it's not going to provide actionable information, where you are digesting something that you're seeing on screen and being able to make an impactful decision in your everyday business operations. I don't really think it's an effective BI tool. So that's, where we really like to combine the data analytics and the business intelligence. The technology side, what we're passionate about is combining that with the human decision making element. So as a CFO we want to have technology bolster your gut feeling and or challenge it. being as the case may be. 

 

Adam: (02:01)

That makes a lot of sense to connect the two together cause they, you have to partner with the business. You can't just look at pretty charts and make decisions. You have to go beyond that. 

 

Robert: (02:09)

Exactly. I mean it's what we're trying to do is give you a tool that will enable you to make a corrective action at day two as opposed to day 45 when the books are closed at the end of the month. That's really what we're looking to. That's the type of environment that we think does become now an effective tool that you can use in your organization to really change. I mean, in some cases, if you could correct an action and have visualizations where it alerted you to an action, at day two in a process. So something that happened yesterday and you can make a corrective change today, then that's something that I think is pretty powerful. 

 

Adam: (02:50)

So could you give us a overview of the types of data analytics and business intelligence you focus on? 

 

Robert: (02:56)

Sure. So really what we like to do is, look at the fragmented data solutions. So what I mean by that is most organizations, especially in today's app world, have an app that does one specific thing very well. There are other situations where you have an all in one environment where you have one ERP system that does everything from payroll to time entry to point of sale to everything else. But really what we look for is, you know, kind of the low hanging fruit right now in the industry, which really makes a big value, is the organizations that use one application for payroll, one application for their order entry or their point of sale, one maybe business operational system in their warehouse or in their plant. Where there's reports that are being driven from each one of those, but none of them are really in a consolidated reporting fashion. One of the places where we really provide a lot of value and really what we're providing is value to, to our clients is the ability to take fragmented or disconnected systems. Great example of that is you have Salesforce as your CRM and maybe you have QuickBooks as your backend financial. They have operational integrations, but to run a consolidated report or even see insights right next to each other, Salesforce and QuickBooks. Usually what that means is you have someone on your staff who's pulling data down from Salesforce, pulling data down from QuickBooks, putting it into Excel and manipulating it from there. So that's typically where we like to live and the way that we do that really is by the ability to pull all the data without impacting your environment. So one of the things that we try to do is we want to have as minimal it involvement as possible and the way that we're able to achieve that is really by our interview interview process. So as far as the types of data analytics and business intelligence that we focus on, it's really, interesting to see how even within identified vertical niches that every organization operates a little bit differently. Of course you have your 80 20 rule, which always applies. However, where we like to really provide values is really looking at partnering with you and saying what will make the most sense? You know, we don't want to just provide a product, and this is typical in the industry, is within business intelligence. Someone says business intelligence, someone says data analytics and people think, okay, I'm going to have a dashboard, a dashboard that I can log in and it's going to show me all my metrics. And that's great. And from a very high level, it's absolutely what every single data analytics or business intelligence project is. But again, we're probably going gonna revert back to our first question of an effective environment. What we try to do is really say, what's going to make the most impact to you to see connected and joined together. it's one of, it's one of those things where we could provide something off the shelf. And a lot of what we do is, however, it's also partnered with the custom piece that makes your job that much more effective. 

 

Adam: (06:22)

So as we dig a little deeper into this idea of analytics and business intelligence, you know, most people understand analytics and insight is vital for business success in today's industry. So what is it about the data analytics that many often overlook or don't fully understand? 

 

Robert: (06:38)

Sure. there's the old mantra within It, really any technology project where you do a project and you kind of wipe your hands of it, you walk away and it's there and it's running. Business intelligence is a little bit different, you know, the analytics and insights that can change week to week in some cases. So what we try to, what we try to really educate, as we go through our projects, we try to really educate on is that this is a commitment to changing the way that we make our decisions as part of this organization. What I mean by that is what we really want to be able to do is say, this will be effective to you as as much as you use it. And you know, there's a lot that we can get into in machine learning and AI where the more that you put in, the more value that you get out, but at a very base sense of it, where we see a lot of the maybe overlooking or maybe just error in calculation is a lack of commitment from organizations once they decide to go forward with a project. So what we try to do is really focus on the data literacy. So yeah, we have the project, we have to build everything, we have to make it right. We have to make sure that it's giving you the insights that you need. But then from there, that's really the launching point. That's the stepping stone to get to an effective system. That's where a lot of organizations that we've helped have stopped is that post implementation. We really focused on that data literacy part where throughout your whole organization, ideally, but really especially the key decision makers, we need to be in front of them. Or you need to have a plan in place at your organization of, hey, this is a system that will benefit us in our decision making. How are we going to commit to always using it? And that's probably, I would say that's a majority of failures in a system. I would also say we try to, we try to validate data. there's always the caveat with these type of projects where garbage in, garbage out. If that's what we're looking at where we just have bad data come in, we can't fabricate good data. So in some cases you may need, you may want the business intelligence project, you may need to take a step back and say, we really need to clean up our data first. Let's do some data projects before we get into analytics or business intelligence. 

 

Adam: (09:09)

What is it? What do you think it is that gets somebody over that post-implementation and blues where they stop going forward? You just mentioned in your answer that they reached that wall. They just don't get over. What does it take to get over that hump? 

 

Robert: (09:22)

I'm a big proponent of top down, implementation specifically when it comes to these types of technology tools. What I mean by that is if as leaders in the company, no matter what level that is, whether it's the C suite, whether it's the director level supervisors, whoever's tasked with making those decisions when they're running their meetings, this needs to be a focus point. This needs to be the start. I'll give an example of an organization who implement Salesforce and the VP of sales never looks at it. They're probably not going to get great value from Salesforce, whereas the VP of sales who starts every meeting with Salesforce pulled up and looking at the results that are in Salesforce, they're going to get better, better adoption, better usage, better utilization within the tool. So one of the things that I would challenge any organization who's started these projects or who's looking at these is really think about that post implementation. You have the tools at your fingertips now, how are you going to use it so that you are basically making sure that you get your ROI out of it. And the first way to do that, in my opinion, is to use it while you're making your decisions. Of your company, usually it's very high level KPIs that begin the process. So when you're in your executive meetings or your board meetings, you know, the data needs to be in front of people there. That's really where you start getting an immediate impact. 

 

Adam: (10:55)

So share the data from the top down basically. 

 

Robert: (10:57)

Yeah, I mean the transparency and I understand that there's some privacy concerns that there's, you know, that type of situation. So obviously it's not going to be carte blanche but really for the meetings where decisions are really being made, it's critical that we're including this system and this tool in those, in that decision making process. 

 

Adam: (11:22)

So Robert, could you tell us a little bit about your role at Tag IQ and what you're doing to assist companies when it comes to data and insights? 

 

Robert: (11:30)

Absolutely. So I'm president of TAG IQ. We're a trout CPA company. Trout CPA is a public accounting firm located in Lancaster, Pennsylvania. They acquired us about a year and a half ago now and really what we're seeing, the partnership and the synergies there are that they're providing advisory services to their clients, whether it's around, evaluations, whether it's around tax planning, you know, really from those type of services. Well, the analytics is partnered with that. The analytics makes that service that much better. So what we are, what we've been seeing and what we're, what kind of our vision is here, in the past a year and a half and forward is that we're able to provide strong financial advisory services in addition to the technology analytics space. The partnership there is pretty powerful and so from my role, really what I do is I'm the Go-Betweens so I can speak the executive language and I can evaluate and identify business opportunity from a decision making standpoint, but I can also convert that to a technology developer who's going to be tasked with actually executing that project, you know, I'm passionate about solving business problems. And really when it comes down to it, one of the problems in any business is how do we make the best decision that we can make. So if you boil down everything, that's really what we talk about in business. So it's provided me a great forum to be able to just talk with business owners, talk with CFOs, identify what their challenges are and then translate that to a technology solution, that can make their life easier and their organization more successful, 

 

Adam: (13:26)

Make those effective decisions as we've been talking about. 

 

Robert: (13:29)

Bingo. Exactly right. 

 

Adam: (13:31)

So I think it's fair to assume that most businesses are using some form of technology. I mean, you already mentioned that when you, talk with different companies, they may have one doing their customer information in one doing their books, another one doing their something else. You know, everybody's doing so many different technologies to assist with their analysis and reporting, you know, but if the businesses aren't using effective data business and business data analytics and business intelligence tools already, what can they do to be more efficient? You know? And where should they look to start? 

 

Robert: (14:03)

Sure. So they should call me, absolutely 100% first. But joking aside, what we really want to do is we try to focus on two things which I believe are foundational. Consolidation. We've talked about that you have fragmented systems. You want to be able to have a consolidated environment. And so as you're working with your internal it or you're working with a consultant, whoever you're working with, that got to be paramount to be able to even start with an effective, environment. So consolidation of the data, whether it's everything that's completely integrated or that you're pulling data into its own data warehouse or data Lake, those are some of the, some of the tech technical words that we use to talk about. We store all of the different systems data in one place. That's definitely something that you want to look at. Even if it's just a question of do we have a consolidated data environment, it's a great place to start. It will open up many conversations probably with your technology provider. The other side of it is automation, one of the soft costs that every organization has when they don't have an effective CIT, system is they have in some cases multiple but at least one person's time who's running manual reports, who's doing this kind of the same solution. But manually when you add that up over the course of a year, the investment that that company's making in that employee to do that, typically is not where they see value in that employee. They typically don't have a position for manual data polling. So it's usually a controller or someone on the finance team who's kind of crunching these numbers manually and at a cost to the organization. So automation is something that you immediately, you have an ROI on because you're freeing up it probably key employees time, to be more strategic with the organization. So those two things are definitely foundational, to what we are really seeing. And what you're really able to be able to do is, if you can, if you can answer those two questions, do we have a consolidated environment and is it automated? What you're, what you automatically get out of that? If the answers are yes, is a lack of a waste of a key resource time, but also insights that you wouldn't have in a fragmented system. 

 

Adam: (16:36)

That makes a lot of sense. And in that key resource, you're suddenly able to develop them into a different type of a leader or grow them in their role because they can look at the data from a bigger picture as opposed to just mining little things. 

 

Robert: (16:48)

Absolutely. And really, you know, the insights part of it too is often overlooked. But the insights that you get from having a consolidated data environment really starts giving you confidence that you're looking even at the right KPIs that you're looking at the right metrics. You know, the, the ability to say, what should I be looking at? Sometimes you get lost in that when you don't have all the systems pulled together. You have one department who's working off of their system and it's never really integrated into the rest of the organization. And you find out that that has a key, that that becomes a key indicator of your business. 

 

Adam: (17:24)

Yeah. Everybody needs to be looking at the same picture to make the best decisions. So we just talked about how would somebody would get started. what about the other side, you know, a business already has advanced analytics and business intelligence. What do you see happening in the future? You know, what should that establish firm do next? 

 

Robert: (17:43)

Yeah, there's a lot out there, right? We're talking about technology in today's world and sometimes that gets a little, you know, I think the three, probably the three main things that everyone's hearing right now that are, whether you want to call them buzzwords or trends or whatever you want to call it. it's definitely the machine learning and AI. That environment, which is really, once you have an effective BI tool, machine learning and AI comes almost naturally out of that. but that's really becoming pretty hot and there's a lot of value there. And then the other one I would say is blockchain. Blockchain is one of those things out there that I think everyone kind of is aware of and people are, are dipping their toes in and you're seeing some of the large enterprise organizations make investments in that. But I don't think it's really come downstream to the mid market, small market yet that will, that will get here eventually. But I think if you want to make something that would make an impact on your organization today, it's really looking at the machine learning and AI. you know, I think in the next year or two you're going to start seeing most applications have a on switch to apply machine learning and AI. It's going to be one of those situations where you can switch it on and look at what you get. But unless, like we talked about before, you're committed to using it and you're committed to looking at the insights and splitting your data and looking different ways at it. you know, there really needs to be some expertise around what insights you're getting out of that machine learning. You know, the, the great example there, that I think probably everybody uses is customer churn. So machine learning is fantastic at looking at characteristics of your customers and predicting based on your history predicting which, you know, five or 10 customers you think you're going to lose in the next year. And if you can tell that six months before it may or may not happen, you probably are going to touch that client differently. You're probably going to work with that client differently. So that's an example of, you know, once you have your foundational analytics and you have automated reports, you have consolidated insights, you really want to start looking at that predictive analysis. Machine learning is a great place to start with that. 

 

Announcer: (20:04)

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.