How CFOs Can Master Data Governance to Scale Their Companies with Nathan Bell
This episode of Future Finance explores how organizations can better align their finance and data strategies to harness the power of emerging technologies like AI. With a focus on data governance, analytics, and the integration of advanced tools into financial workflows, hosts Paul Barnhurst and Glenn Hopper discuss both challenges and opportunities for finance professionals in the digital age.
Nathan Bell, is a seasoned executive with over 20 years of experience in steering organizations through complex financial and digital transformations. His career spans roles in credit management, banking, venture capital, and corporate finance, with expertise in data governance, analytics, and strategy. Nathan's passion lies in helping finance leaders leverage data to drive decision-making and growth.
In this episode, you will discover:
The importance of getting your “data house” in order before implementing AI.
How self-service BI tools can both empower teams and create challenges in organizations.
Why data governance and stewardship should be a co-led initiative between finance and engineering.
The role of finance leaders in ensuring data literacy and ownership across departments.
Practical insights into scaling finance operations for mid-market companies looking to grow.
As AI and automation tools continue to evolve, organizations must focus on integrating these technologies into their workflows in a way that complements their existing data strategies. Nathan emphasized the critical role finance leaders play in ensuring data quality and ownership across the organization.
Follow Nathan:
LinkedIn: https://www.linkedin.com/in/nathan-bell-1038662/
Website: https://www.vai-consulting.com/
Company: https://meetvirginia.io/
Join hosts Glenn and Paul as they unravel the complexities of AI in finance:
Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii
Follow Paul:
LinkedIn: https://www.linkedin.com/in/thefpandaguy
Follow QFlow.AI:
Website - https://bit.ly/4fYK9vY
Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.
Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.
In Today’s Episode:
[01:58] - Generative AI and Finance: Myths vs. Reality
[11:17] - AI's Dependency on Clean Data
[12:36] - Guest Introduction: Nathan Bell
[20:02] - Bridging Data, Finance, and Storytelling
[25:25] - Foundations of Data Governance
[29:54] - Challenges with BI and Metrics Ownership
[32:33] - Scaling Finance Teams for Growth
[42:38] - Fun Segment: Travel Stories
[45:29] - Closing Thoughts and Call to Action
Full Show Transcript
[00:01:31] Host2: Glenn Hopper: Well, welcome to Future finance. Paul and I were just talking before Paul smashed the record button there. And Paul, this week you and I made, I guess probably me more so than you. I live and breathe generative AI and applications in finance and I'm, you know, traveling around. It's all I talk about generative AI in finance. And you know, everybody's excited about it. Anytime I do a webinar or a speech or a course, people come take it. But then when I talk to him. So far I've found one finance group that has fully leaned into generative AI. But they have a policy, push it out to all their employees, encouraging them to use it, getting feedback out there, you know, driving usage of generative AI to get more efficient. But other than that, people are not using it. Are you seeing the same thing? Are you talking to people about it as much as I am? What are you seeing out there?
[00:02:25] Host1: Paul Barnhurst: You know, funny enough, I had a dinner Tuesday night where we discussed AI with about 15 senior leaders, CFOs, you know, controllers, different things. And one interesting part of the conversation is someone asked me, when is it going to be able to build models for us? Like, the answer is not right now. It's, you know, can it do this? It's just not there. And I talked to Liam Bastick who wrote a book on AI and he's like, it can't. I've tried every, you know, he's spent months like it just doesn't build a real model yet. I'm not saying it's not useful. So that was interesting. I think that was one area where some of the stuff I wanted to be able to build my modeling. That's what you want it for. It's not there yet, but we talked about how it was bad in math, but there are definitely some that had use cases. Most were trying to figure it out, but there was one who was saying, hey, I use it to get a list of my customers to target because they focus on K through 12. Since it's been trained on all the internet data they can get, every school district will tell me about the demographics of the school district and narrow down a list. And so that was one, you know, another was sharing. We had a consultant come in for their audit and prepared 12 memos for their accounting team in one day, something that usually would have taken weeks and they were ready for their audit. Great use case. The CFO was just thrilled. He's like, I thought it was gonna be a two week engagement. I was paying this guy and he cranked out like 12 memos in one day. I'll hire him again. Like, he was awesome. And so there are we're seeing that snowball and individual use it, I think real company policies, These tools. We're just not there yet in mass adoption. You got the early adopters in pockets. That's at least how I feel about it. Is that kind of you?
[00:04:02] Host2: Glenn Hopper: Yeah. You know, and like you, I mean, I'm thinking of the most recent groups that I've talked to and yesterday I wasn't even, I was just a guest here. It was nice to be on one of these and not have to talk, but the AI finance club that Nicolas Boucher's group, I went on and did that and it was all CFOs. Some were fractional, some were full time, and we had breakout rooms and we're talking none of them were using it at all like so for me. Well, one guy said he was using it for company research, distilling down leads, identifying, you know, so but in the finance function, not at all. And I just think for me one of the issues is it's not integrated into the workflows. You've got to come out of Excel. I mean, and you've got to go over to ChatGPT or Claude or really, I'm mostly. I'm just talking about ChatGPT. Now, the other Claude is having problems these days. They've got more capacity or more. More demand than they have compute. And they keep locking up there. So for several weeks since they rolled out computer use, Claude's been slow. But, you know, the idea that it's something new in the workforce. And then to your point about building a model, it's not if it can't build a model and it can go do some cool analysis and some what ifs and scenarios and change it up. But if you can't package it up and take it with you. Well, what if you need to make modifications to it? So I think the problem now is it's still this kind of standalone tool. It's really cool over here, but people haven't figured out how to keep sort of a memory and keep, you know, carry it through their normal workflows.
[00:05:39] Host1: Paul Barnhurst: I definitely think that's part of it. And I also think most people I mean, I'll include myself in that group and I'm trying to get there are afraid of Python. You know, I'm starting to learn some. I've been playing with the Python in Excel, and I think most people aren't willing to spend 30 bucks on copilot. Well, I can use ChatGPT. And on the whole, I'd say ChatGPT is better than copilot, but copilot is integrated to your workflows. You have it in outlook and it respond to an email. When you're doing that formula, you can just hit it and ask the question. You're doing a word document. You have it create the example right in word, and it's better than Gemini. At this point, I think all the tools are better than Gemini. Right? So I would think that's, you know, an area, but people are a little, I think, afraid, especially at an enterprise level, to spend 30 bucks per employee. That adds up in a hurry. Right. You got 100 employees if you want all of them to have it, you know, that's an extra, what, 30,000 a month?
[00:06:37] Host2: Glenn Hopper: Yeah. So. So OpenAI teams' accounts are 30 bucks a month. I think the enterprise version is 60 bucks a user a month. So that's a lot. That's serious. That's like a PwC level investment. You know, they went all in partnering with them and here's my long term bet. Microsoft's going to get there. They spent, you know, their, what, 15 billion. Yeah their 15 billion or whatever invested in. Well more than that between OpenAI and their other investments they've made.
[00:07:07] Host1: Paul Barnhurst: Well they own what? About 50% of OpenAI?
[00:07:09] Host2: Glenn Hopper: So it's something that you know.
[00:07:12] Host1: Paul Barnhurst: It's kind of a weird arrangement because of the public private. Yeah. But they have a big stake in OpenAI.
[00:07:17] Host2: Glenn Hopper: Yeah. And so what's going to happen is Microsoft's going to get their. So all the stuff that we pride ourselves on, all the model building and the formulas and the, you know, complex stuff that we do, people are going to be able to go into Excel in their normal workflow. And instead of doing their Power Query and Index match and, you know, whatever nested ifs and all the complex stuff that we geek out on, they're just going to type, do this in Excel is going to get there. And when it's like this is for the board report, it's going to be connected. Bump them over to PowerPoint and it's all going to be integrated. I think Microsoft's going to get there. And so I'm still bullish on Microsoft long term. But they're not there now. And ChatGPT is amazing. But you know people to use it. They still have that whole trust thing. And it's not in the workflow. I could see problems.
[00:08:06] Host1: Paul Barnhurst: Aren't great at math. Right. And so you're using the coding in other parts of the tool. And sometimes people still may not trust it when you ask it to give you the details. You still may not get what you want unless you're getting code. And so I think they're, you know, finance people were notorious. We want to understand our black box.
[00:08:24] Host2: Glenn Hopper: And you have to think about audit. You can't just explain. Well, we just fed it into the black box.
[00:08:28] Host1: Paul Barnhurst: Yeah, the machine did the accruals. Trust us. It's right. Okay. You have a material deficiency. Accountability. That's not what you want to write. Yeah.
[00:08:37] Host2: Glenn Hopper: So I mean short term here's the thing. If you're trying to figure out ROI, if you, you know, short term there's people who do what I do, which is we can integrate this into your workflow, but it's a very bespoke design and we're going to do it and it's going to take weeks or months.
[00:08:52] Host1: Paul Barnhurst: Basically building an agent type of thing.
[00:08:54] Host2: Glenn Hopper: Yeah. That goes into your workflow. That's great. But you know, and if you've got a data science team, if you've got machine learning engineers in your company, they're doing this already as well. But if you don't, you've got to hire outside consultants to come in and build it. And it's like, well, what if we build all this? And then Microsoft finally nails it and we've dropped six figures on getting this implementation in, and then it's all all for naught. But I still don't know, I mean, if the company is big enough, you could make that investment and have that much of an efficiency gain and still get ROI, even.
[00:09:26] Host1: Paul Barnhurst: If you're a big enough company and you decide to do like, you know, PwC's gone all in. I think in some of these others you can do it. The small companies, you can pay for a couple accounts and get benefit. It's the question of do they feel like they're getting enough benefit? And it depends on what you're hoping it will accomplish. I think in the hardcore analysis, unless you're really using Python, it's going to struggle if you're wanting it to help write formulas. Fabulous. If you're wanting some basic graphs, a little bit of scenario, but it doesn't, you're right, it's not integrated in the workflow. There's a little bit of a gap there, and there's concerns around giving it your data unless you build it in. So I think we're just in the growing pains like the early stage of the internet. And I think most people are at least experimenting in some way. But what's so strange is the majority. I would have never guessed this if I told you two years ago most people would use AI to help with creativity and generating ideas and the stuff that we all thought we'd be doing more of because we had AI and it would take care of all the drudgery, so to speak. You probably would have looked at me like, what are you talking about? But that's really where we're at right now. Which is ironic. Not that you can't use it for a lot of workflows and technical stuff, but that's what they're finding most people are using LMS for. The average user, not necessarily companies to build workflow, but was the average thing. People are using open AI or cloud for brainstorming. It's creativity. It's an Excel formula. It's not do a detailed analysis for me or this or that. So I find it interesting.
[00:11:03] Host2: Glenn Hopper: You know, and I think that goes back to it not being integrated into our workflows and into our data. And that actually goes, you know, so this week's guest, Nathan Bell, talking about data and analytics. I mean, I think that maybe one thing that could come out of that is if you're going to lean into AI, you've got to have your data house in order. And how many companies actually do, regardless of your size, enough that you could hook into it, use the AI and everything, and maybe, you know, this is that final last call for if you haven't done, you know, gotten through your digital transformation or your data maturity. You know, now is the time. Because once we do have access to generative AI, more and more, it's only as good as is the data.
[00:11:49] Host1: Paul Barnhurst: That's in it. Really get it truly integrated into our workflows at a level where it's common. You're going to be left behind if you don't have good data. And to paraphrase George Box who said all models are wrong, some are useful. I like to say all data is wrong, some is useful, some more than others right? Or the other one is no. Data is clean, some is useful.
[00:12:12] Host2: Glenn Hopper: Yep.
[00:12:13] Host1: Paul Barnhurst: Right and wrong. That's kind of more I kind of paraphrasing and having fun, but you need to make sure your data is useful and that you've done the work that a machine can analyze it and provide real value. If not, you're going to get left behind at some point. It's a question of when, not if.
[00:12:29] Host2: Glenn Hopper: Absolutely. Well, should we go ahead and bring Nathan on and then we should.
[00:12:34] Host1: Paul Barnhurst: Go ahead and let that roll.
[00:12:36] Host2: Glenn Hopper: Welcome to Future finance. I'm Glenn here with Paul and my good friend Nathan Bell. Nathan is a seasoned executive with over two decades of experience steering organizations through complex business, finance and digital transformations. His expertise spans a wide range of areas, but all straddle the intersection of technology, data analytics and finance. In other words, he's kind of like the perfect template for future finance guests. Throughout his career, Nathan has been at the forefront of leveraging data and emerging technologies to drive growth and efficiency. He has a deep understanding of financial operations from FP&A and accounting to banking and venture capital. He's also a proponent of data driven decision making, specializing in data governance and master data management to help organizations effectively harness their data. Today, Nathan will share his insights on how finance leaders can navigate digital transformation and implement effective data strategies. His unique blend of financial acumen, technical knowledge and strategic vision promises a wealth of valuable information for our listeners. Nathan, welcome to the show.
[00:13:40] Guest: Nathan Bell: Thanks. Glad to be here. Thanks for having me on, guys.
[00:13:42] Host2: Glenn Hopper: First question, most importantly, data or data.
[00:13:48] Guest: Nathan Bell: Can you throw that curveball at me first? Huh? I am a data person.
[00:13:54] Host2: Glenn Hopper: So I think internationally it's data. But in the US we say data. Maybe particularly in the South, I don't know. I sort of use them interchangeably, like Caribbean and Caribbean.
[00:14:03] Host1: Paul Barnhurst: I'd say data, but sometimes I'll say data.
[00:14:06] Guest: Nathan Bell: Honestly, I don't think I've ever said data. I think my business partner actually says data, but it's data for me. Maybe it's because I started watching Star Trek when I was a kid, and I remember that character data.
[00:14:18] Host1: Paul Barnhurst: That's probably why I mostly say data. I watch it too.
[00:14:22] Host2: Glenn Hopper: So data it is then. And your career has been kind of marked by work and finance, data analytics. And now I will get to that always. Because as Paul knows, I can't go five minutes without mentioning it. But you and I have very similar backgrounds and I want to hear from your standpoint, finance, data analytics all being sort of joined at the hip through your career. How have these worked together for you throughout your career?
[00:14:49] Guest: Nathan Bell: Yeah. It's interesting. I look back on it and at first glance or blush, it doesn't make a lot of sense when you kind of see where I've been and what I've done and where I've worked, but there really has always been a connective tissue through it all. And it does go back to that technology, data analytics, storytelling, those types of things. I actually started out and within my undergrad being business computer systems, I started out as a coder and it was through the business school. So it was kind of like an MIS degree, if you will. But it was like a business degree with a computer science minor. I was actually coding cobalt for TransUnion, the credit bureau in Chicago out of school. And I think you've heard this story before, maybe from other people. But I watched that movie Office Space, I think, a little too many times, and decided after the 20th time that I don't think I wanted to be that guy and didn't know where I was going to go. But I thought at the time that, well, an MBA is a good choice because it gives you a lot of options. So I actually started getting an MBA in finance, and it kind of was a natural progression for me for being a business person and then working at TransUnion. One of the things that TransUnion actually led me on that path was understanding how credit reports work, how they're built. I actually from my job there was in charge of all the data capture and gathering from all the different institutions credit cards, credit unions, banks gathering that data and using it.
[00:16:17] Guest: Nathan Bell: It was called the Metro two format at the time. That's unique to the credit reporting industry and making sure that it was structured right so that it can ultimately go on the live database. When people pull the credit report, it was showing properly. That was my first kind of intro to the whole process. Most people only ever see that final product, right? You see a credit report from you, but you don't know how the heck they're getting all that information. Put it together in a format and doing it correctly. So that was my first kind of, you know, entry into data. But I decided that finance has always been kind of near and dear to me. And I jumped into a really unique role that combined both my credit, background and data to the Indian finance I was at the time, you know, going to night school at the university in Chicago. And I became a credit manager for a small commercial leasing company. And this is kind of you'll see where this is going to tie in to what we're talking about here. And I learned I was very fortunate. I learned I shared an office with the founder, the president of the company, and my job was to tie the financials of businesses that were seeking capital equipment. It was a lot of like yellow iron type Leasing and stuff. We eventually did some software, but a lot of it was CapEx, commercial leasing, and my job was to take all the financials from these small business owners, medium sized businesses.
[00:17:35] Guest: Nathan Bell: Didn't have financial literacy. I was lucky if they had a CFO or a head of finance. It is usually like a bookkeeper and I take all their financials, combine it with the credit report, and I would put a credit memo together and craft a story, if you will. And then I'd have to go pitch it to the banks because we were just a broker. And then I'd have to go get the best rate. And then, you know, of course, our sales team would then close that person on that commercial lease. I had to learn a lot about the leasing structures, but my job, and I was lucky to be trained by my mentor at the time, was to paint a really good story and get that bank to buy off on that commercial lease. And again, that's where I was very lucky to learn that it's not just about gathering the information and putting, you know, the individual owner's credit reports together, their personal financial statements, together, the financials of the business, but put it together in a way and get the best rate, you know, and having these relationships with the bankers and the underwriters, that actually led me to become a commercial underwriter myself in banking. And that was when things really started to level up for me and my understanding of FP&A. And I don't know if you two are familiar with the banking industry, but I was a senior credit underwriter or a credit analyst when I first got into there, and they have software called Moody's.
[00:18:49] Guest: Nathan Bell: Most of them, I don't know if it's still there, but you would take the financials from the clients and you'd have to put them in there and compile them in the system. And it allowed you to unify and standardize. And this is, I'm dating myself. This is, you know, 15 years ago, I think was the last time I used it. But the process itself of gathering the data and putting it together into the system and normalizing it and then being able to present a story with the outcome. It allows you also to benchmark in the system against other industries of that, you know, potential loan. And then from there. I don't know if you're familiar with the Five Cities of credit, but I learned how to present and underwrite a company. You've got the financials, you've got the personal information of the background. You've got to go through the five CS, which is like character condition. I might not even remember all this collateral capacity. You know, those types of things and put that all together in a cohesive, in some cases 40 or 50 page story that did include the dropped financials. Right? And you'd pull stuff out and highlight it. You might propose some covenants, but, you know, ultimately led to me to becoming chief credit officer of a commercial bank. We were OCC regulated, and I chaired the board loan committee.
[00:20:01] Guest: Nathan Bell: And being in that room and being the one that was kind of overseeing my job was to protect the bank's capital, but overseeing the credit analysts and underwriters and helping them understand not just how to put the financials together, you know, and cash flow statements and the balance sheet. But how to read? I always had an uncanny knack to look at the numbers and see what was really going on with the business, if you will. And there's always a story behind it. And then being able to ask the right questions and decide, should we make this loan yes or no? If we do, what rate should it be? And then what covenants should we be putting in there to really track? And if you do it right, you're a good partner to the business. Right. So that was my kind of I would say random path. But it does make sense when you put together. And then after that I got an offer to be CFO. Employee number two of a company in Portland, Oregon. I'm actually located in the Portland metro area here. And it was employee number two. There I got to, you know, put all of those learnings to work, help them raise money. Had to go through that whole series, you know, dog and pony show in the Bay area. And then using my background in banking helped get and secure debt financing to avoid having to give away capital. And a lot of folks don't know that that's a better route.
[00:21:15] Guest: Nathan Bell: If you understand the banking world and giving away equity in your business. But you get and you have to know the world. You have to be able to talk the lingo, present your financials in a story that makes sense to the bankers and all of those types of things. But yeah, I was employee number two there. Built the finance accounting team for them, ultimately took over operations facilities and then built their data team. And it started, as you might have heard this before, it started as an FP&A financial, you know, planning and reporting. It was never truly FP&A. And then we ultimately made I was able to hire the folks I needed to and made that jump to true FP&A. And it wasn't until I would say been there about ten years that we decided that we needed our first BI tool. So that was when, you know, things really got interesting. We went out and got a redshift. A redshift started pointing, you know, into the data warehouse, a bunch of systems, and then rolled out Tableau. Ultimately, I'll try to keep this long background as short as possible. But ultimately that led to my, you know, deep, deep dive into data and analytics because we saw the benefit of that. The FP&A team turned into a true advanced analytics center of excellence. We actually broke the two apart and then it became a true data team. I had data engineers, data analysts, data scientists. We rolled out full data ops.
[00:22:33] Guest: Nathan Bell: I had data product managers and owners. We were deploying advanced analytics to all the company, not just finance anymore. Everybody got hungry once. They got a little bit a taste, a little bit of what we could do. It was always more and more and more, what else? If I gave you access to my department's systems, could you combine that with what you already have? We ended up ultimately 162 pipelines we were managing across, not just our accounting systems, but, you know, e-commerce, marketing, sales, Salesforce, those types of things. At that point, you know, the path. And I got way into it and it kind of became obsessive with me, a little bit of data analytics. And I went to the Harvard Business Analytics program, and that led me to want to further my career on that side of things. So I went to Gartner. Very fortunate, actually, to work at Gartner, and they were spinning up their finance practice. At the time it was still fairly small, and they had seven teams identified. They wanted to build data analytics being one of the seven. So I was one of the first analysts there. And we can get into this a little bit later. But that's when I really, truly understood what was going on, not just with the world I had been in and digital, you know, digital media, but really talking to CFOs, heads of FP&A, you know, controllers, chief accounting officers at Gartner. I got to spend close to two years on the phone with, you know, 500 plus finance execs there.
[00:23:55] Host1: Paul Barnhurst: Ever feel like your go to market teams and finance speak different languages? This misalignment is a breeding ground for failure in pairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. Meet qflow.ai, the strategic finance platform purpose built to solve the toughest part of planning and analysis B2B revenue. Qflow quickly integrates key data from your go to market stack and accounting platform, then handles all the data prep and normalization under the hood. It automatically assembles your go to market stats, make segmented scenario planning a breeze, and closes the planning loop. Create airtight alignment, improve decision latency, and ensure accountability across the teams. But thank you for that. I mean, kind of great introduction. A lot of experience there. And, you know, I'd like to ask one question for you. At the end of the day, we talk a lot about AI, but everything, whether it's data analytics, whether it's AI, whatever you're doing, it starts with your data and having good data and data governance and management. And many companies really struggle there. So if companies are listening and they want to get to that point where, hey, I want better analytics, I want AI. How should they think about data governance and management? Where should they start?
[00:25:34] Guest: Nathan Bell: You know, that's a really good question. And it's funny, when I went to Gartner, I was really excited to talk about advanced analytics and predictive prescriptive forecasting, scenario planning. And I was, you know, I can't wait for my first calls. Immediately jumped into I think it was the first week I started taking calls with CFOs and FP&A leaders in advisory role, and they wanted to talk about advanced analytics, too. But within five minutes of that call, it was quite clear they were nowhere near any sort of readiness to do anything, you know, on the exciting or fun part of it, whether it's AI or fancy analytics and the conversation. Day after day, week after week, kept going back to what is the quality of your data? Do you have a data governance? What is data governance? Do you have a data capture strategy? Do you know who's creating the data? Is there any data stewardship? Is there any clear ownership? And they would look at me with just blank eyes like, what are you talking about? I'm like, well, the reason why you can't get anything going, especially in some cases they'd already bought some technology with some promises of AI or advanced analytics. They hadn't seen anything delivered in a year or two. It was because they had no structure at all. I heard a lot of people tell me, oh, isn't that what you know, CTO? He says that they own data governance or that's something they do. And I spent a lot of time talking to finance leaders about their role in participation and how they actually need to co-lead it with their counterparts, the engineering CTO, CEO, if they have that.
[00:27:01] Guest: Nathan Bell: And I spent a lot of time talking about data literacy and, you know, in some cases it was definitely a yawner for them. It was not what they wanted to talk about. It wasn't something that they wanted to, you know, it's like, hey Nathan, I'm a CPA. I'm not going back to school for this. Stuff like data governance, master data management. It's always been engineering and developers and CTOs. I said, yeah, well, now you're the messenger. You're the one, you know, in the finance and FP&A world, you're not just controlling your own domain, which is, you know, ERP or accounting systems, which I'm sure are clean. You're now having to report on operating metrics and performance metrics. And that means combining your clean ERP data with all of their dirty data across marketing and sales and HCM systems and all of that. Right. And you're going to be, at the end of the day, accountable because you're putting the board pack together. You're putting the clothes together. And yeah, you might just be the messenger. But typically, as you probably know, CFOs still are held accountable because they're the ones again, compiling and aggregating and reporting that information, especially if you're publicly traded or regulated. And when I explained that to them, they're like, wait, so I have a role in this, I need to participate in this data governance, right? And that's when there was an aha moment. Some of my clients and some were like, you know what, I'm going to bring my CTO on the next call. I want you to talk to them.
[00:28:22] Host2: Glenn Hopper: So I you know, it's funny you you mentioned that because early like my first CFO role, the first time I'd had to go into the board meeting and represent the company, I was just as you said I was taking, you know, I, I knew everything inside and out with my financial statements. But we're going through the deck and I'm presenting the ops, just all the KPIs, you know, just as we're doing kind of the financial reporting. So I'm, I'm reporting, you know, sales as part of the financial statements. But I'm also reporting, you know, why we had miss here and over here and all that. And then I turned to ops. And I really like we built our in-house software system that kind of tracked all these, all these different KPIs. But I really didn't pay any attention to them, and I just was going to present them and move on. And I'm sitting right next to the CEO, and I reported some, I guess I don't even remember what it was. It's been a million years because I'm that old, but reported something and this board guy stopped me, you know, for the first time in my whole presentation and was like, wait a minute, walk me through this. And I sort of hemmed and hawed, and I was like, well, I'm kind of the messenger. Why don't you talk to our CEO here, who just kind of looked at me and said, I've never seen these numbers. So it was just and he was completely making me own them. And so since then it was, oh, if I'm going to be reporting these and I'm going to be, then I'm going to get all up into your business. I'm going to define these KPIs for you. I'm going to define the source of truth. And it's just, yeah, you are the messenger. But, the messenger will be shot.
[00:29:54] Guest: Nathan Bell: You had that moment that I think every finance exec that is probably listening to this right now is going to know in their head where you thought you were just the messenger. But apparently you're also the owner of the KPIs and metrics. And if it's not a favorable report, they're looking to you as well to give insight as to why. I had the same situation where we had actually thought we were doing the right thing by rolling out self-serve BI analytics, right? So we pulled out Tableau to the whole organization, and then we actually switched to looker. But I thought this was empowerment. And I'm sitting there at the quarterly offsite. I presented the board pack because I had to compile and put it together. We're looking at the KPIs and metrics as well. I go first and then after me, when all the other insects and after me was the head of sales. They said, I know what you all just saw in the board pack from Nathan, but those aren't my numbers. Here's mine. And then they went after me. The marketing person went after that HR ops. By the end of the day, we'd seen multiple versions of truth being presented. They all claimed it was coming from my team's, the I tool right where they all are getting their data, and the CEO pulled me aside at the end of that day of that offsite, said Nathan. This can never happen again. I am paralyzed. I cannot make any decisions because I've seen so many versions. Everybody is saying they don't agree with your numbers, but they're also saying it's being their numbers are being pulled from the same source of truth. Your, you know, BI tool looker, what is going on here. Right. So that's kind of had this epiphany in that moment, you know, as far as ownership and consensus and trying to get you know, I don't think single version of truth exists these days. I actually wrote about it at Gartner. We'd like to call it sufficient truth, but trying to get that multiple version of truth eliminated. I do warn anybody trying to roll out self-service analytics about these scenarios.
[00:31:46] Host2: Glenn Hopper: Yeah. So that actually, you know, that you're meeting a very specific need with VI consulting. So you're not a fractional CFO shop. You're not coming in doing people's accounting and bookkeeping. You're coming into the office of the CFO to help them. So I guess I'm trying to understand because I've, you know, as whether it was when I was a full time CFO or as a consultant. I've mostly worked in the SMB space and, and, you know, data maturity for a business that's under, say, $50 million in revenue a year is usually not great just because they don't have the resources and wherever they are in the growth. But so I'm trying to picture you're probably not. So I don't know. You tell me is an SMB coming to buy consulting or is it more sort of mid market companies and what industry like where are you finding what's a typical client?
[00:32:43] Guest: Nathan Bell: Yeah that's a good question. And you know I've been fine tuning it you know over the past year. But it does come about resources right. You have the budget that you have the time. Half the time when we talk to our clients, it's. I might have the money, but I can't pull my team off, you know, for whatever, ten, 20 hours, whatever it is, commitment wise that it might take. And it's one of those catch 22, right? As far as the team knows, they need to level up. They need automation or AI, they need data literacy and some training. But we run such a small team that even five hours a week is too much. My team's already taxed, right? So then they're caught in this loop, right? And for those types of clients, it's difficult for us. We'd like to say that we go, you know, about 50 million in revenue is probably the lowest that we'd like to help. And a lot of that's just about time and resources. But that sweet spot for us is like 50 to 250 million in revenue. And those companies are growing. The ones that are trying to 2 or 3 X in the next, you know, three, 4 or 5 years. And as you know, scaling is very difficult. And preparing the finance team to support, not just internally, your team and what it needs to do, but to support the other teams as they're scaling, is where we find our sweet spot.
[00:34:06] Guest: Nathan Bell: One of the things that I would say that I discovered working at Gartner, talking to all these finance leaders, and I talked to, you know, across multiple industries and different sizes, is very few of them have a plan for scaling. Very few of them know. It's like that in some cases too. It's data and analytics and AI. They know that that plays a part in it, but they don't quite know where that sits or fits. And you know, you hear this term that people process technology. But that's kind of where we think about it. I often will talk to CFOs, and the first thing they want to say is what technology are you going to recommend? And I was like, whoa. No, it's the last thing I want to do is throw some new platform on you that your team's going to have to learn. We're going to start, you know, with the fundamentals. One of my most popular pieces at Gartner that I wrote White Paper was The Five Steps to Unlock Advanced Analytics for Your FP&A team. And it's boring stuff. So when you go in there, it's the step one is establishing data governance, right? Step two is running a workshop for KPIs and metrics. So we'd like to attach that to business drivers. Do you even know what your business drivers are? If I asked all your execs individually, would they even agree on what these are or what the KPIs? I'm like, no, okay, well let's get those foundations set.
[00:35:23] Guest: Nathan Bell: And then the third component, once we have those. Agreed. And this is kind of what I like to do in an initial engagement is to then start figuring out what are the data ingredients we need to calculate that KPI or metric. Do you know where that's coming from? Do you know what team that's creating that data? To even have the capture structure in place? Is that field required? Is it a structured or unstructured field in your CRM or in your platform? And then we start doing that real kind of nitty gritty governance and data work, which is not fun for most people, but I like to at least get the most out of what they currently have from a technology standpoint and help them understand it's a little bit of a later, you know, literacy problem with them understanding, oh, okay, so this team owns this system that's generating this data that is then being combined with my team's data. They have no governance or rules or policy or procedures on how they're doing theirs. Or in some cases, this metric that we think is accurate is we're actually just using because we don't have access to the data we really want. We're using something else as a proxy.
[00:36:26] Guest: Nathan Bell: And that's why our forecasts are so crappy. It's because we're not actually doing what we really want to do, which is with the right data. But these are the types of things that I like to go in and go in a specific order. I do ultimately get to, again, more boring stuff creating a data dictionary, a glossary, a map, those types of stuff that I think are required and should be maintained. But again, it starts with governance and then tying things to things that happen in finance. Leaders understand KPIs and metrics like laying out that cascade, understanding who's reporting on what and who's generating the data. We do a little bit as far as the technology, like do you have a data warehouse? Do you have a data lake? Do you need a lake house? What are you trying to do? Are you trying to get to true AI? Are we just talking about RPA? Here we're talking about intelligent automation. You know, and you guys probably know everybody's a little bit different on their journey and their understanding of what that is. Typically my clients, whether it's at Gartner or via consulting, they put all of that into some kind of AI bucket. And then when you start talking to them, they kind of have some aha moments like, I'm sorry, but RPA is an AI, or why isn't it explaining that more? Yeah.
[00:37:33] Host2: Glenn Hopper: It's rule based AI right?
[00:37:37] Host1: Paul Barnhurst: Yeah. A lot of differences. And I can imagine you've had a lot of fun conversations during that time at Gartner. Kind of speaking of Gartner, I was looking at your LinkedIn profile, and you recently posted five trends for 2025 from Gartner. And one of them kind of resonated with me. It showed, I think it was like 70% of CFOs are concerned about the skill set they have for their teams to be able to support the mission. So what do you think finance needs to focus on to have that skill set? What's missing?
[00:38:04] Guest: Nathan Bell: It's actually so true. And it's been, you know, a little over a year and a half since I've been at Gartner. But I did a good job of labeling my conversations to try to bucket them. Of course, I was running analytics on my own calls and data and data literacy and concerns about their teams leveling up and understanding this stuff was always top three. It wouldn't start, the conversation wouldn't start there, but I'd start asking questions and it made sense. I'm like, so who on your team would you say is the person that is leaning the most into the technology or has a natural curiosity? We talk about citizen data scientists a lot, right? I, experts and we'd have this conversation and said here a couple of things that we saw and I've seen just from my own teams that I've led and anecdotally is you're going to typically find 1 or 2 people in your team that is that person, and you're going to lean heavily into them. They might ask you to go to a program or your school or certification, and you're going to pay for that in the hopes that they're going to bring it back into the organization and disseminate that and be a leader and teach the rest of the people in your company.
[00:39:12] Guest: Nathan Bell: That typically isn't the case. Unfortunately, what's going to happen is they're going to leave your organization because once they have all that skill, they become very rare. They become a unicorn, and somebody's going to offer them way more than you think you want to pay them. After you just spent a bunch of time and money investing in these people's education on your team, the amount of attrition that we saw with our clients was crazy the moment they invested. And then it was catch 22. I had a CFO. I won't name any names because we're in a large government entity, $40 billion budget and had gone through three years in a row. Three different die trained experts, when they finally invested, sent them to school. They learned how to use Tableau. They learned how to do this. Data aggregation and reporting three years straight in a row had lost that person every time they hired him.
[00:40:02] Host1: Paul Barnhurst: I'm sure when the private sector came and offered a $40,000 raise or whatever the number was, I'm out.
[00:40:09] Guest: Nathan Bell: Of course. Right. But time and time again, I saw this, and I had to level set with some of these CFOs as well and said, look, yes, you're going to spend time and money in their training and development, but you have to give them the raise that they're going to qualify for. And if you can't see that, you're going to lose them again and again and again. Right. But yeah, they need to develop. And it's not just to find the one person. It's you're going to need everybody to be on some level of program. In fact, one of the themes that we had was, can you help us build a data literacy or digital literacy or an AI literacy program, something that, you know, a playbook that we can run here internally, you know, with what little precious time we have on the side, like, how can I have my my folks move along that adoption path? And the data does show that certain individuals and types of roles are going to adopt earlier. FP&A always is the earliest adopter according to the surveys, and the data controllers are typically the laggards. They seem to be the most total sense.
[00:41:10] Host2: Glenn Hopper: I Know exactly what you're talking about.
[00:41:12] Guest: Nathan Bell: To this. And there's data. Gartner has all of it to show. It's kind of yeah. So you again anecdotally, you know, firsthand what I'm talking about on this.
[00:41:20] Host1: Paul Barnhurst: Yes. Yeah, definitely. You know I've always been a little more on the data side. And often people have come to me because I did report writing first and I did a little bit of bi, and I did a master of science and Information Management. So I'd had my hand, I'd been a business analyst. I was always viewed as that data guy, Not super techie. You don't need to be. It wasn't writing code, but I could speak to the IT people. I understood how a database worked. I could do some SQL, you know, those type of things. Enough to be dangerous and helpful at the same time.
[00:41:51] Guest: Nathan Bell: Those people are so critical. Like I said, they're the ones that unfortunately, I think in some ways cripple an organization because if you have a data guy, you don't think anybody else needs to learn. That person becomes a single point of failure. They leave you because they're going to get a lot more money somewhere else. And now everybody's like, does anybody know how to update or manage these dashboards that our data guy created? And now you're screwed, right? So yeah, you definitely want some redundancy. Find those people that are willing to learn. Maybe they're not as eager as that first person, but, you know, identifying some of those folks that are really dragging their feet. We kind of had internal saying at Gartner that, you know, AI is not going to replace people in finance. Finance, people Who use. I are going to replace those who don't.
[00:42:37] Host1: Paul Barnhurst: Great answer there. We're going to move into kind of our fun random question section. We might get 1 or 2 of these in, as we know we're getting near the end of our time. So I'll start. You get two options. I can use the random number generator to pick a number between 1 and 25, or you can pick the number yourself. We'll see what kind of fun personal question we get.
[00:42:58] Guest: Nathan Bell: Let's do the random, I'm feeling it.
[00:43:00] Host1: Paul Barnhurst: All right, here we go. Number five. What's the most interesting place you've traveled to?
[00:43:08] Guest: Nathan Bell: So, US is Interesting. I've been to 47 states here. There's three that I would still really like to check off. And some of those places I would say would qualify as some interesting places to be. But really where I think, again, I have such great memories of this. I did summer abroad my senior year, my undergrad, and I was very fortunate and it was a Europe and I got to go to England, London. Cambridge. I went to Austria. I was in Innsbruck. Outside Eccles, outside Innsbruck in Austria, Munich, Germany and northern Italy. But really the most interesting place and it was the time I was there that made it interesting as well. I did get to meet our Secretary of State, Madeleine Albright, when I was in Austria. That was cool. Her son in law was teaching at the University of Innsbruck at the time, so that's why they were out there. But Malta was my most interesting and favorite place. It's the small island country just south of Italy. For folks who are big gladiator fans, that's where the movie Gladiator was filmed. I was there in 2000 right when the filming was ended, so we actually got to hang out with a lot of the actors and crew that were filming that while we were in Malta. It's also where the famous Blue Lagoon is, which we got to go check out.
[00:44:24] Guest: Nathan Bell: But one of the funniest stories about why that was interesting too, is I didn't know this at the time. Is Malta is one of the most popular vacation destinations for English folks. Britain, because they speak English, not. They speak Maltese, but they also speak English there. And of course, it's in the Mediterranean. So we're sitting there, you know, we're obviously still in class. But that evening, a bunch of us went to a Scottish bar called The Scotsman Pub, and we're sitting in the pub and somebody walks in and he walks in with what looks like a trophy. Some other people, some look like some security guards or bouncer type folks. Everybody starts screaming and the girls start screaming. I didn't know who he was. And everybody's like, you need to go get a picture with this guy. And remember, this was 2000. I walk up to him and I was like, my friends say, I need a picture with you. You know, here's your shot, would you mind? And he starts chuckling and he's just shaking his head. And he just goes Americans. Anyways, I got a picture with Dave Beckham and Cup. He had just won with Manchester United at the Scotsman pub in Malta during the summer. So yeah.
[00:45:29] Host1: Paul Barnhurst: I think that's a great note to end on. I love that story. It's kind of funny sometimes. I had one where Jimmy Carter was shaking everybody's hand, and the guy next to me that was American had no idea who he was. And I'm just like Americans.
[00:45:43] Guest: Nathan Bell: Americans.
[00:45:44] Host1: Paul Barnhurst: Here's my story, but I think we'll end it there. Thank you so much for joining us, Nathan. Really enjoyed it. Glad we were. Get to chat. And I love the data focus you have because we need more of it in finance.
[00:45:56] Guest: Nathan Bell: Thanks for having me.
[00:45:58] Host2: Glenn Hopper: Yeah. Thanks, Nathan.
[00:46:00] Host1: Paul Barnhurst: Thanks for listening to the Future Finance show. And thanks to our sponsor, qflow.ai. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice and may your robot overlords be with you.