274. CFO 4.0 Revisited | CFO Stories: How to become a data-driven CFO with Glenn Hopper

Narrator: Welcome to CFO 4.0, the future of finance. The CFO role is changing rapidly, moving from cost controller to strategic visionary. And with every change comes opportunity. We are here to help you take advantage of this transition to win at work, drive your career forwards and lead with confidence. Join Hannah Munro, managing Director of itas, a financial transformation consultancy, as she interviews key experts to give you real world advice and guidance on how to transform your processes, people and Data. Welcome to CFO 4.0, the future of finance.

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Hannah Munro: so hello everybody and welcome to this episode of CFO 4.0. So with me today is Glenn Hopper. So Glenn is CFO of sand. And is it Sandline Global? Glenn, you're going to have to make sure I get the titles right here.

Glenn Hopper: Yeah, our uh, so the full name is Sandline Global, but our logo just says Sandline.

Hannah Munro: So brilliant. So either way I'm right. I like that.

Glenn Hopper: That's right, that's right.

Hannah Munro: Brilliant. So tell us a little bit about yourself Glenn, because obviously, um, the reason I particularly asked you on the show was um, your book Deep Finance, which I thought in itself is a, a really interesting title. But tell, tell me a bit about your background when, you know, how did you end up writing a book?

Glenn Hopper: Yeah, uh, the book came out of uh, my years of experience, uh, battling with data in the finance, uh, in the finance realm and realizing pretty early on how important it was to not just have the financial data but being able to incorporate operational data, external data sources and into what we do. And uh, I'm an fpa, FPA guy by nature. Uh, and that's really, that's my comfort zone. I love building models and uh, I've just, over the years, I've been in finance for a million years, but I guess I've held the CFO role at four or five different companies now for about the past 15 years. And um, it's all been in the uh, small and medium sized business space, you know, under 50 million and annual revenue and it's typically, well, I want to say actually for all these businesses I've been in, they've all been startups, you know, just the first few years of existence. So startup, not a lot of revenue, out of the gates building something, not a lot of resources. And I learned, you know, I think um, necessity is the mother of invention. So the fact that I didn't have much of a team, I had to find ways to automate because I, I've always, no matter how small the company wanted to present financials the same as when I was in the corporate world. And if you don't have a lot of resources, you need to find as many ways as you can to automate that if you're uh, if you're going to be trying to look like one of the bigger players.

Hannah Munro: Absolutely. So, um, there is definitely something I do find. You get a lot more innovation in that smaller sector purely because they don't have a choice. Like if they want to look good and be, you know, um, up there next to the big boys, um, they've got to do something a little bit different. So yeah, I think that's um. Yeah, you see a lot, see a lot more technology savvy individuals I find in the staff, small to medium sector versus when you get higher up.

Glenn Hopper: Yeah. And you know, another interesting thing, when you're in a small business, everybody has to wear multiple hats. So at different companies I've had different roles. I've had um, IT roll up to me before and that's fine, but I'm, you know, probably not the best IT guy for, for a company, but you know, you just, again you, you just have to do what you do. But um, it's um. I don't know though. I, I say I'm not the best IT guy because that's not my, uh, my primary function. However, being forced to work as the IT guy has helped me more understand the technological side of things. So everything kind of rolls together and it builds to where you are. So experiences that may be painful at the time do give you additional knowledge for future positions. So. And it's things that I've used in my, uh, in my finance role as well.

Hannah Munro: Absolutely. Um, and we talked obviously about you being a bit of a data geek and loving, um, the, the analysis side. But do you find that obviously having gone into a couple of smaller businesses and you know, potentially talking to others in the industry, that you find different levels of data MA maturity within an organization? Um, in terms of, you know. And do you have any classification that you think about when you think about those levels of maturity?

Glenn Hopper: Yeah, so very, very different levels. And I've been. So it's a company where I am now. Company was founded in about 2015 and it's um, uh, the nature of the work here is it's a lot of service. There is a SaaS component and some uh, recurring revenue component as well. But everything is backed up by our project manager specialists. And um, there were these disparate systems where, you know, there's the, the CRM at the front where you're just tracking prospects and leads. And then once somebody's won, the data is kind of dead there. Then somebody re enters the project into the project management software and all that data just kind of lives there. And then uh, the accounting software doesn't talk to the project management except when we upload the uh, invoices. So in, so there wasn't, I mean and there's, you know, there's canned reports in each of those but you've got these different silos of data. And I do find that uh, more often than not in small businesses where the systems aren't all talking to each other. And that's for me getting those systems to talk to each other, whether it's implementing a um, full ERP solution, which is what I'm doing at the company where I am now, or just using APIs to get the systems talking to each other. When you have this unique identifier that carries through the full customer life cycle, you can get so much data. You can use that data to predict things like churn or you know, when it might be time to try to upsell or I mean there's a million things you can do with it. But if you don't have the systems across the board talking to each other, it's harder to, to um, compile that. And then you end up doing things like uh, you know, downloading into a SQL table on your personal laptop or do what everybody else does and just throw it all into Excel. And you've got these, you know, 25 tab giant spreadsheets that you're all your vlookups and your pivot tables and you know, one thing gets out of place and you break the whole thing and you spend half your time trying to go find where the uh, where the information is. So that's, I've, because I've been in these uh, sort of, we'll call them pre financing. The last, well this company was bootstrapped. The company before had some private placement investment early and um, you know, the expectation sort of in that seed to A round of companies is um, they don't expect, you know, you, maybe you don't have a lot of customers yet or any. So you don't really have a lot of internal data points. So people can get a pass. I think companies at that level of maturity can get a pass sometimes uh, on how much data they have. But when you want to move forward beyond the A round or sometimes, you know, depending on the environment, beyond the A round or if you're going to private equity investment, you certainly have to have a handle on it. And then it just, I always say that your, your data maturity and the processes that you have in place to handle that data, um, are, they're sort of like um, internal ip, meaning it actually adds value to the business. And if an investor, you know, maybe not to a customer but to an investor who is looking to put money in your company or a competitor maybe that's very similar. If you have this operational excellence that comes from the data and the systems and the processes, they're more likely to invest in you and, or put a premium on um, on your company based on that.

Hannah Munro: And I think that's a really interesting point actually Glenn, and I think it's one that's less thought about is actually seeing um, systems and integration in particular as an investment versus a cost. Um, and like you say, seeing it as a piece of value in the, you know, the myriad of ways that people value companies. But I think it's a critical part that very often gets overlooked until it becomes to the point where they actually have to do it because there's an investor lining up and they're, they're trying to give them information that, and like asking about systems are like we actually need to have this in place to, to move this forward. So I do find that really interesting um, as a, as a concept.

Glenn Hopper: Yeah. And that's a constant battle that I face is you know, finance is all is can very easily be seen as a cost center and that's it. Um, but um, and that's, you know, if you're, if all you're doing in the finance function is giving the historical report cards and you're just backward looking and saying, you know, we missed ebitda. This, I mean don't get me wrong, this is still a key part of the, of the job. But the expectation now um, is that yes, you can, you understand where the company is, all the details on the company you can report on, whatever variances there were, why you're missing the model, why you are, why sales are up this quarter versus last, versus last year, what the projection is, why you're meeting or missing projected. You still have to understand all of that. But you also now, and I'm sure you're hearing this from more and more guests, the expectation of a CFO right now is that you are a strategic partner to the CEO and to the rest of the management team and you're not just telling them what they did. And it's, I always think of it like this. The old um, CFO was very uh, uh Very deep knowledge in tax and accounting and all that and uh, but not maybe very broad knowledge and they could get a pass for maybe not understanding all the intricacies of the business. Now though, I think because so much of what has been historically uh, done by people in finance and accounting is automated and you are um, freed up to be, I look at, you know, things like expense reports, that there's a million expense management software programs out there and there's um, you know, even like closing, uh, reconciling bank accounts and all that. There's every, I mean any finance function you can tell me, I think has been automated. Um, so you know, with what you put in place, you have this historically, not to be insulting here, but mindless work of, of data entry. You know, if a person, if it takes eight hours to do it, a computer can do it in an eighth of a second, you know, um, so as you move away from that sort of data entry type task, the expectation is that, okay, well first off the question is, does that mean we can reduce headcount? And what I always argue is no, we can't reduce headcount. We're going to maybe have to spend a little more on headcount because we're either going to upskill our current employees to make them more forward looking and use, make better use of their time, or we're going to have to hire experts, be it uh, a business analyst or data scientist, you know, if you had a budget for that. But uh, and then it's always, that's the point of the battle of well now you're just costing me more. You're not adding any revenue. You know, you're, you're just making yourself more of a cost center. And so that's the battle is to say, well yes, there's, you know, you can look at it as an expense or an investment and then to make it, treat it as an investment, you have to give it an roi and you have to say, this is why I need the additional funds, this is why I need the additional headcount. This is why I'm not cutting headcount because this is what we're going to give you going forward. And it's that full data visibility that helps with your fpa, that helps, you know, maybe closer to a real time close, um, and giving them insights into the data. And it's for me at pretty much every company I've ever uh, been the CFO for, um, I have managed KPIs for the entire company because it starts with me wanting that data for my models and to help understand as in, as close to real time as I can, you know, what's causing a variance or if I need to know, hey, you know, cost of goods sold are going up this month. But if, to give me more of that real time view, um, and then to understand you know, utilization of the um, of the team to know where our billing is going to be. So I'm trying to get all this data and I think that because you're able to be a, an impartial reporter on um, the financial data then you also, you just kind of default to, you can be an impartial reporter on the sales metrics, the operations metrics, the service delivery, you know, whatever they are because you're separated from them. So you're not going to try to um, you know, make your numbers look better or you know, you just come in the kind of the third party and report on that. So you know, having all that data, tracking it if you can, if you're in an environment where you can sell that to the CEO and the management team, then you can kind of move away from that idea of a cost center. And then if they start seeing the value add that all this reporting brings, whether it's operational KPIs, um, or financial data, when it gives everyone a more clear picture and it becomes part of the decision making process, then you can start ah, kind of showing how it's a, um, how it's an investment and not a, not an expense. So really long winded answer on, on that. But it's, you can tell I'm, I'm passionate about it.

Hannah Munro: It's a really good answer and I think I love the fact. So I have this conversation a lot about if you are only valuing finance in the number of hours they are spending on entering data, then you're missing the point in my personal opinion of finance. And um, because you have these amazing talented staff that you know and most of them don't get in like let's be honest, right? Nobody goes into finance thinking great, I'm going to spend my entire life typing journals into a system, right? It's not the bit that they do it, they like it because they want to. I think most of them, people go in because they, they feel like the numbers make the business work and they want to be a part of that piece of, of helping maintain, you know, that piece. And there's always somebody that loves the satisfaction of making sure a balance sheet balances and doing a reconciliation and um, don't get Me wrong. I've met a few in my time, but I completely agree. The reality is, is that not just transactional entry, but reconciliation, fraud, identification, that is all being picked up by machines or computers in an AI, you know, the days of trying to know. There's even like, there's programs that send you emails to say, by the way, somebody's posted something that's posted to the wrong nominal. You know, we're getting away from that kind of oversight visibility and uh, you know, moving away from. We've hit transactional, we've, we're starting to smash oversight and it's now into that understanding piece which I think is, is both really exciting but scary for a lot of finance people.

Glenn Hopper: Yeah, yeah, it's uh, but you know, I, um, it's. I've been doing this for a long time and I, to me and to the finance people that I have worked with, anyone who's been an fpa, you hear these business analysts come along, you hear about data science and uh, all these new things and we think I've been doing this for, I've been doing this for 20 years. This is what, you know, you know, I'm using uh, you know, polynomial regression to try to calculate a utility bill and. But I'm doing it in Excel. So you know, so then whether it's tableau or you know, work that some people do in R or Python even, you think, um, that's great and that's uh, you know, a good value add to the business. Um, but I've been doing this stuff in Excel since you know, the early 2000s. So you know, to think that, um, I don't know, to me it comes naturally to sort of progress, uh, down this road as the technology has gotten better. And I know not all finance people are going to do this, but I'm enough of a geek that I actually went and took courses on R and not great at Python, a little bit of Python, but you know, learning, uh, and then uh, you know, refreshers on statistics and probability because it's uh, it's amazing the power that you can have even if you aren't in a full, uh, you know, super mature data environment. If you, as long as you have the data, it's amazing what you can do. I mean a simple linear regression is probably the first step in any data science process anyway. And we've all been doing those in Excel forever. So it's, I think that the skills are the same, but there are uh, newer, much more powerful tools out there. And if there is A weakness in finance, it is people who've been around for a while maybe haven't gone back to uh, learn those tools. But people who are just coming out of school right now, um, are very much, uh, you know, they're, they're getting trained in, even in undergrad in these tools. So uh, I think they're going to be driving a big shift to, towards more technology in finance and better, better data science.

Hannah Munro: But I also, I, I loved your, your, your comment earlier about. Actually it's not, it's not finance perceptions of itself, itself necessarily the issue. It's more the perception of the wider organization of finance. And you talked about being seen as adding value, being seen as a, uh, as not a cost center, but. And as an investment. So how do you manage that conversation? Because obviously in startups that's a hard conversation because, you know, money's tight, you know, Runway shorts. How, how do you have those kind of conversations? And how do you, how do you get the buy in from the wider management and execs?

Glenn Hopper: Yeah, that's a great question because it's very, I mean it's almost a daily, daily, uh, struggle, um, until you really, you know, settle in and show your value. So my approach to that is there's the old, uh, you know, the. Call it the old school CFO is, you know, the nickname is the cf. No, is just everything, Everything you go ask the CFO about.

Hannah Munro: I've never heard it said like that. That's brilliant, Glenn. Uh,

Glenn Hopper: and you know, it's just because, because you go to the CFO and you say, hey, can I do this? Nope, don't have enough money. Hey, can I do this? Nope, not in the budget. And that is. You're a roadblock there and you're not a partner. So to me, to become a partner whenever, whether it's in the budget or not, because we all know, I mean we want our budgets to be perfect and we love the idea of really forecasting everything out and getting it just spot on. Um, and certainly investors and uh, want that as well. But not everything always goes to plan. Plan. And everything changes so quickly. And it sounds cliche to say everything changes so quickly now, but look at the acceleration of technology. You know, over my career it's certainly gone, you know, steadily up. But now with AI and machine learning it is. Things are rocketing faster than they have in my career. So I think it's safe to say if you're doing even, even your annual budget, um, things that you back in depending on how, uh, how good Your company is at budgeting maybe September, maybe January. You know, the things that you thought when you finalize the budget could change significantly three months from now. I mean, look at Covid and the impact that that had on, on everyone. So I think that instead of just having that mindset where your first response is, no, it's not in the budget, my first response is always, so, you know, uh, sales and marketing is an easy example. So, you know, finance always has a hard time understanding a marketing budget. They're like, well, where's the roi? How do we understand this? And what's the old, um, uh, saying that, um, I know that half of my money that I spend on advertising is wasted. The problem is I don't know which half. I mean, that's the, the finance guys. Like, well, how do we know if we spend $20,000 in AdWords this month that it's going to work? You know, how do we know that that's effective? And certainly there are ways that you can, you know, show the effectiveness of advertising. But when sales and marketing comes to me and sales and marketing is in, this is a show for finance people. So I won't, I won't pull all my punches. But, um, you know, sales and marketing, typically, they're great, charismatic people that were going out and selling their creatives and as soon as you start talking numbers, they kind of just glaze over and they don't want to hear it. But if they want, if you're in kind of a data driven and a finance driven company and you want, you know, and they come to you with a good idea or what they think is a good idea instead of just a knee jerk opinion, it is, well, let's look at the data. Okay, how much is it going to cost to do this and what will that net us and help them work through, you know, a payback period or an ROI or whatever that, you know, let's look at it and really be honest about the model. What we know, what we have from our historical data or whatever we can glean from industry data or other people that have done this. And you know, whether it is a call center that says they need more employees, it's like, well, let's look at the utilization of your existing employees and let's see, you know, is it, do we actually need more employees or do we need to focus on scheduling a little better or something and just helping build models? Take your, uh, finance geek superpowers and use them for good instead of using, instead of just blocking them and, and telling all the other departments, what they can't do. So I think, and I'm real big on, uh, you can't discount experience and what good managers bring to the table. And it's great, they're going to have good hunches. And I always say, let's go from hunch to hypothesis. So you have this great idea and it worked at some other company you're at six years ago, that's great. But let's take what you think and let's put it into a model now and let's let the data show what your hunch is. Um, and sometimes, you know, models may not be 100% accurate, but it's a lot easier to make a decision if you're weighing three or four different projects. And you know, this one came from operations, this one came from sales and marketing, um, and you know, which project are we going to do with limited funds? Um, if you can have some data around those and show what the payback is and show what the ROI is and help non financial people to understand, you know, how we're going to evaluate these projects, then they see you as a partner and not someone to avoid and that you're not going to just ramble on going through every, uh, account in the P L and telling them we were off by 12% in sale, uh, you know, in travel. And that's because we had this con, you know, that that's not super valuable. You can send them a spreadsheet or a tableau dashboard or whatever and they can send. See that it's when you really add that value, that that's how you show that you're not just a cost center, that you are adding something important to the business.

Hannah Munro: Yeah. So demonstrate the value of data, uh, for decision making and then say, this is what I need in terms of systems and integrations to deliver this on a consistent basis is what we're saying.

Glenn Hopper: Exactly. Yeah.

Hannah Munro: Love it. Love it. Yeah. And to do what? It's funny, isn't it? I've seen a couple of different responses to integration conversations. Right. So I either get that, oh my God, yes, please give us a consistent data set across the organization from finance, or I get a do not touch my data. This is my finance data. It's much prettier than sales as data, uh, don't integrate. So I've seen both sides of that, uh, reaction which I find fascinating, like genuinely fascinating, because I've always thought that finance is only as good as the data that it pulls from the rest of the business. This, and there is A piece where there seems to be like a box in some organizations around the finance data and nobody from outside of finance can step over that line and deal with it. So be really interested in your insight and your view on that.

Glenn Hopper: Yeah, so that's actually my origin story. My, I was actually in sales and marketing in uh, uh, in my early career I was a product manager for a ah, this was back in like 2000 but it basically think of WordPress but back 20 years ago. So it was a cool product and it was. But um, anyway, just the quick version of how I got from marketing to finance was like I was saying sales and marketing people typically don't want to do the numbers. I had a product that was, I thought was woefully underfunded. So I just started getting as much information as I could could and like you said there was that silo. Finance didn't want to share anything directly with me. So I had to kind of build everything from just sort of from the ground up when I knew our budget was already sitting there in finance. And anyway started off uh, just as a side hustle being the, the de facto um, budget guy for marketing. And then ultimately I got poached by the chief operating officer who said I want one of those. So my, I started, so I started in finance, not rolling up in the finance organization. I was in on the operations side. And it was for what you just said. Exactly. Finance would not give anyone access to their data. And on this was in telecommunications and um, back you know we had all this customer premise equipment, very expensive and we had switches and all that and we had to a very tight budget that we had to manage. But if you can't um, see in, in real time what you're doing, it's very difficult. And then you know, uh, invoices would come in and they would go through the procurement arm and they would sit and finance and we're not seeing an invoice until um, you know, two weeks after it came in. And it was just a very uh, convoluted system. And ultimately we ended up getting into some trouble because there is a rather large invoice that we never got and ended up resultantly, you know, right before the annual board meeting and meeting with our investors. We had a huge budget miss because no one ever gave us a budget and we didn't have access to the system. So uh, it was like a, it was a low point in my career and I thought well I could always go be a high school teacher if this doesn't work. Out and we had just a whole lot of uh, jumping through hoops and getting everything ready and then basically ultimately having to give the mea culpa um to the investors and the board when we missed the budget. And I really thought I was about to be fired after that fiasco. But instead I actually got uh, started. I had our company's first business intelligence team and this was back in say 2004 or so and it was, it was basically just citizen coding. We uh, they wouldn't give us any kind of write access but we got them to agree to give us read access to the financial uh data. And we didn't, I didn't have any SQL people or I didn't know it so we had Microsoft access and we would just go pull this information from finance. But we uh, because we were able to get that information it completely changed um the, the whole department and we were able in real time and we got so good at managing to budget knowing exactly and we had long story uh what came out of that was we started having real time inventory that was updated daily um on where our image. So if there was equipment rolling around in someone's truck, if there was some in a warehouse, we knew every day what it was and we were able to put it, we were able to put in logistics and all that. So it was a really cool time and we built up, we, by the time I left uh, we had, they weren't all business analysts but it, you know it was crystal reports writers and whatever else back then. But we had almost 30 people in the group that were doing business intelligence across the company and that. So the reason I say that's my origin story is I knew the pain of not having access to that data and what a mistake it made. So ever since then every company I've gone to um, um I start at the company with I call it an ISO 9000 audit. Uh, uh, it's, I've changed it over the years and it's not as horrible maybe as a true like if we were trying to get ISO 9000 certified it would be um, more uh, more complex than what I have now. But really and it's it you get weird looks when you come into a company. The first thing I do, I want to see the end to end customer life cycle from lead prospect at one end. What data do we have on them? Where does it go where, why doesn't it flow through to another system? And what um, how can we get this data to use it in reports downstream? How can we get the Systems talking to each other and you know, so all the way anyway all the way through the project management, all the way through invoicing and then all the way through when a customer eventually turns off. And it lets you understand more about your customer acquisition cost, all the things that sales and marketing wants, your uh, provisioning times, things that operations needs to know. Um, and when you have that level of visibility you um, can, you just, you have to know where you are before you can effectively model out anything in the future, before you can explain variances in the finances. So I'm huge on this whole democratization uh, of data and let everybody like any to me, I mean not payroll data and obviously sensitive information but, but I love the idea of a data warehouse or whatever your data structure is, where anyone from senior management all the way down to frontline employees can go play around with it, uh, and you know, have access to data. Because if you're in a continuous improvement environment, you know, and you're encouraging people all, all through the company to use data and find solutions, then the people at the front line may have the best solution in the world. But if they can't get access to the data, they can't prove it. So that's my big soapbox on democratization of data. And it's very important.

Hannah Munro: You're absolutely preaching to the converted. So I can't tell you how many conversations I've gone into. And uh, so I'm a big believer on two things. The firstly, data should be as real time as possible, so correct as soon as possible. And um, that it should be open a bit like yourself and available to those that need it. And I think there's, there's always, there's always a protectionism of well, I don't want to give people reports because the first thing they're going to do in our is ask me questions. And I think there's two reasons people ask questions. Either the data isn't right and then we need to sort the problems or it's that they don't understand what they're seeing and why things are changing. And that's an amazing relationship building opportunity in my personal opinion for finance to go in and educate and build that knowledge. Because as soon as you empower managers and execs, you know, they don't go through finance training as standard is unless you teach them and educate them, they're never going to understand the data. And you're in that horrid loop of you know, I'm only, you only get to see what I give you, and then, you know, and then you ask me a billion questions which you should be able to find the answers to from the data I'm giving you. Anyway, so. Yeah, so. So I'm gonna get off my soapbox now and have.

Glenn Hopper: So that. I love that you said that because I'm one of the companies, um, that this was, it seemed like a strange industry to go in back when I did it. But we ended up selling the business to a, ah, Leonard Green, the big private equity firm. And now every private equity firm in the world I think is trying to get into this. But I was in the car wash business and it was a regional car wash chain. And um, so when I got there we, they didn't have a CFO before me. And when I got there, the idea was we knew that we wanted to either sell or get funding and grow this thing. So we had to professionalize our finances and we also had to make the company run as tight as possible. Well, so the car wash managers, very blue collar, very good at what they do, very technical. I mean there's plumbing, mechanical, electrical, all kinds of things they have to know. Finance wasn't one of them. But we wanted to drive unit level finance as uh, you know, to get our EBITDA and everything, um, as strong as we could at each unit level. So we built a fairly complex profit, uh, sharing plan where they had their budget for their unit and then they had, you know, tiers above it. If they beat the budget and they could, basically it was driving to an EBITDA number. But you know, they could beat revenue. They could drive up the average wash price, you know, whatever they could do to do it or, and obviously cutting expenses. Well, so I would go to each store and there were 12 or 15 of them and it was, but every month to start and then we were able to move it. But I would sit down with the manager and we would go through their P and L. And these are people who've never taken a finance class. But when they know that they're going to get paid based on their performance, uh, to the, to the P and L, well suddenly everybody turns into, you know, I feel like I'm talking to auditors by the time, uh, and they're like, wait a minute, why is my cost of goods sold on this product higher this month than it was last month? Let me see those invoices. And but the great thing was, I mean by the time we sold that, we had a 63% EBITDA margin at the unit level. And that was in the, and the guys would compete with each other over this. And it was, I've, I've been at other companies where the employees don't need to know our financial performance. That's, that's just going to worry them, um, if we have a down month or whatever. And I go to the other side of it, I think everybody needs to know where we are because it impacts whether they decide, you know, something that may be in flush times, may um, not be a, ah, frivolous expense. But if you're, if things are lean and you're over your budget in whatever category, you know, maybe you don't need to fly the whole team out for a team meeting this quarter. You know, if they don't know where they are on the budget, they just assume everything's infinite. So I like everybody having visibility. And that's the same can be said not just for the financials, but for all the operational specs too. Like if it is our utilization is down this month or you know, whatever metric you can think of, if our pipeline is not uh, where it was, then it holds everybody accountable when everybody sees the information.

Hannah Munro: Yeah. And it also, I feel like one of the big things I love about integration projects and connecting the dots is that it gives you a shared agreement over what is a data, as in how do you define your data. And that's one of the big challenges I find is that sales will think a product is literally the physical item, finance will think it's something different. Um, and then there's, there's some sort of debate around actually what is the price for that product supposed to be. And it's always, that's always fascinating. Right. I've been in so many conversations where clients are going, you we're charging what for that. And it's always really interesting. So I think there is, there are so many. I think one of the, well one of the hardest things to think about connecting the dots and data is actually creating those shared definitions and um, making sure that everyone is looking and working with that data in the same way and understands what they're entering as well.

Glenn Hopper: Yeah. And that, so I think of a single source of truth. And if you have non integrated systems that aren't talking to each other, you could have customer information in the CRM, customer information and you know, whatever other tools, customer information and accounting and they don't line up and maybe, you know, yes, uh, we certainly should be invoicing for everything that we do. But if there are discrepancies between the project management or whatever we're doing to track our material items and what's on the invoice. It's, you know, which one of these is, is right. And then in a non integrated world. And finance always defaults too. Well, it's finance, we're always right. We, you know, but um, it's, but it's tough when you have. So if you're trying to do KPIs and you're um, trying to work with data, uh, and you know that there's information listed maybe in three different systems and all three of them are different. It's like how do you define what is the source of truth? And then there can be arguments and, and disagreements of, or, or confusion about well, which one is right. But then if you can have all the systems talking or you know, it's great if you have everything in a single erp. But really, I mean for every business, even if you have an erp, there's going to be offshoot things that go, you know, nothing out of the box, just has all the data. So it's very important to think about what is the source of truth and um, you know, how clean that that data is and which data to use when you're modeling and building all this out.

Hannah Munro: One of the things I find really interesting about this conversation is you almost see part of your CFO role as ownership of that revenue data, that sales, that pipeline, that visibility. So tell us a little bit about how you see the CFO's role in that revenue generation, that revenue support piece.

Glenn Hopper: Yeah, so my ownership on it I think comes from the stage of businesses that I work in where I'm typically in the investor meetings more than the Croatia is um, and I'm the one who's having to explain it. So if I need to explain it and I don't have, you know, I could maybe get prepped from sales on where we are in the pipeline and all that. But it's uh, there's a lot of shoot the messenger and if I'm going to get shot over something, I want to be sure that I'm at least uh, understanding what I'm getting shot uh, for. So I mean, I think, I mean everyone on the management team, we all know that top line is, you know, we have to um, uh, grow that. And that's, you know, top line's sexy. And uh, that's what everybody wants to hear is we grew revenue, you know, by X percent this, this year, this quarter, whatever. Um, but the, one of the reasons, I think the pandemic and sort of where we are this year. One, um, of the reasons I'm very interested in revenue is because what is the quality of the revenue we're getting? Different products have different margins and if you don't look at it from a, the full financial picture of revenue cost, you know, and the associated expenses and you don't know what your true margin is on something, you could be growing some very expensive product that doesn't add much to the bottom line. And so if I'm going to add my, if I'm going to add value to the company, it's going to be well, let's see you know, what our revenue trends are, which products are moving and what our pipeline. Uh, I love the pipeline obviously for forecasting but once the revenue comes in, is it the right revenue and we're able to, by working with sales you can say hey, this product is great and it's expensive but here's all the costs associated with it. Um, so it's not really a great product. But also at the company where I am now, there's a product that is super high revenue and it historically has had a lot of costs. So the margins are much lower than a lot of our other products. But we're getting enough data now that we're building out a model and I think maybe by the end of the year, maybe first of uh, next year, a lot of the stuff that we used to outsource and have to pay for is going to be um, done in house and at, at a much m lower cost. So if you, if it's just sales thinking about oh well we sold that product and we got this revenue, great. I have, I'm, I get bonused on you know, my commissions and everything are on revenue. But you're not looking downstream. The company could actually be less healthy than it was on lower revenue. So I think it is very important and then not that I'm the one as the finance guy that I'm going to make these decisions but I feel like it's incumbent on me to provide this information so that the full management team can make the decision about what we need to do.

Hannah Munro: No, absolutely. And um, I think it's always interesting to speak to CFOs that uh, not only get the non financial data but actually come from a non traditional background so that haven't come in through that traditional route. I see and feel a different conversation when I'm talking about data and the power of it. It's quite interesting actually to see that shift. So you're very much a data driven cfo. And obviously are as passionate, I think, as I am. It's very rare I meet somebody that's as passionate about data and analytics and what, what it can do, um, for businesses. I am. So what are your top tips for those, you know, from the CF, the desk of the CFO? What are your top tips for other CFOs about how do they make that transition? How do they become that data driven, um, um, and data friendly? I'm not sure if that's the right term. Cfo.

Speaker B: Yeah.

Glenn Hopper: So it's, um, I think it is you. It starts with something simple of realizing, you know, what your role could be in the broader team. And if you start to think of, let me get out of my silo of just finance and what it, what finance could or should do. And it's not just, I'm telling you how you spent your money and, you know, how you compared to budget and how you compared to last year. It is, I have a skill set that is different than what the rest of the management team's skill set is. And they bring, you know, uh, sales leaders and operational leaders in for something specific and they're bringing you in as a peer to these people. So what, um, what can you do other than just being, you know, providing the report card every month? And then I think it's, for me, really serious about that, uh, ISO 9000 thing. It's just, let me get out of my office and walk and talk to, uh, I mean the first, you know, the frontline people and see what they're doing and understand the processes. And I want to know that everything from end to end, and I think by understanding the process of your products and, and customers, what they go through, then it gives you that bigger picture of what the company's doing. And then in seeing that, um, then you start to see this data that's out there and then keys on what, uh, what you could do with that data, how it could improve your models, how it could improve your financial reporting. And I think, I don't know, I'm going through sort of steps rather than tips. I think that the, the tip is, um, to be, uh, intellectually curious and know, understand your business, understand what's going on in it, and see all the data that's out there and figure out what you might be able to do with it. Um, but if you don't know the technology, you know, you just see this data and you don't know the side of, well, how would I gather it, what would I do with it? Then it's kind of pointless. And I'm not saying you've got to be the chief data officer, but if you have one at your company, I'd recommend talking to him and understanding where all this, uh, all this information is. But I think that we can't. And I don't think, I don't care what you're doing. If you're, you know, sales and marketing thinking about how, how much machine, um, learning is being used in marketing decisions now and whether, you know, it's ad placement or a b testing to determine what ads are going to run and everything. I mean, no matter what you're doing, you can't just be an expert in that. You now have to at least have a working knowledge of what machine learning is, how it's being used, what products are out there, what can be done internally versus externally. So you, uh, I'm one of these crazy, uh, like lifelong learner people and I'm always taking, uh, additional classes. But I think it served me well because then I know, okay, in a perfect world, what would be possible right now and what's not. And then, you know, figuring out where to be in, in the middle. Um, so I think being intellectually curious, understanding the technology and understanding where you are in your own company's data maturity and what's available, I think those are the, the three steps to get started. And then I guess I would add to it, um, the way to think about data. And if you're starting out your data science journey, I think there's the three things I always go back to is, okay, let me audit this data I have and see what it tells me. So that's the descriptive phase of data science. It's like, this is where we are right now. Um, and then, okay, I know this about these customers. I know that this is when customers renew their contracts. I know this is when customers turn away. Let me see if I can find correlations between when they turn away and something that happened before. And so that you kind of get this descriptive picture of your customer and your products. Um, and then from that you can take that data and apply it to what you're projecting into the future. And you've got these predictive models where you can say, okay, well, based on this, you know, our churn percent was this. And if this happens, you know this is going to be the result. And this product is growing, going up, this product's going down. Um, and you, but you can take these trends and extrapolate them out as finance people have been doing forever, and make you know your, your plans for the future. But then where data starts to be really valuable is you've got your descriptive, you did your predictive of what's going to happen and once you see that that predictive comes true, then you can do the prescriptive analytics, which is where you say, okay, if a customer has three, um, you know, support calls in a month, they're probably going to turn away the next month or, you know, whatever the metric is, then you can start making prescriptive decisions. And this is where you really become a partner with the rest of the, the management team is, okay, here's our list of people who've met this criteria are potentially going to turn away. Let's come up with a way to stop them. And it's. You start to, that's when you really start to see the power of data. So I think having a picture of where it's going, understanding the technology. And once you start, and also if you find small wins, it's easy to get hooked on it. And you're like, tell me more, give me more data. What, you know, what else is out there?

Hannah Munro: Yeah, it does get a little bit addictive getting the data hit, as I sometimes refer to it. And I find that a lot with, um, when we implement systems and we give data to the, like, the wider organization. As soon as you start giving them data, they're just like, feed me, give. Yeah, um, and I love that. Right. For me that either that means that what I'm, we're doing is working, you know, and then you've got to control the. Feed me. You know, you've got to make sure you've got some appetite control. Um, but for me that's, that's a really exciting page. Like you say, you get that you get addicted to understand not just seeing data, but understanding it and it. And like you say, you get really curious. It's, it's amazing what you can find once you have access to the information.

Glenn Hopper: Absolutely. Yeah.

Hannah Munro: So, um, so what we haven't talked about and I think, well, we definitely need to mention is your book. So tell us a little bit about your book. Um, and um, you know what, what, what it's about where people can find out about it and you know, if people want to learn more about data and, and um, the, the Deep Finance piece.

Glenn Hopper: Sure, sure. So the, uh, the book is called Deep Finance. It's Corporate Finance in the Information Age. And it's um, available on Amazon or Barnes and Noble or all your fine booksellers carry it. Um, it's I almost came into this book accidentally. I don't um, I think in another universe the, there's a version of me that went on to become uh, a uh, science fiction writer because I think that's, that's a secret dream of, of mine. Um, but you know, finance, uh, I went that direction instead of. But I always. And as a former journalist my way of solving problems is usually to take, you know, it's something that starts out as uh, sort of keeping notes for myself and just sort of making sense of something and I, I just find I need to create a narrative around it. So I've always been a blog a lot write for various different publications and I've always done that and it's, I do like writing but it's also, it helps cement information for me, helps me work through things and understand them better. And as I have been on this um, automation journey and uh, everything I've been through is especially once I started diving into the machine learning realm and really once the technology started to get there where you could do so many more cool things with data, uh, I found myself writing all the time about it like I'd find some new trick and I'm like let me tell you how to do bootstrapping in R. And um, you know, and, and I don't know who's reading that but for me it's like well I've just generated this content, I'm gonna, we're gonna publish it somewhere. But um, I, after uh, going through some very similar problems at different businesses I thought you know, I have a library of information that I've put together and a lot of this what I'm seeing is the same problems over and over and I thought I could probably write a book pretty quickly on this just taking the information that I have and uh, I started down the road and I didn't realize it at the time I thought the book was for companies of all sizes. But because I've spent so much time in the startup and in small, medium sized business realm it's really focused on them because we have different issues than someone who's got a team of business analysts and they've already got, they got higher data maturity. So very much written from point of view um, in that it just think I walk through the problems of you know, very low data maturity to you want to become a data driven company. Here's uh, I go through everything from the basics of machine learning to this is how you can or uh, or this is who's on your data science team. Ideally again we never get, you know, in small businesses we never get ideally what we want. But you then say okay, well we need a very small team that can do these multiple things. Um, I even go through um, Agile development because I think uh, Agile that I've used before when I was managing software, um, developers was really an amazing project, uh, management life changer to me. That move from going you know, waterfall, trying to design the whole thing at once to very small quick wins where you're getting instant feedback and that helps if you can do something where you're getting this instant feedback and small wins. It helps people get confidence in you and it helps you able to do more and it helps bring people to your team. So, so I go through Agile, I go through um, getting you know, how you get buy in from reluctant people in the company who to our earlier conversation just see you as a cost center and they're saying you want to do what now and how much is this going to cost and what's it going to give us. So I talk about how to get buy in from the team members and then really explaining everything about uh, how to get from A to Z and what it looks like. My favorite part of it is what it looks like on the other side. And like you know the examples I gave on turn or guessing what a product is going to do or guessing which product to sell. And um, so the idea is it takes someone who maybe has never thought about the um, modern analytics that are out there and it starts pretty basic level and then goes, goes through and I try to keep it high level because it, it could get really boring and really deep. First if you're trying to explain how a random forest algorithm works or something, you know, um, so uh, yeah and it's a pretty short book, it's um, about 50,000 words which is way um, shorter than my space opera would have been. But it's uh, you know, and the idea is it's just a, it's a roadmap for going from zero to fully data driven organization.

Hannah Munro: Amazing. So for anyone that is interested I will pop the link in the show notes along M um with a link to uh, Glenn's profile because it was actually a lot of your LinkedIn posts that was uh, one of the triggers for getting you on the podcast. So you always share some really interesting content. M so I'll pop that in as well. So um, thank you so much Glenn. Thank you to you. And um, for those of you that are watching this on YouTube, you will see Glenn's dog joining in the conversation as well. I think she gets a shade where we start talking about AI and machine learning, quite frankly, that most people do. Um, but thank you so much for joining, Glenn. It's been an absolute pleasure. I'm so wonderful to speak to somebody that's as passionate about data, um, as I get. You know, feel free to jump in your soapbox anytime soon. It's really great conversation and I really appreciate you coming on the show.

Glenn Hopper: Yeah. Thank you, Hannah, for having me.

Hannah Munro: So, um, if you enjoyed this episode, um, everyone, please do, um, please don't forget to, um, pop onto your favorite podcast platform. I'm really not that fussy. I don't mind if it's Apple or Spotify and leave us a review. Would love it. If you feel like I missed a question, if there's something burning that you feel I should have asked or a topic you want us to cover, then please do reach out. This is very much your show and I want this to be as interactive as it possibly can. Um, so send me a message, connect with me on LinkedIn. I'd love to hear your thoughts. Um, and thanks for listening, guys. I'll see you on the next one.

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